10 posts tagged with "MarketBetter"

🟢 Series Difficulty: BASIC (Part 1 of 10) — No AI experience needed. Start here.

There's a quiet revolution happening in sales development, and most SDRs are about to get left behind.

While everyone's talking about AI replacing salespeople, the real story is different: the SDRs who learn to work with AI tools are outperforming their peers by 5-10x. Not because they're better sellers. Because they've eliminated the busywork that eats 70% of their day.

This is the first post in our 10-part series on how SDRs can use Claude Code together with MarketBetter to become radically more effective. No coding background needed. No engineering degree required. Just practical workflows that any sales professional can start using today.

What Is Claude Code (and Why Should You Care)?​

Let's start simple. Claude Code is an AI assistant built by Anthropic that lives in your terminal — think of it like having a super-smart research analyst sitting next to you, ready to do whatever you ask.

But here's what makes it different from ChatGPT or other AI chatbots: Claude Code can actually do things. It doesn't just generate text. It can:

• Read and analyze files — drop in a CSV of 500 leads and ask it to prioritize them
• Search and research — pull together company intel from multiple sources in seconds
• Write and edit — craft personalized emails, call scripts, and LinkedIn messages
• Process data — clean up your CRM exports, find duplicates, standardize job titles
• Build simple tools — create lead scoring models, competitive tracking sheets, and more

Think of it this way: if your current AI tool is a calculator, Claude Code is a full spreadsheet. Same category, completely different capability.

"But I'm Not a Developer..."​

Good. You don't need to be. The way you interact with Claude Code is by typing plain English. You tell it what you want, and it figures out how to do it.

Here's a real example:

"I have a meeting with the VP of Sales at Acme Corp tomorrow. Pull together everything you can find about them — recent news, their tech stack, any recent job postings, and what their LinkedIn presence looks like. Give me a one-page brief I can review in 5 minutes."

That's it. That's the "prompt." No code. No special syntax. Just tell it what you need like you'd tell a colleague.

The Current SDR Reality (It's Not Pretty)​

Let's be honest about what most SDRs' days actually look like:

Activity	Time Spent	Revenue Impact
Researching prospects	2-3 hours	Indirect
Updating CRM	1-2 hours	Zero
Writing/personalizing emails	1-2 hours	Moderate
Actual selling (calls, meetings)	1-2 hours	High
Admin tasks	1 hour	Zero

The math is brutal. Out of an 8-hour day, the average SDR spends less than 2 hours on activities that directly generate revenue. The rest? Research, data entry, email drafting, and the soul-crushing ritual of tabbing between 12 different browser tabs trying to figure out if a prospect is worth calling.

This isn't a "work harder" problem. It's a leverage problem. And AI is the lever.

Enter Claude Code + MarketBetter: The 10x SDR Stack​

Here's our thesis: when you combine Claude Code's analytical power with MarketBetter's signal-driven platform, you create a workflow that turns an average SDR into a top performer.

Not by making them faster at bad activities. By fundamentally changing which activities they spend time on.

How the Stack Works Together​

MarketBetter is your signal engine. It tells you:

• Which companies are visiting your website right now
• Who the actual people are behind those visits (person-level identification)
• What pages they looked at and how many times they came back
• When a cold lead suddenly re-engages
• Which accounts are showing buying intent

Claude Code is your research and execution engine. It:

• Takes those signals and instantly builds detailed prospect briefs
• Crafts hyper-personalized outreach based on real research
• Cleans and enriches your contact data
• Analyzes patterns in your pipeline
• Builds custom workflows for your specific sales process

Together, they create a loop:

1. MarketBetter surfaces the signal → "Company X visited your pricing page 3 times this week"
2. Claude Code does the research → "Here's everything about Company X: they're a 200-person SaaS company, just raised Series B, hiring 5 SDRs, their VP of Sales just posted about outbound challenges on LinkedIn..."
3. You make the call → Armed with context that would have taken 30 minutes to gather manually, in 30 seconds
4. MarketBetter delivers the sequence → AI-written follow-up sequences triggered by behavior

That's the loop. Signal → Research → Action → Follow-up. And it happens in minutes, not hours.

What This Series Will Cover​

Over the next nine posts, we're going deep into every part of this workflow. The series is structured as a progression — Basic → Medium → Advanced — so you build skills step by step. Each post builds on what you learned in the previous ones, and by the end, you'll have a complete AI-powered SDR workflow.

Here's what's coming:

🟢 BASIC (Posts 1-3) — Getting Started​

These posts assume zero AI experience. If you've never used Claude Code, start here.

Part 2: Prospect Research in 30 Seconds — Your first real use case. Learn how to use Claude Code to build complete account dossiers instantly. Pair with MarketBetter's visitor identification to know exactly who to research and when.

Part 3: Writing Hyper-Personalized Cold Emails at Scale — Build on your research skills to craft emails that genuinely feel personal. Then deploy them through MarketBetter's AI sequences.

🟡 MEDIUM (Posts 4-6) — Building Your System​

Now that you're comfortable with basic prompts, these posts show you how to build repeatable workflows.

Part 4: LinkedIn-to-Pipeline — Automate your Sales Navigator workflow. Combines the research skills from Part 2 with the email writing from Part 3, plus MarketBetter's Chrome Extension for importing leads.

Part 5: Competitive Intelligence on Autopilot — Monitor what your competitors' customers are saying. Turn insights into targeted outreach using the techniques from earlier posts.

Part 6: Building a Lead Scoring Model — Create simple but effective scoring logic without a data team. Use MarketBetter's daily playbook to act on the scores.

🔴 ADVANCED (Posts 7-9) — Mastering AI-Powered Sales​

These posts tackle more complex workflows that combine multiple skills. Best tackled after you're comfortable with Parts 1-6.

Part 7: CRM Cleanup in Minutes — Process large datasets, fix dirty data, and build maintenance systems. Clean data powers everything else in this series.

Part 8: Meeting Prep That Doesn't Suck — Build an automated meeting prep system that combines Claude Code research with MarketBetter behavioral data. Multi-step workflows for every meeting on your calendar.

Part 9: Never Let a Lead Go Cold — AI-powered follow-up sequences that combine signal detection, research, and personalized re-engagement. The most sophisticated workflow in the series.

🏆 CAPSTONE (Post 10) — The Full Playbook​

Part 10: The Complete AI SDR Playbook — Everything from Posts 1-9, assembled into a complete daily routine. Your minute-by-minute schedule as an AI-powered SDR.

The 5 Principles of the AI-Powered SDR​

Before we dive into tactics, let's establish the mindset. These five principles guide everything in this series:

1. Signals Over Spray-and-Pray​

Traditional outbound is a numbers game. AI-powered outbound is an intelligence game. Instead of emailing 200 people and hoping 5 respond, you identify the 20 who are most likely to buy and reach out with perfect context. The result? Higher response rates with less effort.

For a deep dive on this approach, check out our guide to signal-based selling.

2. Research Speed = Revenue Speed​

The faster you can go from "who is this prospect?" to "here's exactly what to say to them," the more conversations you have. Claude Code compresses research from 20 minutes to 20 seconds. Over a day, that's hours reclaimed for actual selling.

3. Personalization Is a Competitive Moat​

Generic outreach is dead. When every SDR is using the same templates, the reps who win are the ones who make every touchpoint feel custom. AI lets you achieve true personalization at volume — not "Hi {first_name}, I see you work at {company}" personalization, but "I noticed you just posted about scaling your outbound team, and your company is hiring 3 new SDRs — here's how others in that situation have approached it" personalization.

Learn more in our post on how to write cold emails that actually get replies.

4. Clean Data Is Non-Negotiable​

AI tools are only as good as the data you feed them. Garbage in, garbage out. That's why Part 7 of this series focuses entirely on using Claude Code to clean your CRM data. It's not sexy, but it's the foundation everything else is built on.

5. The Human Makes the Decision​

AI doesn't close deals. People do. The role of AI in this stack is to give you better information faster so you can make better decisions about who to call, what to say, and when to follow up. You're still the one building relationships, reading rooms, and closing business. AI just makes sure you're spending your time on the right prospects.

A Day in the Life: AI-Powered SDR vs. Traditional SDR​

Let's make this concrete. Here's how the same morning looks for two SDRs:

Traditional SDR: Sarah's Morning​

• 8:00 AM — Opens CRM, scrolls through her list of 200 accounts. No idea which ones to prioritize.
• 8:15 AM — Picks 10 accounts alphabetically (she left off at "M" yesterday). Opens LinkedIn to research the first one.
• 8:30 AM — Spends 15 minutes on the first account. Finds the VP of Sales on LinkedIn, reads their last 3 posts, checks the company news page, looks up their tech stack on BuiltWith.
• 8:45 AM — Writes a personalized email. Revises it twice. Sends it.
• 8:50 AM — Starts researching the second account...
• 10:00 AM — Has sent 4 personalized emails. Feeling productive but exhausted.

AI-Powered SDR: Marcus's Morning​

• 8:00 AM — Opens MarketBetter's daily playbook. Sees that 12 accounts visited the website overnight, 3 of them hit the pricing page, and 1 is a return visitor from a cold lead that went dark 2 months ago.
• 8:05 AM — Asks Claude Code to research all 12 accounts. Gets back complete dossiers — company overview, key contacts, recent news, tech stack, LinkedIn activity — for all 12 in under 2 minutes.
• 8:10 AM — Reviews the briefs for the 3 pricing page visitors. Asks Claude Code to draft personalized emails for each based on the research.
• 8:15 AM — Reviews and tweaks the emails. Sends all 3 through MarketBetter with AI-powered follow-up sequences attached.
• 8:20 AM — Calls the return visitor. Already knows their website visit history (MarketBetter), their recent LinkedIn activity (Claude Code research), and that they just posted a job opening for a demand gen role (Claude Code found it). Opens with: "Hey, I noticed you're building out your demand gen team — we've been helping companies in your space solve exactly that challenge..."
• 8:30 AM — Books a meeting. Moves to the next batch.
• 10:00 AM — Has sent 15 personalized emails, made 8 calls, and booked 2 meetings.

Same two hours. Wildly different outcomes.

Getting Started: What You Need​

Ready to try this yourself? Here's what you'll need:

1. Claude Code — Available from Anthropic. You can use it through the terminal or through tools that integrate it. If you're not sure where to start, your team's RevOps or sales ops lead can set it up for you in minutes.

2. MarketBetter — Sign up to start identifying anonymous website visitors and running AI-powered sequences. Book a demo to see how it works with your existing workflow.

3. Your existing tools — Claude Code works with the data you already have. CRM exports, lead lists, Sales Navigator searches — it all feeds into the workflow.

That's it. No complex integrations. No months-long implementation. You can start using Claude Code for prospect research today and layer in MarketBetter's signals as you go.

What About Other AI Tools?​

Fair question. We've written about the differences between Claude Code, ChatGPT, and Codex for sales teams. The short version: Claude Code's ability to handle large amounts of context (up to 200K tokens — think of it as being able to read an entire book at once) and its agentic capabilities make it particularly powerful for sales research and analysis.

That said, the principles in this series apply to any capable AI tool. We focus on Claude Code because it currently offers the best combination of research depth, context handling, and practical utility for SDRs.

Try This Today​

Here's your homework before the next post:

Open Claude Code and give it this prompt:

"I'm an SDR at [your company]. We sell [your product] to [your target market]. My biggest time wasters are [list 2-3 things]. Suggest 5 specific ways I could use AI to reclaim that time and spend more of my day on actual selling."

Take the response and highlight the one suggestion that would save you the most time. That's your starting point.

Then read Part 2: Prospect Research in 30 Seconds to learn how to turn Claude Code into your personal research analyst.

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This is Part 1 (🟢 Basic) of our 10-part series on using Claude Code + MarketBetter to become a more effective SDR. Start with Part 2: Prospect Research →

Want to see how MarketBetter's signal-driven platform fits into your sales workflow? Book a demo and we'll show you exactly how it works with your existing tools.

🏆 Series Difficulty: CAPSTONE (Part 10 of 10) — Everything from Parts 1-9, assembled into your complete daily workflow.

You've made it. Parts 1 through 9 of this series gave you the individual tools and techniques. Now it's time to assemble them into a complete daily system.

This is the capstone of our Claude Code + MarketBetter series — a minute-by-minute playbook for the AI-powered SDR. Not theory. Not "you could do this someday." This is what your actual day looks like when you put everything together.

Here's how every skill from the series maps to your daily routine:

Time Block	Series Skill	Where You Learned It
Morning intelligence	Prospect research	🟢 Part 2
Outreach drafting	Personalized emails	🟢 Part 3
LinkedIn power hour	Sales Nav workflow	🟡 Part 4
Competitive checks	Competitor monitoring	🟡 Part 5
Lead prioritization	Lead scoring	🟡 Part 6
Data maintenance	CRM cleanup	🔴 Part 7
Pre-call prep	Meeting briefs	🔴 Part 8
Re-engagement	Follow-up sequences	🔴 Part 9

If you've been following the series from the beginning — starting with the Basic skills, building through the Medium workflows, and mastering the Advanced techniques — this playbook will feel natural. You've already practiced each piece. Now we're just putting them in the right order.

If you're jumping straight to this post, it'll still work — but you'll get more value from each section if you've read the relevant earlier post. I'll link to them throughout so you can go deeper on any technique.

The AI-Powered SDR's Daily Schedule​

7:45 AM — Pre-Work Intelligence Gathering (15 minutes)​

Before you even sit down at your desk, spend 15 minutes on intelligence gathering. This is your competitive advantage — most SDRs don't start thinking until 9 AM.

Open MarketBetter's dashboard and check:

• Overnight website visitors — who came to your site while you slept?
• Return visitors — any cold leads that came back to life? (This is your highest-priority signal. See Part 9.)
• High-intent page visits — anyone on pricing, case studies, or comparison pages?
• Multi-person visits — any companies with multiple visitors? (Buying committee forming)

Quick Claude Code prompt:

"Here are today's MarketBetter signals — 14 companies visited our site overnight. 3 hit the pricing page, 1 is a return visitor from 3 months ago, and 2 companies had multiple visitors.

Prioritize these for me based on buying intent. Research the top 5 and give me a 3-sentence brief for each: what they do, what's notable, and the best outreach angle."

By 8:00 AM, you have a prioritized hit list for the day. Most SDRs are still making coffee.

8:00 AM — The Morning Sprint (45 minutes)​

This is your most productive window. No meetings, no Slack distractions, pure execution.

8:00–8:15: Batch Research

Take your top 10-15 accounts from the intelligence gathering and batch-research them:

"Research these 10 accounts in detail. For each, give me:

• Company overview (one paragraph)
• Key decision maker with LinkedIn profile
• One personalization hook
• Recommended first-touch channel (email, LinkedIn, or phone)

[list your 10 accounts]"

8:15–8:35: Draft Outreach

Feed the research back to Claude Code for outreach generation:

"Write personalized cold emails for the top 5 accounts. Use the research you just provided. Rules: under 100 words, personal opening, one CTA, conversational tone. Also write LinkedIn connection request notes (under 300 characters) for the other 5."

Review the drafts. Fix anything that doesn't sound like you. This should take 5-10 minutes for 10 personalized touchpoints.

8:35–8:45: Load and Launch

• Load the email drafts into MarketBetter sequences
• Set up multi-touch follow-up cadences for each prospect
• Send LinkedIn connection requests
• Queue any phone calls for the Call Block (coming up next)

Morning Sprint Results: 10 personalized outreach touches, researched and deployed. In 45 minutes. A traditional SDR would need 3-4 hours for this.

8:45 AM — Call Block 1 (60 minutes)​

Now it's time to pick up the phone. This is where humans shine and AI can't replace you.

Pre-call prep (2 minutes per call):

Before each call, pull up your Claude Code research brief. But also check MarketBetter for any last-minute signals:

"Quick prep for my call with [Name] at [Company]. Give me:

1. Their most recent LinkedIn post (topic)
2. One personalized opening line
3. The key pain point to explore
4. A fallback question if the conversation stalls"

During the call:

Be human. Listen. Ask questions. Use the research as context, not a script. The AI prepared you; now it's your turn to build a relationship.

Post-call logging (1 minute per call):

After each call, quickly dictate or type your notes. At the end of the call block, batch-process them:

"Here are my raw notes from 8 calls this morning:

Call 1: Sarah at Acme — interested, wants to loop in CRO, follow up Thursday Call 2: James at Beta — not a fit, too small Call 3: David at Gamma — no answer, left voicemail [etc.]

For each call, write:

1. A structured CRM update (2-3 sentences)
2. For interested prospects: a follow-up email to send today
3. For no-answers: a follow-up email referencing the voicemail"

Your call block produced conversations. Claude Code handles the admin that follows.

10:00 AM — LinkedIn Power Hour (30 minutes)​

Dedicated LinkedIn time, executed efficiently:

10:00–10:10: Engage with Prospects' Content

Check which prospects posted on LinkedIn today. Use Claude Code to draft thoughtful comments:

"Here are 5 LinkedIn posts from my prospects today. Draft a genuine, non-salesy comment for each that adds value to the conversation. Keep each under 2 sentences."

Leave the comments. This warms up prospects before your outreach arrives.

10:10–10:20: Sales Nav Search

Run your saved Sales Navigator searches for new leads. Feed new results into Claude Code for quick analysis:

"5 new leads from my Sales Nav search. Quick assessment: which 2-3 are worth pursuing? Why?"

Import the best ones into MarketBetter via the Chrome Extension. (Full workflow in Part 4.)

10:20–10:30: Connection Request Follow-Ups

Check who accepted your connection requests. Draft personalized DMs:

"These 3 people accepted my LinkedIn connection requests this week:

1. [Name, Title, Company]
2. [Name, Title, Company]
3. [Name, Title, Company]

Write a follow-up DM for each that:

• Thanks them for connecting (briefly)
• Offers a specific piece of value (insight, resource, introduction)
• Ends with a soft conversation opener, NOT a meeting ask"

10:30 AM — Meeting Prep (15 minutes)​

Check your afternoon calendar. If you have meetings, prep now while your brain is fresh:

"I have 2 meetings this afternoon:

1. [Name], [Title] at [Company] — 1:00 PM, discovery call
2. [Name], [Title] at [Company] — 3:00 PM, second meeting (follow-up from last week)

Generate one-page meeting briefs for each. [Full meeting prep prompt from Part 8]"

Layer in MarketBetter website visit data and you're set. (Complete meeting prep system in Part 8.)

11:00 AM — Email and Sequence Management (20 minutes)​

Review responses:

• Check for replies to your outreach from the past few days
• Positive replies → Schedule the meeting immediately
• Objections → Feed the objection to Claude Code for a thoughtful response
• "Not interested" → Mark and move on (or add to long-term nurture)

Check sequence performance:

• In MarketBetter, review your active sequences' open rates, click rates, and reply rates
• Identify sequences that are underperforming
• Ask Claude Code to analyze:

"My email sequence for [campaign] has a 45% open rate but only a 2% reply rate. The emails are about [topic] targeting [persona]. The subject lines are getting opens but the body isn't converting. Review my emails and suggest 3 specific changes to improve reply rate."

Manage follow-ups:

• Check which prospects need manual follow-up today
• Use Claude Code to draft personalized follow-ups based on the last interaction

11:30 AM — Competitive Intel Check (10 minutes, twice per week)​

Twice a week (say, Monday and Thursday), do a quick competitive scan:

"Quick competitive update: what's new with [Competitor A], [Competitor B], and [Competitor C] this week? Check for product announcements, G2 reviews, leadership changes, funding, or social media discussions."

Update your competitive notes. Use any new intel to refine your outreach messaging. (Full competitive intel system in Part 5.)

12:00 PM — Lunch Break​

Step away. Seriously. The AI-powered SDR is more efficient, not more burned out. Eat food. Touch grass. Come back refreshed.

1:00 PM — Afternoon Meetings​

Execute your meetings with the briefs you prepped this morning. You're prepared. You're confident. You know things about this prospect that will surprise them.

Between meetings:

• Quick post-meeting note capture
• Claude Code processes notes into structured CRM updates and follow-up drafts

2:30 PM — Call Block 2 (45 minutes)​

Second phone session of the day. Different prospects, same prep process.

Focus this call block on:

• Warm follow-ups — Prospects who engaged with your morning emails
• Return visitors — Cold leads that MarketBetter flagged as re-engaging
• Time zone coverage — West Coast prospects (if you're East Coast) or international leads

3:15 PM — Cold Lead Reactivation (20 minutes, twice per week)​

Twice a week, work your cold pipeline:

"Review these 10 cold leads. Research what's changed since they went cold. Give me reactivation angles for the top 5 and draft reactivation emails."

Load the emails into MarketBetter reactivation sequences. (Complete reactivation system in Part 9.)

3:45 PM — Admin and Data Hygiene (15 minutes)​

The unsexy but essential stuff:

• Update CRM with today's activities (use Claude Code to process your raw notes)
• Quick data quality check on new contacts added today
• Verify email addresses before adding to sequences

Once a week, do a deeper cleanup session. (Full CRM cleanup workflow in Part 7.)

4:00 PM — Tomorrow's Prep (15 minutes)​

End your day by setting up tomorrow:

"Based on what I learned today, here are the prospects I should prioritize tomorrow:

1. [Prospect who replied positively — need to schedule meeting]
2. [Prospect from MarketBetter who showed high intent but I didn't get to today]
3. [Follow-up from today's meeting]

Research each and give me a quick brief so I can hit the ground running at 8 AM."

Also queue any emails for early-morning delivery through MarketBetter. Your outreach is working before you wake up.

4:15 PM — End of Day Reporting (15 minutes)​

Track your numbers. Use Claude Code to make it painless:

"Here are today's raw activity numbers:

• Emails sent: 35
• Calls made: 22
• LinkedIn touches: 15
• Meetings booked: 3
• Meetings held: 2
• Replies received: 7
• Positive replies: 4

Calculate my:

• Email reply rate
• Call-to-meeting conversion rate
• Total pipeline touches
• Comparison to last week's averages

Any patterns you notice? What should I do differently tomorrow?"

This daily review takes 5 minutes but keeps you on track and continuously improving.

The Weekly Rhythm​

Beyond the daily routine, here's your weekly structure:

Monday:

• Weekly planning — set goals for meetings booked, emails sent, new accounts researched
• Competitive intel update
• Sales Nav search refresh

Tuesday-Thursday:

• Full daily routine as outlined above
• Focus on execution and pipeline movement

Friday:

• CRM cleanup session (30 minutes) — using Part 7 workflows
• Weekly performance analysis with Claude Code
• Cold lead reactivation batch
• Plan next week's priority accounts
• Update your lead scoring model with this week's conversion data (Part 6)

The Numbers: AI-Powered SDR vs. Traditional SDR​

Here's how the same day looks, quantified:

Metric	Traditional SDR	AI-Powered SDR
Accounts researched	10-15	40-50
Personalized emails sent	15-20	50-80
Calls with research context	5-8	15-22
Meetings booked (avg/day)	1-2	3-5
Time on research	3-4 hours	30-45 minutes
Time on admin	1-2 hours	15-30 minutes
Time actually selling	2-3 hours	5-6 hours

The AI-powered SDR doesn't work longer hours. They work better hours. The AI eliminates the time sinks so you can spend your day on what actually moves the needle: conversations with prospects.

Your AI SDR Toolkit Summary​

Here's everything you need, in one place:

Claude Code — Your research and writing engine

• 🟢 Prospect research (Part 2)
• 🟢 Email personalization (Part 3)
• 🟡 LinkedIn outreach (Part 4)
• 🟡 Competitive intelligence (Part 5)
• 🟡 Lead scoring (Part 6)
• 🔴 CRM cleanup (Part 7)
• 🔴 Meeting prep (Part 8)
• 🔴 Follow-up sequences (Part 9)

MarketBetter — Your signal and execution engine

• Website visitor identification (who's on your site right now?)
• Person-level identification (not just companies — actual people)
• Return visitor alerts (cold leads coming back to life)
• AI-powered email sequences (delivery, timing, follow-ups)
• Chrome Extension (LinkedIn-to-pipeline imports)
• Daily playbook (your prioritized hit list every morning)
• Engagement tracking (who's opening, clicking, returning?)

Your Brain — The irreplaceable element

• Building relationships
• Reading the room on calls
• Making judgment calls on timing and approach
• Asking the right questions
• Closing

AI handles the preparation. You handle the performance.

Common Mistakes When Adopting This Playbook​

1. Trying to Do Everything on Day One​

Don't try to implement all 10 parts simultaneously. Follow the progression:

• Week 1 — Start with 🟢 Basic skills: Research (Part 2) and email writing (Part 3). Get comfortable with simple prompts.
• Week 2 — Move to 🟡 Medium workflows: LinkedIn pipeline (Part 4), competitive intel (Part 5), lead scoring (Part 6). Chain basic skills into multi-step processes.
• Week 3 — Tackle 🔴 Advanced systems: CRM cleanup (Part 7), meeting prep (Part 8), follow-up sequences (Part 9). Build automated routines.
• Week 4 — Run the 🏆 Full Playbook: This post. The complete daily routine.

The series was designed this way for a reason. Each tier builds on the skills from the previous one.

2. Over-Automating​

AI should augment your work, not replace your judgment. Always review outreach before sending. Always add your own voice. Always verify key facts. The goal is to be more efficient, not to become a robot.

3. Ignoring the Data​

The playbook improves over time — but only if you track results and iterate. Your daily reporting isn't optional. It's how you learn what's working and what isn't.

4. Neglecting the Human Element​

AI can research, write, and analyze. It can't build trust, read emotions, or navigate complex organizational dynamics. Never let AI efficiency replace human empathy. The best SDRs are the ones who use AI to free up time for more human connection, not less.

5. Skipping CRM Hygiene​

It's tempting to skip the "boring" stuff like data cleanup. Don't. Everything in this playbook depends on clean data. Garbage in, garbage out. Fifteen minutes a day keeps your data clean and your entire system functioning.

The 30-Day Implementation Plan​

This plan follows the same Basic → Medium → Advanced progression as the series itself:

Week 1: 🟢 Foundation (Basic Skills)

• Day 1-2: Set up Claude Code. Practice with basic research prompts from Part 2.
• Day 3-4: Start writing personalized emails using the techniques from Part 3. Compare results to your templates.
• Day 5: Do a CRM cleanup sprint using Part 7 — yes, this is an Advanced skill, but clean data is foundational.

Week 2: 🟡 Workflows (Medium Skills)

• Day 6-8: Implement the LinkedIn-to-Pipeline workflow from Part 4. This combines research + email writing into a multi-step process.
• Day 9-10: Set up competitive intelligence monitoring from Part 5. Run your first competitor analysis.

Week 3: 🟡→🔴 Systems (Medium to Advanced)

• Day 11-12: Build your lead scoring model from Part 6. Start prioritizing your daily list with scores.
• Day 13-14: Implement the meeting prep system from Part 8. Prep for every meeting with one-page briefs.
• Day 15: Run your first cold lead reactivation batch from Part 9.

Week 4: 🏆 Full System (Capstone)

• Day 16-20: Run the complete daily routine from this playbook. Every technique, every time block. Track every metric.
• End of week: Review results. What's working? What needs adjustment? Iterate.

Try This Today​

Here's your final action item for the series:

Tomorrow morning, run the complete Morning Sprint (7:45-8:45 AM):

1. 7:45 AM — Check MarketBetter for overnight signals
2. 8:00 AM — Batch-research top 10 accounts with Claude Code
3. 8:15 AM — Draft personalized emails for top 5
4. 8:35 AM — Load into MarketBetter sequences and send LinkedIn requests
5. 8:45 AM — Start your call block with full research context

One morning. One sprint. Compare your output to a typical morning. If you touch more accounts with better personalization in less time — and you will — you'll never go back.

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This is Part 10 (🏆 Capstone), the final post in our 10-part series on Claude Code + MarketBetter for SDRs. If you haven't read the earlier posts, start with Part 1: The AI-Powered SDR (🟢 Basic) →

Ready to build your AI-powered SDR workflow? Book a MarketBetter demo and see how signal-driven outreach, visitor identification, and AI sequences fit into your daily routine.

🟢 Series Difficulty: BASIC (Part 2 of 10) — No AI experience needed. This is your first hands-on use case.

Every SDR knows the drill. You get a name and a company. Maybe a job title if you're lucky. And then the clock starts: LinkedIn profile, company website, recent news, Crunchbase, BuiltWith, G2 reviews, LinkedIn posts... fifteen tabs later, you've spent 20 minutes and you're still not sure if this person is worth calling.

Now multiply that by 50 accounts a day.

This is the research bottleneck, and it's the single biggest destroyer of SDR productivity. Not because the research isn't valuable — it absolutely is. Personalized outreach based on real intel dramatically outperforms generic messaging. The problem is that the time investment doesn't scale.

Until now.

In this post — Part 2 of our 10-part Claude Code + MarketBetter series — we'll show you exactly how to use Claude Code to build complete account dossiers in 30 seconds or less. And how to pair that with MarketBetter's visitor identification signals so you're never wasting research time on the wrong accounts.

If you haven't read Part 1 yet, start there — it explains what Claude Code is, why SDRs should care, and the overall thesis behind this series. But if you're ready to get your hands dirty with your first real AI workflow, this is where it starts.

What You'll Need​

Before we dive in, make sure you have:

• Claude Code installed and ready to use (your team's sales ops or RevOps lead can help with setup — it takes about 5 minutes)
• MarketBetter account with visitor identification enabled (book a demo if you don't have one yet)
• A list of accounts you want to research (even 3-5 will do for your first try)

That's it. No coding skills. No special training. If you can type a sentence, you can use Claude Code.

The Old Way vs. The New Way​

The Old Way: Manual Research (15-25 Minutes Per Account)​

Here's the typical SDR research workflow:

1. LinkedIn Profile (3-5 min) — Find the contact, read their bio, check recent posts, look at career history
2. Company Website (3-5 min) — About page, product pages, recent blog posts, press releases
3. News & PR (2-3 min) — Google the company name, check for recent funding, acquisitions, partnerships
4. Tech Stack (2-3 min) — BuiltWith or Wappalyzer to see what tools they use
5. Hiring Signals (2-3 min) — Check their careers page or LinkedIn jobs for relevant openings
6. Social Presence (2-3 min) — Twitter/X activity, any podcast appearances, speaking engagements
7. Compile Notes (2-3 min) — Write it all up in your CRM or a doc

Total: 15-25 minutes for a single account.

At 50 accounts per day (a typical SDR target), that's 12-20 hours of research. More hours than exist in a workday. So what actually happens? SDRs skip the research and send generic outreach. Response rates drop. Pipeline suffers. It's a vicious cycle.

The New Way: Claude Code + MarketBetter (30 Seconds Per Account)​

Here's the same workflow, reimagined:

1. MarketBetter alerts you that Acme Corp visited your pricing page twice this morning
2. You paste one prompt into Claude Code:

"Research Acme Corp (acmecorp.com). I need: company overview, recent news (last 90 days), their tech stack, current job openings (especially in sales/marketing), key decision makers with LinkedIn profiles, and any personalization hooks I can use for cold outreach. Format it as a one-page brief."

3. Claude Code delivers a complete dossier in 20-30 seconds
4. You scan the brief, pick your angle, and reach out — with the same quality of personalization that used to take 20 minutes

That's not hypothetical. That's the actual workflow. Let's break down exactly how to do it.

Step-by-Step: Building Your First Account Dossier​

Step 1: Start With a Signal (Not a Cold List)​

The biggest mistake SDRs make with AI research tools is researching the wrong accounts. If you research 50 accounts but only 3 of them were actually in-market, you wasted time on 47 accounts.

This is where MarketBetter comes in. Instead of guessing who to research, you start with confirmed intent signals:

• Website visitors — Companies visiting your site, especially pricing or product pages
• Return visitors — Someone who came back after going dark (a huge signal — see Part 9: Never Let a Lead Go Cold)
• Person-level identification — Not just "someone from Acme Corp" but "Sarah Chen, VP of Sales at Acme Corp" visited your site

When you know who's looking at your site right now, your research has immediate, actionable value. You're not building a dossier for "someday." You're building a dossier for a call you're about to make.

For more on how intent signals work, read our guide to intent data.

Step 2: Craft Your Research Prompt​

The quality of your dossier depends on the quality of your prompt. Here's a template that works consistently:

The Basic Dossier Prompt:

"Research [Company Name] ([domain]). Give me:

1. Company overview — what they do, size, funding stage, HQ location
2. Recent news from the last 90 days — funding, product launches, partnerships, executive changes
3. Their tech stack — what tools/platforms they use, especially [your category]
4. Current job openings — focus on [relevant departments]
5. Key decision makers — [target titles like VP Sales, CRO, Head of Marketing] with LinkedIn profile URLs
6. 3 personalization hooks I can use for cold outreach

Format this as a scannable one-page brief. Keep it concise."

The Deep-Dive Prompt (for high-value accounts):

"Deep research on [Company Name]. I sell [your product/solution] to [your ICP].

I need:

1. Full company profile — founding year, employee count, revenue range, funding history, key investors
2. Their current go-to-market approach — how do they sell? Do they have an SDR team? What does their outbound look like?
3. Pain points they're likely facing based on their size, growth stage, and recent activity
4. Competitive landscape — who else are they likely evaluating?
5. Recent content from their executives — LinkedIn posts, blog articles, podcast appearances, conference talks
6. Tech stack analysis — current tools, potential gaps where [your solution] fits
7. Champion mapping — who would be the buyer, influencer, and end user for [your product]?
8. 5 personalized outreach angles ranked by likely effectiveness

Be specific. Use real data. Flag anything you're uncertain about."

Step 3: Review and Refine​

Claude Code will return a structured brief. Here's what a typical output looks like:

---

ACCOUNT DOSSIER: Acme Corp

Company Overview

• B2B SaaS, marketing automation platform
• ~350 employees (LinkedIn), HQ in Austin, TX
• Series C ($45M, led by Sequoia, June 2025)
• Growing ~40% YoY based on job posting volume

Recent News (Last 90 Days)

• Launched new AI features for email personalization (Jan 2026)
• Named in G2's "Best Marketing Automation" for mid-market (Feb 2026)
• VP of Engineering departed (LinkedIn activity suggests Dec 2025)

Tech Stack

• Salesforce CRM, HubSpot Marketing, Outreach for sequences
• No visitor identification tool detected
• Using Clearbit for enrichment

Job Openings (Relevant)

• 3 SDR roles (posted last 2 weeks) — scaling outbound
• 1 Demand Gen Manager — suggests inbound isn't enough
• 1 RevOps Analyst — building out operations

Key Decision Makers

• James Wilson, CRO (LinkedIn: linkedin.com/in/jwilson)
• Maria Garcia, VP of Sales (LinkedIn: linkedin.com/in/mgarcia)
• David Park, Head of Growth (LinkedIn: linkedin.com/in/dpark)

Personalization Hooks

1. They're hiring 3 SDRs — they're clearly investing in outbound. Your solution helps SDR teams perform at scale.
2. The VP of Engineering departure may signal internal shifts. Tread carefully but it's a potential change catalyst.
3. Their recent AI email features suggest they value automation — they're already bought into the AI thesis.

---

Review this in 60 seconds. Highlight the hooks you want to use. Move to outreach.

Step 4: Connect the Signals​

Here's where the magic happens. You're not just looking at Claude Code's research in isolation — you're layering it with MarketBetter's behavioral data.

MarketBetter tells you: Maria Garcia from Acme Corp visited your pricing page twice yesterday and your case studies page this morning.

Claude Code tells you: Acme Corp is hiring 3 SDRs, just raised Series C, and their CRO recently posted about scaling outbound.

Your outreach writes itself: "Maria, I see Acme is building out the SDR team — congrats on the growth. When companies hit your stage, the biggest question is usually 'how do we maintain personalization at scale?' That's exactly what we help with..."

That's not a cold email. That's a warm, relevant, perfectly-timed message. And it took you 2 minutes total.

Batch Research: The Power Move​

Once you're comfortable with individual dossiers, level up to batch research. This is where Claude Code really shines.

The Batch Research Workflow​

1. Export your MarketBetter daily signal list (the companies showing intent today)
2. Feed Claude Code the entire list:

"I have a list of 15 companies that visited our website today. Research each one and give me a brief for each with: company size, what they do, one key recent development, and the best personalization angle. Rank them by likely fit for [your ICP]. Here's the list:

1. Acme Corp (acmecorp.com)
2. Beta Industries (betaindustries.io)
3. Gamma Solutions (gammasolutions.com) ..."

3. Claude Code returns 15 mini-briefs, ranked by fit
4. You focus your morning on the top 5

Instead of spending your entire morning researching, you spend 5 minutes reviewing Claude Code's output and then the rest of your morning selling.

Advanced Prompt Patterns for SDRs​

Here are some specialized prompts for common research scenarios:

The "Pre-Meeting" Deep Dive​

"I have a meeting with [Name], [Title] at [Company] in 2 hours. Research them like my career depends on it. I need: their career history, recent LinkedIn activity, anything they've published or said publicly, mutual connections, their company's recent news, and 3 talking points that will make me sound like I've known their business for years."

(For a complete meeting prep workflow, see Part 8: Meeting Prep That Doesn't Suck.)

The "Competitor Customer" Research​

"I need to research [Company] as a potential customer. They currently use [Competitor]. Research what they might be frustrated with based on [Competitor] reviews on G2 and Reddit. Find their most likely pain points and suggest an angle for approaching them about switching."

(More on competitive intelligence in Part 5.)

The "Trigger Event" Research​

"I just saw that [Company] announced [trigger event — new funding, executive hire, product launch]. Research everything about this event and how it creates an opportunity for us to reach out with [our solution]. Give me the angle and draft an email opening."

The "Reactivation" Research​

"[Company] was a prospect 6 months ago but went cold. Research what's changed since then — new leadership, new funding, new challenges, shifts in their tech stack. Help me find an angle to re-engage them."

Common Mistakes to Avoid​

1. Researching Without Intent​

Don't just research random accounts because you can. Start with a signal — a website visit, a LinkedIn engagement, a trigger event. Research is only valuable when it leads to action.

2. Over-Researching​

Claude Code can give you pages of information. You don't need pages. You need 3 things: who to contact, what to say, and why now. Everything else is noise.

3. Not Verifying Key Claims​

Claude Code is incredibly capable, but it can occasionally get details wrong. If your outreach hinges on a specific fact — "I saw you just raised Series B" — verify it before you reference it. Nothing kills credibility faster than getting a basic fact wrong.

4. Copy-Pasting Without Personalization​

Claude Code gives you raw material, not finished outreach. Always add your own voice, adjust for tone, and make it feel like something a real human would write. (More on this in Part 3: Writing Hyper-Personalized Cold Emails.)

Making It a Daily Habit​

The SDRs who get the most value from Claude Code don't use it sporadically. They build it into their daily routine:

Morning Sprint (15 minutes):

1. Check MarketBetter for overnight website visitors and intent signals
2. Feed the top 10-15 accounts into Claude Code for batch research
3. Review the dossiers, pick your top 5, and plan your outreach

Before Every Call (2 minutes):

1. Quick Claude Code research on the specific person you're about to call
2. Scan for recent LinkedIn posts, company news, or mutual connections
3. Walk into the call with context

End of Day (5 minutes):

1. Research tomorrow's follow-up targets
2. Use Claude Code to draft follow-up messages for today's conversations
3. Queue them in MarketBetter for morning delivery

For the full daily routine, check out Part 10: The Complete AI SDR Playbook.

The ROI of AI-Powered Research​

Let's put real numbers on this:

• Time saved per account: ~18 minutes (from 20 minutes to 2 minutes)
• Accounts researched per day: 50 (up from 10-15)
• Hours reclaimed per day: ~3 hours (redirected to selling)
• Expected impact on pipeline: 2-3x more conversations with researched, personalized outreach

That's not incremental improvement. That's a fundamentally different job.

Try This Today​

Here's your action item:

1. Pick 3 accounts that you're planning to reach out to this week
2. Open Claude Code and use the Basic Dossier Prompt from above for each one
3. Compare the output to what you'd have found doing manual research
4. Time yourself — how long did Claude Code take vs. how long you'd normally spend?

Most SDRs who try this have a reaction somewhere between "wait, that's it?" and "I've been doing this manually like a fool." Either way, you'll never go back.

---

This is Part 2 (🟢 Basic) of our 10-part series on Claude Code + MarketBetter for SDRs. Next up: Part 3: Writing Hyper-Personalized Cold Emails at Scale →

Ready to pair AI research with real-time buyer intent signals? Book a MarketBetter demo to see visitor identification in action.

🟢 Series Difficulty: BASIC (Part 3 of 10) — Builds on the research skills from Part 2. Still beginner-friendly.

Here's the paradox every SDR faces: personalization works, but it doesn't scale. And scale works, but it isn't personal.

You know from experience that a truly personalized email — one that references a prospect's recent LinkedIn post, connects it to a business challenge, and offers a relevant insight — gets replies. Maybe 15-25% of the time. But writing those emails takes 10-15 minutes each. At that rate, you can send maybe 20 personalized emails per day.

On the other hand, you could send 200 templated emails per day. But everyone can smell a template from a mile away. Open rates drop. Reply rates hover near zero. Your domain reputation takes hits. And you feel like a spammer.

What if you could write 100+ genuinely personalized emails per day? Not "Hi {first_name}, I see you work at {company}" personalization. Real personalization — the kind that makes a prospect think "this person actually did their homework."

That's what we're covering in Part 3 of our Claude Code + MarketBetter series. And it starts with understanding why most "personalized" emails still feel fake.

In Part 2, we learned how to use Claude Code to build prospect dossiers in 30 seconds. Now we're taking that research and turning it into emails that actually get replies. Same simple prompting approach — we're just adding a new skill on top of what you already know.

Why "Personalized" Emails Still Feel Generic​

Most SDR sequences use what we'll call Level 1 personalization: name, company, and maybe industry. Here's what that looks like:

"Hi Sarah, I noticed Acme Corp is growing fast in the SaaS space. Companies like yours often struggle with outbound pipeline. Would you be open to a quick chat about how we can help?"

That email technically has personalization tokens. But it says nothing that couldn't apply to 10,000 other companies. Sarah reads it and thinks: "Template. Delete."

Level 2 personalization adds a company-specific reference:

"Hi Sarah, congrats on Acme Corp's Series B! As you scale your sales team, pipeline generation usually becomes a bottleneck..."

Better. But Sarah got 15 other emails that mentioned her Series B. Every SDR with a trigger event tool sends the same email.

Level 3 personalization — the kind that actually gets replies — connects multiple data points into a genuine insight:

"Hi Sarah, I saw your LinkedIn post about the challenge of maintaining email quality while scaling your SDR team from 5 to 15. That resonated — we've seen that exact inflection point at companies like yours where deliverability tanks because reps start blasting templates. We built something specifically for this: AI sequences that write personalized emails for each prospect based on their actual website behavior, not just firmographics. Would it be worth 15 minutes to see how it works?"

That email demonstrates real research, connects it to a genuine pain point, and offers a specific solution. The prospect can tell a human put thought into it. That's the bar. And Claude Code helps you hit it at scale.

The 3-Step Personalization Framework​

Here's the workflow:

Step 1: Research (30 seconds)​

Use Claude Code to gather personalization ingredients. If you followed Part 2, you already have your dossier. Now you need to extract the personalization hooks — specific data points you can reference in your email.

Prompt:

"I'm writing a cold email to [Name], [Title] at [Company]. I sell [your product]. Research this person and give me:

1. Their 2-3 most recent LinkedIn posts or shared content (topics, not URLs)
2. Something notable about their company in the last 60 days
3. A likely pain point someone in their role at their company size faces
4. Any connection between what they've publicly discussed and what I sell

Keep it concise — bullet points only."

Claude Code returns something like:

• Recent LinkedIn activity: Posted about challenges hiring experienced SDRs in a competitive market. Shared an article about AI in sales with the comment "interested but skeptical."
• Company news: Expanded into the European market last quarter. Hiring 8 roles across sales and marketing.
• Likely pain point: Scaling outbound into new markets without established brand recognition. New SDR hires take 3-4 months to ramp.
• Connection to your product: Your solution reduces ramp time and enables personalized outreach in new markets.

Step 2: Draft (15 seconds)​

Now feed those hooks back into Claude Code with your email prompt:

"Write a cold email from me ([your name], [your title] at [your company]) to [Name], [Title] at [Company]. Use these personalization hooks:

[Paste the hooks from Step 1]

Rules:

• Under 120 words
• No more than 4 short paragraphs
• Opening line references something specific about THEM (not about us)
• One clear, low-friction CTA
• Tone: confident, casual, helpful — not salesy
• Don't use the words 'innovative,' 'cutting-edge,' 'game-changing,' or 'synergy'
• Don't start with 'I hope this email finds you well'"

Claude Code generates something like:

Subject: quick thought on your EU expansion

Hi Sarah,

Saw your post about the SDR hiring challenge — scaling into Europe without established brand recognition is a different beast. Most teams we talk to find that what worked domestically doesn't translate 1:1.

The biggest lever we've seen: letting AI handle the initial personalization research so each rep can send thoughtful outreach from day one instead of month three. Cuts ramp time dramatically.

We help teams like yours run hyper-personalized outbound at scale without sounding like robots. Worth 15 minutes to see how it might help with the EU push?

Best, [Your name]

Step 3: Review and Send (30 seconds)​

Read it. Adjust anything that doesn't sound like you. Then drop it into MarketBetter as the first touch of an AI-powered sequence.

Total time: under 2 minutes for an email that would have taken 10-15 minutes to write manually. And the quality is as good or better than what most SDRs produce by hand.

Scaling This to 50-100 Emails Per Day​

Individual emails are great. But the real power of this workflow is batch processing. Here's how to scale it:

The Batch Email Workflow​

1. Export your MarketBetter signal list — the accounts showing intent today
2. Feed them to Claude Code in batches of 10:

"I need to write personalized cold emails to 10 prospects. For each one, research their LinkedIn and company, find a personalization hook, and write a cold email under 120 words. My product is [description]. My ICP is [description].

Here are the 10 prospects:

1. Sarah Chen, VP Sales at Acme Corp
2. James Miller, CRO at Beta Labs
3. [etc.]

Give me the emails in order, each with the subject line, personalization hook used, and the email body."

3. Review the batch — Claude Code returns 10 drafted emails in 2-3 minutes
4. Edit the ones that need tweaking — usually 2-3 out of 10
5. Load them into MarketBetter sequences — each prospect gets a multi-touch sequence starting with this personalized first email

At this pace, you can produce 50-100 personalized emails in under an hour. That leaves you 6+ hours for calls, follow-ups, and meetings.

The MarketBetter Delivery Engine​

Writing the email is only half the battle. You also need:

• Smart send timing — MarketBetter optimizes delivery times based on when prospects are most likely to engage
• Multi-touch sequences — Your personalized first email is followed by AI-generated follow-ups that maintain context
• Signal-triggered sends — If a prospect visits your site after receiving an email, MarketBetter can trigger the next touch immediately
• Deliverability management — Email warmup, rotation, and reputation monitoring to make sure your messages land in inboxes

This is why the Claude Code + MarketBetter combo is so powerful. Claude Code creates the content. MarketBetter handles the delivery, timing, and behavioral triggers. You handle the conversations that result.

For more on optimizing deliverability, check out our post on how to improve email open rates.

Email Templates That Work (Starter Prompts)​

Here are proven prompt templates for common SDR scenarios:

The Trigger Event Email​

"Write a cold email to [Name] at [Company]. The trigger: [trigger event]. Connect this event to a likely need for [your solution]. Keep it under 100 words, conversational, with a question as the CTA."

The Competitor Displacement Email​

"Write a cold email to [Name] at [Company]. They currently use [Competitor]. Based on common [Competitor] complaints (reference G2 reviews), highlight 1-2 pain points they might have and position [your solution] as the alternative. Don't bash the competitor — be respectful but clear about the difference."

The Social Proof Email​

"Write a cold email to [Name] at [Company]. They're in [industry] with ~[size] employees. Reference a similar company in their industry (without naming them specifically) who saw [specific result] using our solution. Make it credible and specific without sounding like a case study."

The Re-Engagement Email​

"Write a re-engagement email to [Name] at [Company]. They were interested 3 months ago but went silent. Research what's changed at their company since then and use a new angle. Don't reference the old conversation directly — make it feel like a fresh, value-led touchpoint."

For more on cold email best practices, see our comprehensive guide on how to write cold emails that get replies.

The Anatomy of Emails That Get Replies​

Based on thousands of outbound emails, here's what Claude Code should always include (and avoid):

Always Include:​

• A specific reference to the prospect (not their company — them personally)
• A clear "why now" signal — why you're reaching out at this moment
• One concrete value proposition — what's in it for them
• A low-friction CTA — "worth 15 minutes?" beats "can we schedule a 30-minute demo?"

Always Avoid:​

• Company-centric language — "We're the leading..." Nobody cares
• Multiple CTAs — Pick one ask, not three
• Long paragraphs — 2-3 lines max per paragraph
• Buzzwords — "AI-powered solution" "cutting-edge platform" "innovative approach"
• Fake urgency — "Limited spots available" on a demo calendar

Optimal Structure:​

1. Line 1: Something about THEM (proves you did research)
2. Line 2-3: Connect their situation to a common challenge
3. Line 4-5: How you help (one sentence, specific)
4. Line 6: CTA (question format, low commitment)

Tell Claude Code these rules upfront and it'll follow them consistently.

Quality Control: The Human Filter​

Even with great AI-generated emails, you are the quality filter. Here's what to check before hitting send:

The 30-Second Review Checklist:​

1. Does it sound like me? If not, adjust the tone
2. Is the personalization accurate? If Claude Code referenced a LinkedIn post, verify it exists
3. Would I respond to this email? If not, it needs work
4. Is the CTA clear and reasonable? One ask, low friction
5. Is it under 120 words? If it's longer, cut it

Most emails pass this check on the first try. When they don't, it takes 30 seconds to fix. That's still way faster than writing from scratch.

Advanced: Building Your Email Style Guide​

Over time, you'll develop preferences. Maybe you always open with a question. Maybe you like shorter emails. Maybe you have specific phrases you love or hate.

Create a personal style guide and include it in every Claude Code prompt:

"My email style guide:

• Always under 100 words
• Never use exclamation marks
• Open with an observation, not a question
• Sign off with 'Best,' not 'Thanks,'
• Write at a 7th-grade reading level
• Use short sentences. Like this one.
• CTA format: 'Worth [X] minutes to [benefit]?'"

Claude Code will adapt to your style immediately. After a few emails, it feels like your voice, not a robot's.

Measuring What Works​

The beauty of running AI-personalized emails through MarketBetter is that you get data back:

• Which personalization angles get the highest reply rates? (Trigger events? LinkedIn posts? Hiring signals?)
• What email length performs best? (Usually shorter wins)
• Which CTAs convert? ("15-minute call" vs. "quick question" vs. "worth a look?")
• What send times work? (MarketBetter optimizes this automatically)

Feed these insights back into your Claude Code prompts to continuously improve. This creates a flywheel: better data → better prompts → better emails → more replies → more data.

Try This Today​

Here's your concrete action item:

1. Pick 5 prospects you need to email this week
2. Use the 3-step framework above: Research → Draft → Review
3. Time yourself — how long does it take per email with Claude Code vs. without?
4. Track the results — note your reply rate on Claude Code-assisted emails vs. your usual templates

Most SDRs see 2-3x higher reply rates on AI-personalized emails vs. templates. And they produce them 5x faster. That's the whole ball game.

---

This is Part 3 (🟢 Basic) of our 10-part series. You've completed the Basic tier! Next up: Part 4: LinkedIn-to-Pipeline → — your first Medium-level workflow.

Want AI-powered sequences that deliver hyper-personalized emails at the perfect moment? Book a MarketBetter demo to see it in action.

🟡 Series Difficulty: MEDIUM (Part 5 of 10) — Builds on research skills from Part 2 and outreach techniques from Part 3.

Every SDR has had this experience: you're on a call with a promising prospect, and they drop the bomb — "We're actually already using [Competitor]. We're pretty happy with them."

And you freeze. Because you don't really know what [Competitor]'s customers love, what they hate, or why they might consider switching. You mumble something about being "different" and the call goes nowhere.

Now imagine a different scenario. The prospect says the same thing, and you respond:

"Makes sense — [Competitor] does some good things, especially with [specific feature]. What I hear from a lot of teams who've been on it for 12+ months is that [specific pain point from G2 reviews] starts to become a real issue as they scale. Have you run into that?"

The prospect pauses. "Actually... yeah. That's been a headache."

That's competitive intelligence in action. And in Part 5 of our Claude Code + MarketBetter series, we'll show you how to build a competitive intel system that runs on autopilot — so you always know exactly what your competitors' customers are saying.

By now, you're comfortable with the basics. In Part 2, you learned to research individual prospects. In Part 3, you turned that research into personalized emails. Here, we're applying those same research skills to a different target: your competitors and their customers. The prompting patterns are similar — you're just asking Claude Code different questions.

Why SDRs Need Competitive Intelligence​

Most SDRs think competitive intel is the sales manager's job. Or product marketing's. And sure, those teams should build battlecards and positioning docs. But here's the reality:

1. Those battlecards are usually 6 months out of date — The competitive landscape moves fast. What was true last quarter isn't necessarily true today.

2. Generic battlecards don't help with specific objections — When a prospect mentions a specific competitor feature or complaint, you need specific answers. Not bullet points.

3. The best competitive intel comes from customers, not marketers — Reviews on G2, Reddit comments, LinkedIn posts, and Twitter/X threads from actual users tell you what the sales deck never will.

4. Competitive intel is a prospecting goldmine — If you know that [Competitor]'s customers are complaining about [specific issue], you can proactively target those customers with messaging that addresses that exact pain.

Claude Code turns competitive monitoring from a "nice to have" into an automated part of your daily workflow.

Building Your Competitive Intelligence System​

Step 1: Map Your Competitive Landscape​

Start by telling Claude Code who you're watching:

"I sell [your product] in the [your category] space. My main competitors are:

1. [Competitor A] — [brief description]
2. [Competitor B] — [brief description]
3. [Competitor C] — [brief description]

For each competitor, give me:

1. A summary of their current positioning and key differentiators
2. Their ideal customer profile (based on their website and case studies)
3. Where their customers are most likely to leave reviews or discuss the product (G2, Capterra, Reddit, etc.)
4. Known weaknesses based on public reviews and discussions
5. Recent product changes or announcements that affect our competitive positioning"

This gives you your baseline. Save this output — you'll reference it regularly.

Step 2: Review Mining​

Online reviews are the most honest source of competitive intelligence. Customers don't pull punches on G2 or Capterra.

The G2 Review Analysis Prompt:

"Analyze the most recent G2 reviews for [Competitor]. I need:

1. Top 5 things customers love — What keeps them on the platform?
2. Top 5 complaints or pain points — What frustrates them most?
3. Common switching triggers — What would make them consider alternatives?
4. Feature gaps mentioned — What do customers wish the product did?
5. Customer profiles — What type of company (size, industry) seems happiest vs. unhappiest?

Organize this so I can use it in sales conversations. Give me specific, quotable insights, not generic summaries."

The output becomes your competitive playbook. When a prospect says "we use [Competitor]," you already know:

• What they probably like (so you don't trash-talk those features)
• What frustrates them (so you can empathize)
• When they'd consider switching (so you can test those triggers)

Step 3: Job Posting Intelligence​

Competitors' job postings reveal more about their strategy than any press release. Here's how to mine them:

"Research the current job openings at [Competitor]. Based on their hiring patterns, tell me:

1. Are they growing or restructuring? (Lots of new roles = growth. Lots of leadership roles = restructuring.)
2. What teams are they building? (Hiring enterprise sales = moving upmarket. Hiring customer success = retention issues.)
3. What technology are they investing in? (Job requirements reveal their tech stack and priorities.)
4. Any signals about product direction? (Hiring ML engineers = building AI features. Hiring integration engineers = expanding platform.)
5. How does this affect our competitive positioning?"

This intelligence helps you anticipate competitor moves before they announce them.

Step 4: Social Listening​

LinkedIn, Twitter/X, and Reddit are where unfiltered opinions live. Claude Code can help you process what people are saying:

"Research what people are saying about [Competitor] on LinkedIn, Twitter, and Reddit in the last 30 days. Look for:

1. Customer complaints or frustrations
2. Praise for specific features
3. Comparisons to other tools (including ours)
4. Posts from [Competitor]'s employees that reveal company direction
5. Discussions about switching from or to [Competitor]

Summarize the sentiment and give me 3 actionable takeaways I can use in prospecting."

Turning Intel Into Outreach​

Here's where it gets tactical. Competitive intelligence isn't just for handling objections — it's for creating opportunities.

Play 1: The "Pain Point Poach"​

When you know a competitor's customers are frustrated about something specific, you can proactively target those customers:

"Based on the G2 review analysis of [Competitor], their customers' biggest pain point is [specific pain]. Write me 3 different cold email angles targeting [Competitor]'s customers that:

1. Don't mention [Competitor] by name
2. Address the pain point as a general industry challenge
3. Position our solution as solving it specifically
4. Are under 100 words each"

Claude Code might produce something like:

Subject: scaling outbound without the deliverability hit

Hi [Name], I've been talking to a lot of sales teams in the [industry] space this month, and there's a pattern: once you hit 10+ SDRs, email deliverability tanks. Warmup tools help, but they don't solve the root cause — which is usually template volume overwhelming domain reputation.

We take a different approach: AI-personalized emails that look handwritten, sent at volumes that keep your domain healthy. Worth 15 minutes to see how?

Notice: no competitor name mentioned. Just addressing a known pain point. The prospect self-selects because the pain is relevant to them.

Play 2: The "Review Response" Outreach​

When someone posts a negative review of a competitor on G2, it's an invitation:

"Write me a LinkedIn message to reach out to someone who posted a 3-star review of [Competitor] on G2. They mentioned [specific complaint]. Don't reference their review directly (that's creepy). Instead, engage them around the topic of [pain point] and offer a relevant insight or resource. Keep it helpful, not salesy."

Play 3: The "Job Change" Competitor Intel​

When a competitor's employee leaves (especially in customer-facing roles), their customers may be affected:

"A senior Customer Success Manager at [Competitor] just left the company (per LinkedIn). Research:

1. How many accounts they likely managed
2. How this might impact those customers
3. Draft an outreach message to [Competitor]'s customers that addresses potential service gaps without being opportunistic"

Play 4: The "Feature Gap" Positioning​

When reviews consistently mention a missing feature that you offer, use it:

"G2 reviews of [Competitor] frequently mention that they lack [specific feature/capability]. We have this. Write me a cold email to [Competitor]'s customers that naturally highlights this capability as part of how modern teams solve [related challenge]. Don't position it as 'we have what they don't' — position it as 'here's how leading teams are approaching this.'"

Building Your Competitive Dashboard​

Create a running document that Claude Code helps you maintain. Here's the structure:

Competitor: [Name]

Category	What We Know	Last Updated	Source
Key strengths	[list]	[date]	G2, website
Key weaknesses	[list]	[date]	G2, Reddit
Recent product changes	[list]	[date]	Blog, LinkedIn
Hiring signals	[list]	[date]	LinkedIn Jobs
Customer sentiment trend	[up/down/stable]	[date]	Social listening
Best outreach angle	[angle]	[date]	Review analysis

Update this monthly. It takes 15 minutes with Claude Code — a task that would take a full day without it.

Feeding Intel Into MarketBetter​

Your competitive intelligence should directly inform your MarketBetter targeting:

1. Build competitor-specific lead lists — Export [Competitor]'s customers from your CRM or Sales Nav and import them into MarketBetter via the Chrome Extension (see Part 4)

2. Create competitor-specific sequences — Use Claude Code to write email sequences tailored to each competitor's known pain points. Load these into MarketBetter.

3. Set up website monitoring — MarketBetter's visitor identification tells you when a competitor's customer visits your site. That's a hot signal — if they're browsing your pricing page, they're actively evaluating alternatives.

4. Track engagement patterns — When a competitive prospect opens your emails multiple times or visits your site repeatedly, MarketBetter flags them for immediate follow-up.

The Ethics of Competitive Intelligence​

A quick but important note: competitive intelligence should be ethical and professional.

Do:

• Use publicly available information (reviews, social posts, job listings, press releases)
• Focus on understanding market dynamics, not personal attacks
• Be respectful of competitors in conversations with prospects
• Let your product's strengths speak for themselves

Don't:

• Misrepresent competitor capabilities
• Use deceptive tactics to gather information
• Trash-talk competitors in outreach
• Pose as a customer to get competitor pricing or demos

The best competitive sellers win by being better informed, not by tearing down the competition.

A Weekly Competitive Intel Routine​

Here's how to make competitive monitoring a sustainable habit:

Every Monday (15 minutes):

1. Ask Claude Code to check for new developments at each competitor (news, announcements, product changes)
2. Review the summary and update your competitive dashboard
3. Flag anything that changes your outreach messaging

Every Month (30 minutes):

1. Do a full review mining refresh — G2, Capterra, Reddit
2. Update your competitor battlecard with new insights
3. Ask Claude Code to suggest updated email angles based on new competitive intel
4. Share key findings with your sales team

Quarterly (1 hour):

1. Full competitive landscape review
2. Update positioning and messaging
3. Create or refresh competitor-specific outreach sequences in MarketBetter

Try This Today​

Here's your action item:

1. Pick your #1 competitor — the one you lose deals to most often
2. Ask Claude Code to analyze their recent reviews on G2 using the Review Analysis prompt above
3. Identify the top 3 pain points their customers mention
4. Draft one cold email targeting a competitor customer, addressing one of those pain points (without naming the competitor)
5. Save the competitive analysis somewhere you can reference before your next call

You'll walk into your next competitive deal armed with specific, customer-validated insights instead of generic talking points. That's the difference between "we're different" and "I've heard from teams in your situation that X is a real challenge — have you experienced that?"

---

This is Part 5 (🟡 Medium) of our 10-part series. Next up: Part 6: Building a Lead Scoring Model Without a Data Team →

Want to know when your competitors' customers start visiting your website? Book a MarketBetter demo to see real-time visitor identification in action.

🟡 Series Difficulty: MEDIUM (Part 6 of 10) — Uses research skills from Part 2 and connects to MarketBetter's signal data. The most analytical post so far.

Every SDR knows the frustration: you've got 200 leads in your queue, and they all look the same. Same priority level. Same generic tags. No clear signal about who to call first.

So you do what every SDR does — you start at the top of the list and work your way down. Or you sort alphabetically. Or you go with gut instinct. None of these are strategies. They're survival mechanisms.

Meanwhile, the enterprise sales teams down the hall have sophisticated lead scoring models built by data teams, powered by Marketo or HubSpot, with algorithms that predict which leads are most likely to convert. You don't have that. You don't have a data team. You don't have a marketing ops person who can build predictive models. You have a CRM, a list of leads, and a quota.

Here's the good news: you can build a lead scoring model in 30 minutes using Claude Code. It won't be as sophisticated as a machine-learning-powered enterprise system. But it'll be 10x better than alphabetical sorting. And when you pair it with MarketBetter's daily playbook, you'll have a complete system for knowing exactly who to call first, every morning.

This is Part 6 of our Claude Code + MarketBetter series — the last of the Medium-level posts. In the Basic posts (Parts 1-3), you learned to research and write. In Parts 4 and 5, you built multi-step workflows for LinkedIn and competitive intel. Now you're going to do something more analytical: use Claude Code to build a system that makes decisions for you. You'll define scoring rules, apply them to data, and create a repeatable process that gets smarter over time.

If that sounds complex, don't worry. The Claude Code prompts are just as straightforward as the ones you've been using. You're just asking slightly more structured questions.

Let's build your scoring model.

What Is Lead Scoring (and Why Do You Need It)?​

Lead scoring assigns a numerical value to each lead based on how likely they are to buy. Higher score = more likely to convert = call them first.

Simple concept. But most scoring models fail because they're either:

• Too complex — Built by data teams with 47 variables that nobody understands
• Too simple — "Enterprise = high priority" doesn't tell you anything useful
• Too static — Set once and never updated, even as your market changes
• Disconnected from action — Great model, but nobody uses it in their daily workflow

The model we're going to build avoids all of these traps. It uses three categories of signals, is easy to understand, and plugs directly into your MarketBetter daily playbook.

For a deeper dive on scoring best practices, check out our lead scoring best practices guide.

The Three Pillars of SDR Lead Scoring​

Your scoring model is built on three pillars:

Pillar 1: Firmographic Fit (Does this company match our ICP?)​

This is the "who are they?" question. It includes:

• Company size (employee count or revenue)
• Industry
• Geography
• Technology used
• Funding stage

Pillar 2: Behavioral Signals (Are they actively interested?)​

This is the "what are they doing?" question:

• Website visits (especially high-intent pages like pricing)
• Email engagement (opens, clicks, replies)
• Content downloads
• Social media interactions
• Event attendance

Pillar 3: Timing Signals (Is now the right moment?)​

This is the "when is the right time?" question:

• Recent funding rounds
• Leadership changes
• Job postings in relevant departments
• Competitor contract renewals
• Seasonal buying patterns

Each pillar contributes to a total score. The leads with the highest combined score get your attention first.

Step-by-Step: Building Your Model with Claude Code​

Step 1: Define Your Ideal Customer Profile​

Before you can score leads, you need to know what a great lead looks like. Ask Claude Code:

"Help me define my Ideal Customer Profile (ICP). I sell [your product] to [your market]. My best customers tend to be:

• Company size: [range]
• Industry: [industries]
• Typical buyer title: [titles]
• Common pain points: [pains]

Based on this, create a firmographic scoring rubric with a 0-30 point scale. Give me the exact criteria for each score level."

Claude Code returns something like:

Firmographic Scoring (0-30 points)

Criteria	Points	Details
Company Size	0-10	1-49 employees: 2pts, 50-200: 7pts, 201-500: 10pts, 500-1000: 8pts, 1000+: 5pts
Industry	0-10	SaaS/Tech: 10pts, Financial Services: 8pts, Healthcare: 6pts, Manufacturing: 3pts, Other: 1pt
Geography	0-5	US: 5pts, UK/Canada: 4pts, Western EU: 3pts, Other: 1pt
Funding Stage	0-5	Series A-C: 5pts, Seed: 3pts, Bootstrapped: 2pts, Public: 2pts

Notice how the scoring reflects YOUR specific ICP. A 200-person SaaS company in the US scores higher than a 5,000-person manufacturer in Asia — because that's who buys from you.

Step 2: Build the Behavioral Scoring Component​

Now add the engagement signals. This is where MarketBetter's data becomes critical:

"Now create a behavioral scoring rubric (0-40 points) based on these engagement signals I can track:

• Website visits (from MarketBetter visitor identification)
• Pages visited (pricing page, case studies, product pages)
• Visit frequency (one-time vs. return visitor)
• Email engagement (opens, clicks, replies)
• LinkedIn engagement (profile views, connection accepts, post interactions)

Weight the signals by purchase intent. A pricing page visit is more valuable than a blog page visit."

Claude Code returns:

Behavioral Scoring (0-40 points)

Signal	Points	Details
Pricing page visit	10	Single strongest buying signal
Case study/testimonial page	7	Evaluating social proof
Product/feature pages	5	Active research phase
Blog/content visit	2	Awareness stage
Return visitor (2+ sessions)	8	Sustained interest
Multi-page session (3+ pages)	5	Deep engagement
Email opened (2+ times)	3	Interest but not action
Email link clicked	5	Active engagement
Email replied	8	Direct interest
LinkedIn connection accepted	3	Openness to conversation

Step 3: Build the Timing Scoring Component​

Finally, add signals that indicate the timing is right:

"Create a timing/trigger scoring rubric (0-30 points) based on these signals:

• Recent funding announcement
• Executive leadership changes
• Job postings in relevant departments
• Company expansion/new office
• Technology changes or migrations
• Contract renewal season (if known)

Weight by urgency of the buying window."

Claude Code returns:

Timing Scoring (0-30 points)

Signal	Points	Details
New funding (last 60 days)	8	Budget available, growth mandate
New CRO/VP Sales (last 90 days)	7	New leaders bring new tools
Hiring SDRs/AEs (active postings)	6	Scaling sales = needs tools
Hiring demand gen/marketing	5	Building pipeline infrastructure
Technology migration announced	6	Open to new vendors
Competitor contract likely up for renewal	5	Evaluation window
Expansion/new market entry	4	Growing pains = new needs

Step 4: Score Your Existing Leads​

Now apply the model. Export your lead list from your CRM and feed it to Claude Code:

"I have a list of 100 leads. Apply this scoring model to each one:

[paste your scoring rubrics]

For each lead, I have:

• Company name, size, industry, geography
• Website visit data from MarketBetter (pages visited, frequency)
• Email engagement data (opens, clicks, replies)
• Any known trigger events

Score each lead across all three pillars, calculate the total, and rank them from highest to lowest. Group them into tiers:

• Hot (70-100): Call immediately
• Warm (40-69): Prioritize this week
• Cool (20-39): Nurture sequence
• Cold (0-19): Low priority

Here's the data: [paste your lead list with available data]"

In 2-3 minutes, you have a fully scored, prioritized lead list. No data team required.

Using MarketBetter's Daily Playbook as the Execution Layer​

A scoring model is useless if it doesn't change your daily behavior. Here's how to connect your Claude Code scoring model to your MarketBetter workflow:

The Morning Ritual (10 minutes)​

1. Check MarketBetter's daily playbook — New website visitors, return visitors, engaged prospects
2. Apply your scoring model — New behavioral signals from overnight activity change scores
3. Identify your Hot tier — These are your first calls of the day
4. Identify new entrants to Warm tier — Prospects who were Cool but just visited your pricing page. They jumped tiers overnight.
5. Execute — Start with the highest-scored leads and work down

Signal-Triggered Score Updates​

MarketBetter sends you real-time signals throughout the day. Each signal should update your mental scoring:

• Prospect visited pricing page → +10 points. If they were Warm, they're now Hot. Call them.
• Prospect opened your email 3 times → +5 points. They're interested. Send a follow-up.
• Prospect visited your site from a new device → +3 points. They might be sharing your site with colleagues. Multi-stakeholder interest.
• Cold lead returned to your site → Re-score them entirely. They might have jumped from Cold to Warm in one visit. (More on re-engagement in Part 9.)

Automated Scoring with MarketBetter​

MarketBetter's built-in engagement tracking does much of the behavioral scoring automatically. Your Claude Code model handles the firmographic and timing scoring that MarketBetter doesn't cover. Together, they give you a complete picture.

For more on how intent data drives this process, read our guide to what intent data is and how it drives growth.

Refining Your Model Over Time​

Your first scoring model won't be perfect. That's fine. Here's how to improve it:

Monthly Review (15 minutes)​

"Here are my last month's results:

• 15 leads scored Hot → 8 converted to meetings (53%)
• 30 leads scored Warm → 6 converted to meetings (20%)
• 45 leads scored Cool → 2 converted to meetings (4%)
• 10 leads scored Cold → 0 converted to meetings (0%)

Also, 3 meetings came from leads scored Cool or Cold. Here's what those leads had in common: [details]

Based on this data, what adjustments should I make to my scoring model? Are any signals over- or under-weighted?"

Claude Code will analyze the conversion data and suggest specific adjustments. Maybe pricing page visits should be worth 15 points instead of 10. Maybe industry scoring needs recalibration. Make the adjustments and run the updated model.

The Feedback Loop​

Over 3-6 months, your scoring model gets increasingly accurate because you're refining it based on actual conversion data. This is essentially what data teams do with machine learning — just simpler and driven by your domain expertise instead of algorithms.

Advanced: Multi-Persona Scoring​

If you sell to multiple buyer personas, you might need different scoring models for each:

"I sell to two different personas:

Persona 1: VP of Sales (cares about pipeline and team productivity) Persona 2: RevOps Leader (cares about data quality and tech stack efficiency)

Create separate behavioral scoring rubrics for each persona. A VP of Sales visiting a case study page is different from a RevOps leader visiting an integration page — weight them differently."

This gives you nuanced prioritization. A RevOps leader on your integrations page might score higher than a VP of Sales on your blog — even though the VP is the more senior title — because the RevOps behavior signals active evaluation.

Common Scoring Mistakes to Avoid​

1. Over-weighting title/seniority — A Director who's actively researching is more valuable than a VP who isn't
2. Ignoring negative signals — Unsubscribes, bounced emails, and "not interested" replies should decrease scores
3. Scoring once and forgetting — Scores should be dynamic, updated with every new signal
4. Too many tiers — Hot/Warm/Cool/Cold is enough. Don't create 10 tiers that nobody can remember
5. Ignoring the denominator — If your Hot leads aren't converting at a higher rate than Warm leads, your model isn't working

Try This Today​

Here's your concrete action item:

1. Open Claude Code and use the prompts from Steps 1-3 above to build your scoring rubrics
2. Pick 20 leads from your current queue
3. Score them manually using your new model (estimate where you can)
4. Sort them by score and compare the order to how you would have prioritized them with gut instinct
5. Work the list in score order for one week and track your results

Most SDRs find that their intuition was right about 60-70% of the time. A scoring model gets you to 80-90%. That 20-30% improvement in prioritization translates directly to more meetings with less effort.

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This is Part 6 (🟡 Medium) of our 10-part series. You've completed the Medium tier! Next up: Part 7: CRM Cleanup in Minutes → — your first Advanced-level post.

MarketBetter's daily playbook surfaces the behavioral signals that power your lead scores. Book a demo to see how it works.

🔴 Series Difficulty: ADVANCED (Part 7 of 10) — Processes large datasets and builds maintenance systems. Best after completing Parts 1-6.

Nobody becomes an SDR because they love data hygiene. But here's the uncomfortable truth: dirty data is silently destroying your pipeline.

Every duplicate contact means wasted outreach. Every wrong email address means a bounced message hurting your domain reputation. Every outdated job title means you're personalizing against information that's no longer true. And every inconsistent company name means your reporting is wrong, your targeting is off, and your sequences are hitting the wrong people.

The average CRM has a 25-30% data decay rate every year. That means if you haven't cleaned your database in 12 months, nearly a third of your contacts have bad data — wrong emails, outdated titles, people who've left the company entirely.

Most SDRs know this. They just don't have time to fix it. Manual CRM cleanup is mind-numbing work that can take days. Nobody wants to spend their Friday afternoon deduplicating 3,000 contacts.

What if you could clean your entire CRM database in minutes instead of days? That's what we're covering in Part 7 of our Claude Code + MarketBetter series.

Welcome to the Advanced tier. In the Basic posts (Parts 1-3), you learned to research and write one prospect at a time. In the Medium posts (Parts 4-6), you built multi-step workflows and analytical models. Now we're leveling up to working with large datasets — hundreds or thousands of records at once. You'll feed Claude Code entire CRM exports, ask it to find patterns and problems, and build automated maintenance routines.

The prompts are still plain English — but you're processing more data, chaining more steps together, and building systems that run on their own. If you've been following the series, you're ready. If you're jumping in here, I'd recommend at least skimming Part 2 to understand the basics of prompting Claude Code.

Why Clean Data Matters for SDRs (More Than You Think)​

Before we get into the how, let's be clear about why this matters for your specific workflow:

1. Deliverability​

Every email that bounces hurts your sender reputation. Enough bounces and your emails start landing in spam — even the ones sent to valid addresses. If you're running outbound sequences through MarketBetter, clean data is the foundation of deliverability.

For more on improving email deliverability, see our guide on how to improve email open rates.

2. Targeting Accuracy​

MarketBetter's power comes from matching website visitors to your contact database and triggering the right outreach at the right time. If your CRM data is messy — duplicate companies, inconsistent names, missing fields — those matches don't happen. You miss signals.

3. Personalization Quality​

When you use Claude Code for prospect research and email writing (as we covered in Parts 2 and 3), you're pulling from your CRM data. If the title says "VP of Sales" but they were promoted to CRO six months ago, your personalization is wrong. Wrong personalization is worse than no personalization.

4. Reporting and Forecasting​

Your lead scoring model from Part 6 is only as good as the data feeding it. Dirty data produces inaccurate scores, which leads to bad prioritization, which means you're calling the wrong people first.

The Five Types of Dirty Data (and How Claude Code Fixes Each)​

Type 1: Duplicates​

The Problem: The same contact exists in your CRM multiple times with slightly different information. "Sarah Chen" and "S. Chen" at the same company. "Acme Corp" and "Acme Corporation" and "ACME" as three separate accounts.

The Claude Code Fix:

"I have a CRM export with [X] contacts. Find all probable duplicates based on:

1. Same email address
2. Same name + same company (accounting for variations like 'Sarah' vs 'S.')
3. Same company domain with different company names

For each duplicate set, tell me:

• Which record is the most complete (has the most filled fields)
• Which record was most recently updated
• Your recommendation for which to keep as the primary record
• What data from the duplicate(s) should be merged into the primary

Output as a CSV I can use for cleanup."

Claude Code can process thousands of records and identify duplicate clusters in minutes. What would take a sales ops person days takes AI minutes.

Type 2: Outdated Information​

The Problem: People change jobs every 2-3 years. Your CRM still shows them at their old company with their old title.

The Claude Code Fix:

"I have a list of 500 contacts. For each one, check if:

1. They're still at the listed company (based on any available public information)
2. Their job title might have changed
3. The company itself has changed (acquired, merged, shut down)

Flag any contacts that likely have outdated information. For each flagged contact, give me your best guess at updated information and your confidence level.

Here's the list: [paste or attach contact list]"

Pair this with MarketBetter's data enrichment to fill in the gaps. MarketBetter can verify email addresses and update contact information as part of its lead intelligence platform.

Type 3: Inconsistent Formatting​

The Problem: Company names are spelled 10 different ways. Job titles aren't standardized. Phone numbers have different formats. States are sometimes abbreviated, sometimes spelled out.

The Claude Code Fix:

"Standardize this CRM data:

1. Company names: Use the official company name (e.g., 'Salesforce' not 'salesforce.com' or 'SFDC' or 'Salesforce Inc.')
2. Job titles: Standardize to a consistent format (e.g., 'VP of Sales' not 'Vice President, Sales' or 'VP - Sales' or 'V.P. Sales')
3. Phone numbers: Format as +1 (XXX) XXX-XXXX
4. States: Use 2-letter abbreviations
5. Industries: Map to a standard list: [your industry categories]

Output the cleaned data in the same CSV format."

This sounds boring, but it's incredibly important for segmentation and targeting. When your company names are standardized, MarketBetter can accurately match website visitors to CRM records. When titles are consistent, your lead scoring model works properly.

Type 4: Missing Data​

The Problem: Half your contacts are missing key fields — no phone number, no industry, no company size. You can't score or prioritize leads you don't have data on.

The Claude Code Fix:

"I have 200 contacts with incomplete data. For each contact where I have at least a name and company, research and fill in:

1. Company size (employee count)
2. Industry
3. Company HQ location
4. Likely phone number format (direct dial if available publicly)
5. LinkedIn profile URL
6. Company website

Mark each enriched field with a confidence level (high/medium/low).

Here's the list: [paste contact list]"

This is where Claude Code's research capabilities really shine. It can enrich contacts at a pace that would take a human team weeks.

Type 5: Invalid Emails​

The Problem: Bounced emails hurt your sender reputation. But you don't know which emails are invalid until they bounce — and by then, the damage is done.

The Claude Code Fix:

"Analyze these email addresses for potential validity issues:

1. Obvious typos (e.g., '@gmial.com' instead of '@gmail.com')
2. Role-based emails that shouldn't be in a prospect database (info@, support@, sales@)
3. Personal email domains used for a business contact (gmail, yahoo, hotmail)
4. Email format inconsistencies within the same company (e.g., 'firstname.lastname@' vs 'flastname@')
5. Defunct domains

Flag and categorize each issue. For typos, suggest the corrected email.

[paste email list]"

This pre-screening catches obvious issues before you send. For full email validation, use a dedicated verification tool — but Claude Code's analysis catches the low-hanging fruit that most SDRs miss.

The Complete CRM Cleanup Workflow​

Here's the full process, start to finish:

Phase 1: Export and Assess (5 minutes)​

1. Export your CRM contacts as a CSV
2. Feed it to Claude Code:

"I just exported my CRM. It has [X] contacts. Give me a data quality assessment:

1. How many records have missing email addresses?
2. How many have missing phone numbers?
3. How many have missing company size or industry?
4. How many potential duplicates can you identify?
5. What's the overall data quality score (1-10)?
6. What should I fix first for the biggest impact?"

This assessment takes 2 minutes and tells you exactly where to focus.

Phase 2: Deduplicate (10 minutes)​

Run the duplicate detection prompt above. Review Claude Code's recommendations. Merge or delete the duplicates in your CRM.

Phase 3: Standardize (10 minutes)​

Run the standardization prompt. Import the cleaned, formatted data back into your CRM. Everything is consistent now.

Phase 4: Enrich (15 minutes)​

Run the enrichment prompt for contacts with missing data. Review the results (especially anything flagged as medium or low confidence). Update your CRM.

Phase 5: Validate Emails (5 minutes)​

Run the email validation prompt. Remove or correct invalid addresses. This saves your sender reputation from day one.

Total time: about 45 minutes for a complete CRM cleanup. Compare that to the 2-3 days it would take manually.

Maintaining Clean Data (So You Never Have to Do This Again)​

Cleanup isn't a one-time event. Data decays constantly. Here's how to stay clean:

The Weekly 5-Minute Check​

Every Friday, export your new contacts from the past week and run them through a quick Claude Code quality check:

"Review these 30 new CRM contacts added this week. Check for:

1. Duplicates with existing records
2. Missing key fields
3. Formatting issues
4. Obvious email validity issues

Flag anything that needs fixing."

Five minutes. Clean data maintained.

The Monthly Enrichment Refresh​

Once a month, take your top 100 accounts and check for updates:

"Check these 100 contacts for potential changes:

1. Have they changed jobs or titles?
2. Has their company been acquired, merged, or shut down?
3. Has the company announced funding, expansion, or layoffs?

Flag any records that need updating."

Automated Hygiene with MarketBetter​

MarketBetter helps maintain data quality in real time:

• Email verification on import — bad addresses are flagged before they enter your sequences
• Contact enrichment — missing fields are filled automatically using multiple data sources
• Company matching — website visitors are matched to your CRM records, surfacing both new leads and existing contacts that need updating

The ROI of Clean Data​

Let's put numbers on this:

• Bounce rate reduction: From 5-8% to under 2% → Protects your sender reputation
• Targeting accuracy: 25-30% more accurate matching → More website visitors connected to the right sequences
• Personalization quality: Fewer wrong titles and outdated references → Higher reply rates
• Time saved: 3-5 hours per week that you'd spend manually fixing data errors → Redirected to selling
• Sequence performance: Clean data + good targeting = 2-3x better email performance

Clean data isn't glamorous, but it's the infrastructure that makes everything else in this series work. Your lead scoring model (Part 6) needs accurate data. Your personalized emails (Part 3) need current information. Your Sales Nav imports (Part 4) need to not create duplicates.

Try This Today​

Here's your action item:

1. Export your CRM contacts (or even just one segment — like your top 200 accounts)
2. Ask Claude Code for a data quality assessment using the prompt from Phase 1
3. Fix the top 3 issues it identifies
4. Set a calendar reminder for a Friday 5-minute check

Your CRM will be cleaner by end of day than it's been in months. And every email, sequence, and outreach effort you run from that point forward will perform better because of it.

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This is Part 7 (🔴 Advanced) of our 10-part series. Next up: Part 8: Meeting Prep That Doesn't Suck →

Clean data powers better MarketBetter targeting and deliverability. Book a demo to see how the platform keeps your contact data fresh.

🔴 Series Difficulty: ADVANCED (Part 8 of 10) — Combines research, behavioral data, and multi-step workflows into an automated system.

You booked the meeting. Nice work. Now the real anxiety begins.

Who is this person? What does their company do? What did they look at on your website? What have they been talking about on LinkedIn? Do you share any mutual connections? Are they evaluating competitors? What should you lead with?

Most SDRs handle meeting prep one of two ways:

Option A: The 30-second glance. Quick peek at their LinkedIn profile, maybe a Google search. Walk into the meeting knowing their title and company name. Hope for the best.

Option B: The 45-minute deep dive. Open 15 tabs. Read every LinkedIn post. Stalk their company's news page. Check Crunchbase. Look at their tech stack. Write notes. Realize you've spent your entire morning prepping for one meeting.

Neither is great. Option A means you're underprepared and it shows. Option B means you prepared brilliantly for one meeting but didn't have time for anything else.

There's an Option C. In Part 8 of our Claude Code + MarketBetter series, we'll show you how to generate a comprehensive one-page prospect brief in under 3 minutes — for every single meeting on your calendar.

Why this is an Advanced post: In Part 2, you learned to research a single prospect. That was a one-step process: give Claude Code a name, get a dossier back. Meeting prep is different — it's a multi-step system that combines Claude Code research with MarketBetter's behavioral data, processes multiple meetings in a batch, and generates structured outputs for different meeting types (discovery vs. follow-up vs. executive). You're also layering in techniques from Part 5's competitive intel and Part 6's lead scoring to create briefs that are more strategic, not just informational.

The One-Page Brief: What You Actually Need to Know​

Before we get into the workflow, let's define what a great meeting prep brief contains. You don't need everything — you need the right things:

The Essential Six​

1. Person profile — Career history, role tenure, what they've done before this job, key LinkedIn activity
2. Company snapshot — What they do, size, growth stage, recent news (last 90 days)
3. Website behavior — What pages they visited, how many times, how recently (from MarketBetter)
4. Likely pain points — Based on their role, company size, and industry, what are they probably struggling with?
5. Talking points — 3 specific things you can reference that show you did your homework
6. Mutual connections — Anyone in your network who knows them or works at their company

That's it. Six categories, one page. You can review it in 2-3 minutes before the call and walk in prepared.

The Auto-Research Workflow​

Step 1: The Morning Brief Batch​

Every morning, check your calendar for the day's meetings. Then batch-process your prep:

"I have 4 meetings today. Research each prospect and generate a one-page brief for each:

1. Sarah Chen, VP of Sales at Acme Corp — Meeting at 10:00 AM
2. James Miller, CRO at Beta Labs — Meeting at 1:00 PM
3. David Park, Head of Growth at Gamma Solutions — Meeting at 2:30 PM
4. Lisa Wang, Director of Revenue Operations at Delta Tech — Meeting at 4:00 PM

For each person, I need:

Person Profile:

• Current role and how long they've been in it
• Previous roles (last 2-3 positions)
• Recent LinkedIn activity (topics they post about, articles they share)
• Education or notable affiliations

Company Snapshot:

• What the company does (one sentence)
• Employee count and growth trend
• Recent news (funding, product launches, leadership changes, last 90 days)
• Key competitors in their space

Likely Pain Points:

• Based on their role and company stage, what are they probably dealing with?
• What challenges are common for someone with their title at a company of their size?

Talking Points:

• 3 specific things I can reference to show I've done my homework
• Include at least 1 reference to their recent LinkedIn activity or public statements

Conversation Starters:

• 2-3 open-ended questions that will get them talking about their challenges

Format each brief to be scannable in under 3 minutes. No fluff."

Claude Code processes all 4 in 3-5 minutes. You now have meeting prep for your entire day — done before your first coffee is cold.

Step 2: Layer in MarketBetter Signals​

Now add the behavioral intelligence that only MarketBetter can provide. Check each prospect's website visit history:

• Pages visited: Did they look at pricing? Case studies? A specific product page? This tells you what they care about.
• Visit frequency: A prospect who visited 5 times in the past week is more serious than a one-time visitor.
• Visit timeline: Did they visit before or after agreeing to the meeting? Visiting after suggests they're actively evaluating.
• Multi-person visits: Is anyone else from their company browsing your site? This could indicate a buying committee forming.

Add these signals to your brief. Now you know not just WHO you're meeting, but WHAT they're already interested in.

Example:

"MarketBetter shows Sarah Chen visited our pricing page 3 times this week and our case studies page twice. Two other people from Acme Corp (titles unknown) visited our integrations page yesterday."

What this tells you: Sarah is past the "what does this product do?" phase. She's evaluating price and looking for social proof. Multiple visitors suggest she's not the only decision maker — there's likely a buying committee. Lead with ROI and case studies, not a product demo.

Step 3: The 5-Minute Pre-Call Review​

Ten minutes before your meeting, pull up your brief and do a final review:

1. Scan the key facts — Name pronunciation, title, company basics
2. Check MarketBetter one more time — Any new website activity since this morning?
3. Pick your opening — Which talking point or conversation starter feels most natural?
4. Identify your ask — What's your goal for this meeting? Next steps, intro to another stakeholder, demo scheduling?
5. Deep breath — You're prepared. You know more about this person than 95% of SDRs who take this meeting.

The Prospect Brief Template​

Here's what Claude Code's output looks like in practice:

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MEETING BRIEF: Sarah Chen, VP of Sales — Acme Corp​

Meeting: Tuesday 10:00 AM | Duration: 30 min | Type: Discovery

👤 PERSON

• VP of Sales at Acme Corp since March 2025 (~11 months)
• Previously: Director of Sales at XYZ Co (3 years), Senior AE at BigCo (2 years)
• Background: Promoted internally from SDR → AE → Director → VP. Knows the trenches.
• Recent LinkedIn: Posted about "the myth of the 100-activity day" (2 weeks ago). Shared an article about AI in sales with comment "skeptical but curious" (last week). Commented on a post about SDR burnout.
• Education: UCLA, Business Economics

🏢 COMPANY

• Acme Corp: B2B SaaS, marketing automation platform
• ~350 employees, Series C ($45M, June 2025)
• HQ: Austin, TX
• Recent: Launched AI email features (Jan 2026), hiring 3 SDRs and a Demand Gen Manager
• Competitors: HubSpot Marketing, Mailchimp, ActiveCampaign

🌐 WEBSITE ACTIVITY (MarketBetter)

• Visited pricing page 3x this week (Mon, Tue, Wed)
• Visited case studies page 2x
• 2 other Acme Corp visitors on integrations page yesterday
• First visit was 2 weeks ago (shortly after her "AI in sales" LinkedIn post)

🎯 LIKELY PAIN POINTS

1. Scaling SDR team (hiring 3 new reps) while maintaining quality outreach
2. New SDR ramp time — she came from the trenches and knows how long it takes
3. Pressure to show ROI on Series C investment — sales needs to grow fast

💬 TALKING POINTS

1. Reference her LinkedIn post about the "100-activity day myth" — ask what she thinks the right metric is
2. Mention the SDR hiring — "building a team from scratch is exciting but brutal. How are you thinking about ramp time?"
3. Her "skeptical but curious" comment about AI — perfect opening to discuss practical AI applications without over-promising

❓ CONVERSATION STARTERS

1. "I saw your post about rethinking activity metrics for SDRs — what does the ideal day look like for your team?"
2. "With 3 new SDRs coming on, what's your biggest concern about getting them productive quickly?"
3. "You mentioned being 'skeptical but curious' about AI in sales — what would change skeptical to convinced?"

---

That brief took Claude Code about 45 seconds to generate. You can review it in 3 minutes. And you'll walk into that meeting better prepared than Sarah's last 10 sales calls combined.

Advanced Meeting Prep Techniques​

The "Second Meeting" Prep​

First meetings are about discovery. Second meetings are about depth. Adjust your Claude Code prompt:

"I had a first meeting with [Name] last week. Here's what I learned: [paste your notes]. We have a second meeting tomorrow.

Research what's changed since our last conversation (any new company news, LinkedIn activity, market developments). Also:

1. Based on what they told me, what follow-up questions should I ask?
2. What competitive alternatives might they be evaluating?
3. Draft a brief agenda for the second meeting that builds on our first conversation
4. What objections should I be prepared for?"

The "Executive Meeting" Prep​

When you're meeting a C-suite executive, you need different preparation:

"I have a meeting with the CEO of [Company]. This is different from a typical SDR meeting. Research:

1. Their public speaking history — keynotes, podcasts, interviews
2. Their strategic vision for the company (based on public statements)
3. Board members and investors (who's influencing their decisions?)
4. Their management style and communication preferences (based on their public persona)
5. Business-level talking points — not feature-level, but ROI and strategic value"

The "Multi-Stakeholder" Prep​

When MarketBetter shows multiple people from the same company visiting your site, you might have a buying committee forming:

"I have a meeting with [Name] at [Company], but MarketBetter shows 3 other people from the company also browsing our site. Research:

1. Who are the other likely stakeholders? (Based on typical buying committee for our product)
2. What does each stakeholder care about? (VP Sales cares about pipeline, CFO cares about cost, etc.)
3. How should I tailor my messaging to address all stakeholders even though I'm only meeting one?
4. What questions should I ask to uncover the rest of the buying committee?"

Connecting Meeting Prep to Your Full Workflow​

Meeting prep doesn't exist in isolation. It connects to everything else in this series:

• Part 2: Prospect Research gave you the initial dossier. Meeting prep goes deeper.
• Part 3: Cold Emails got you the meeting. Now you deliver on the promise of that personalized outreach.
• Part 6: Lead Scoring told you this prospect was worth pursuing. Meeting prep confirms and refines that assessment.
• Part 9: Follow-Up starts immediately after the meeting. Your prep notes become the foundation of your follow-up sequence.

After the Meeting: Closing the Loop​

Great meeting prep doesn't end when the meeting starts. Here's how to maximize the value:

Immediate Post-Meeting (5 minutes)​

"Here are my notes from the meeting with [Name] at [Company]: [paste raw notes]

Organize these into:

1. Key pain points they mentioned
2. Decision criteria and timeline
3. Other stakeholders involved
4. Competitive alternatives they're considering
5. Specific next steps agreed upon
6. Draft a follow-up email that recaps the conversation and confirms next steps"

Update Your CRM​

Use the organized notes to update your CRM with structured information, not a wall of text. Your future self (and your AE, if you're handing off) will thank you.

Trigger the Right Sequence​

Based on how the meeting went, set up the appropriate MarketBetter sequence:

• Meeting went well, next steps agreed → Nurture sequence with relevant content
• Meeting went well, need to loop in other stakeholders → Multi-threading sequence
• Meeting was lukewarm, needs more time → Soft-touch follow-up sequence
• Meeting didn't go well → Long-term nurture or remove from active sequence

Try This Today​

Here's your action item:

1. Check your calendar for tomorrow's meetings
2. Run the Meeting Brief Batch prompt from Step 1 above for every meeting tomorrow
3. Add MarketBetter website visit data to each brief
4. Review each brief for 3 minutes before the meeting
5. After each meeting, note whether the prep helped and what you wish you'd known

Track your results for a week. Most SDRs report that AI-prepped meetings convert at a significantly higher rate than unprepared ones — because the prospect can tell you've done your homework, and they respect your time because you respect theirs.

---

This is Part 8 (🔴 Advanced) of our 10-part series. Next up: Part 9: Never Let a Lead Go Cold →

MarketBetter shows you exactly which pages your prospects visited before the meeting. Walk in knowing what they care about. Book a demo.

🔴 Series Difficulty: ADVANCED (Part 9 of 10) — The most sophisticated workflow in the series. Combines signal detection, research, scoring, and personalized outreach.

Here's a stat that should keep every SDR up at night: 80% of sales require 5+ follow-ups after the initial contact, but 44% of salespeople give up after just one follow-up.

You've lived this. A prospect seemed interested. Maybe they responded to your first email. Maybe they even took a meeting. Then... silence. You sent a follow-up. Nothing. Another follow-up. Crickets. So you moved on to the next lead, and that once-promising prospect became another cold record in your CRM.

But here's the thing: cold leads aren't dead leads. They're leads who weren't ready at the time. Their priorities shifted. Their budget got frozen. Their champion left the company. A competitor swooped in. Whatever the reason, "not now" doesn't mean "not ever."

The SDRs who crush their quota know this. They're not just great at opening new conversations — they're relentless at re-opening old ones. And in Part 9 of our Claude Code + MarketBetter series, we'll show you exactly how to build an AI-powered system that ensures no lead ever truly goes cold.

Why this is the most advanced post in the series: This workflow pulls together nearly everything you've learned. You'll use research skills from Part 2 to understand what's changed with cold leads. Email writing from Part 3 to craft re-engagement messages. Competitive intel from Part 5 to find new angles. Lead scoring from Part 6 to prioritize which cold leads are worth reactivating. And clean CRM data from Part 7 to make sure your records are accurate before you reach out. This is where all the pieces come together — the dress rehearsal before the full playbook in Part 10.

Why Leads Go Cold (And Why That's Okay)​

Understanding why a lead went cold is the first step to bringing them back. Here are the most common reasons:

1. Bad timing — They weren't actively buying when you reached out
2. Changed priorities — An internal project took precedence
3. Budget freeze — End of quarter/year budget cuts
4. Champion left — Your internal advocate changed jobs
5. Competitor won — They went with someone else (this one's not permanent either)
6. Lost in the noise — Your follow-ups got buried and they forgot about you
7. Content gap — You didn't provide enough value to stay top of mind

Notice that most of these are temporary conditions. Budgets get refreshed. New champions emerge. Competitors disappoint. Priorities shift back. The question isn't whether cold leads come back — it's whether you're watching when they do.

MarketBetter's Secret Weapon: Signal Detection for Cold Leads​

This is where MarketBetter changes the game. While most SDRs rely on hope and manual follow-ups, MarketBetter is actively monitoring your website for returning visitors. Including the leads you wrote off months ago.

Here's what MarketBetter's signal detection does for cold leads:

• Return visitor alerts — A prospect who hasn't visited your site in 3 months suddenly shows up on your pricing page. That's a signal.
• Increased engagement — A cold account that used to visit once starts visiting 3-4 times a week. Something changed.
• New stakeholders — Someone else from a cold account starts browsing your site. Maybe a new champion.
• Page-level intent — Not just "they visited" but "they visited your comparison page vs. [Competitor]." That's a very specific signal.

When MarketBetter detects these return signals, it's like getting a second chance — but only if you move fast and move smart.

The Cold Lead Reactivation Framework​

Step 1: Audit Your Cold Leads​

Start by understanding what you're working with:

"I have a list of 50 leads that went cold in the last 3-6 months. For each one, I have: company name, contact name, title, last interaction date, and the stage where they went cold.

Analyze this list and categorize each lead:

1. High reactivation potential — Company is growing, may have new budget, likely still has the original pain point
2. Medium reactivation potential — Worth a touch but don't expect miracles
3. Low reactivation potential — Company situation has changed significantly (downsizing, acquired, etc.)
4. Champion changed jobs — The person left the company. Research where they went (this might be an even better opportunity)

For each high-potential lead, suggest a reactivation angle based on what's likely changed since we last spoke."

This audit takes Claude Code a few minutes and saves you from wasting time on leads that genuinely won't come back.

Step 2: Research What Changed​

For your high-potential cold leads, you need to understand what's different now:

"Research these 15 high-potential cold leads. For each one, tell me what's changed in the last 3-6 months:

1. Company changes — New funding, leadership changes, acquisitions, expansion, layoffs
2. Industry changes — New regulations, market shifts, competitive landscape changes
3. Technology changes — New tools adopted, tech stack changes
4. Personnel changes — Did my contact get promoted? Did new stakeholders join?
5. Public signals — Recent LinkedIn posts, press mentions, job postings

For each lead, give me a 'reactivation angle' — a specific, relevant reason to reach out that doesn't feel like a generic follow-up."

Step 3: Craft Reactivation Messages​

Generic "just checking in" follow-ups don't work. They signal that you have nothing new to offer. Instead, use Claude Code to write value-led reactivation messages:

The "New Development" Re-engagement:

"Write a re-engagement email to [Name] at [Company]. They went cold 3 months ago. Since then, [specific thing that changed at their company]. Connect this change to our solution without being heavy-handed. Don't reference our old conversation. Make it feel like a new, timely touchpoint."

Example output:

Subject: quick thought on the European expansion

Hi Sarah, I noticed Acme just opened the London office — congrats. When US SaaS companies expand into EMEA, one of the tricky parts is maintaining outbound quality in a new market where your brand doesn't have the recognition it does at home.

We've been helping a few companies in a similar stage solve this — essentially getting new-market outbound performing at domestic levels within 60 days instead of 6 months. Thought it might be relevant to what you're building over there.

Worth a conversation?

Notice: no mention of the old conversation, no "just following up," no desperation. It reads like a fresh, relevant outreach based on a current event.

The "Competitor Disappointment" Re-engagement:

"Write a re-engagement email to [Name] at [Company]. They went with [Competitor] 6 months ago. Based on recent G2 reviews, [Competitor]'s customers are reporting [specific issue]. Write a helpful, non-salesy email that addresses this topic without directly suggesting they should switch."

The "New Stakeholder" Re-engagement:

"[Company] went cold 4 months ago. My contact was [Original Name]. MarketBetter shows someone new from the company — possibly [New Name/Title] — visiting our site. Write an email to the new person that introduces our solution fresh, without referencing the old relationship."

The "Value-First" Re-engagement:

"Write a re-engagement email to [Name] that leads with genuine value — an insight, a benchmark, or a relevant trend — with zero sales pitch. The goal is to restart the conversation by being useful. We can sell later. Right now, I just want them to reply."

For more on crafting effective cold emails that get replies, see Part 3 of this series and our standalone guide on how to write cold emails.

Building Multi-Touch Reactivation Sequences​

One email won't reactivate a cold lead. You need a sequence. Here's a proven 5-touch reactivation cadence:

Touch 1 (Day 1) — The Value Lead: Email with a relevant insight, benchmark, or trend. No pitch. Just value.

Touch 2 (Day 3) — The LinkedIn Engage: Like or comment on their recent LinkedIn post. Not a sales comment — a genuine, thoughtful reaction. (Use Claude Code to draft the comment based on their post content.)

Touch 3 (Day 5) — The Resource Share: Share a relevant blog post, case study, or industry report via email. Position it as "thought you'd find this interesting" not "look at our product."

Touch 4 (Day 8) — The Direct Ask: A short, direct email: "I think we could help with [specific challenge]. Worth 15 minutes?"

Touch 5 (Day 12) — The Breakup Email: "I don't want to keep cluttering your inbox. If [specific pain point] isn't a priority right now, totally get it. If it ever becomes one, I'm here."

Use Claude Code to write the entire sequence at once:

"Write a 5-touch reactivation email sequence for [Name] at [Company]. They went cold [X months] ago because [reason if known]. Here's what's changed since then: [new developments].

Sequence:

• Email 1 (Day 1): Value-led, no pitch
• Email 2 (Day 5): Share a relevant resource
• Email 3 (Day 8): Direct but low-pressure ask
• Email 4 (Day 12): Breakup email

Also draft a LinkedIn comment I can leave on one of their recent posts between emails 1 and 2.

Rules: Under 80 words per email. Conversational. No 'just checking in.' Each email should stand alone — they might only see one."

Load this sequence into MarketBetter for automated delivery with smart send timing.

Signal-Triggered Reactivation (The Killer Feature)​

The most powerful reactivation strategy isn't on a schedule — it's signal-triggered. Here's how to set it up:

The Signal → Research → Reach Out Loop​

1. MarketBetter detects a signal: Cold lead returns to your website
2. You research immediately: Ask Claude Code what's changed since they went cold
3. You reach out within the hour: Strike while the signal is hot

"I just got a MarketBetter alert that [Name] from [Company] — a lead that went cold 4 months ago — visited our pricing page and our [feature] page today. Research what's happened at their company since [last interaction date] and draft an immediate outreach email. This needs to feel timely but not stalkerish — don't mention the website visit directly. Use a recent company development as the reason for reaching out."

Why speed matters: When a cold lead returns to your site, there's a window. They're actively thinking about a solution. They might be evaluating you and 2 competitors. The first SDR to reach out with a relevant message has a massive advantage.

Automating Signal Response with MarketBetter​

You don't have to manually watch for return signals all day. MarketBetter can be configured to:

• Send you instant alerts when cold leads return to your website
• Trigger automated sequences based on specific page visits
• Flag return visitors in your daily playbook for immediate action
• Show you the full visit history so you can tailor your approach

For more on signal-based selling and how to act on intent signals, read our signal-based selling guide.

Analyzing Your Cold Lead Pipeline​

Use Claude Code to understand patterns in your cold leads:

"Here's a list of 100 leads that went cold in the last 6 months, including: company, contact, title, when they entered the pipeline, when they went cold, the stage where they stalled, and the reason (if known).

Analyze this data and tell me:

1. Where do leads most commonly go cold? (After first meeting? After proposal? After demo?)
2. When do they go cold? (Time of year, number of days after first contact)
3. Who goes cold? (Certain titles, company sizes, industries more than others)
4. Why do they go cold? (Common reasons if documented)
5. What patterns suggest they'll come back vs. stay cold?
6. Based on these patterns, what should I change about my process to prevent leads from going cold in the first place?"

This analysis often reveals systemic issues. Maybe your follow-up timing is off. Maybe you're losing deals at a specific stage. Maybe certain ICPs just don't convert for you. These insights improve your entire pipeline, not just your reactivation efforts.

The "Champion Changed Jobs" Play​

When your contact leaves the company, most SDRs see it as a loss. Smart SDRs see it as two opportunities:

1. New company opportunity: Your champion knows your product. They might bring it to their new company.
2. Old company opportunity: Someone new took over their role. They might be reevaluating vendors.

"My contact [Name] just left [Old Company] and joined [New Company] as [New Title]. Research:

1. Does [New Company] fit our ICP? (Size, industry, likely needs)
2. Is [Name]'s new role relevant to our product?
3. What is [New Company] currently using for [our category]?
4. Draft a congratulatory email to [Name] that naturally opens a conversation about their new role's needs

Also research who replaced [Name] at [Old Company] and draft an introductory email to them."

This is one of the highest-converting plays in sales. A champion at a new company is essentially a warm lead at a cold account.

Measuring Reactivation Success​

Track these metrics monthly:

• Reactivation rate: % of cold leads that re-engage after your outreach
• Signal-triggered vs. scheduled: Which reactivation method produces more meetings?
• Time to reactivation: How long after going cold do leads typically come back?
• Reactivation-to-pipeline: % of reactivated leads that become active opportunities
• Revenue from reactivated leads: The ultimate metric

Most SDRs find that reactivated leads convert at a higher rate than brand-new cold leads, because there's already some familiarity and trust. The prospect already knows who you are. You're not starting from zero.

Try This Today​

Here's your concrete action item:

1. Pull 10 leads that went cold in the last 3-6 months
2. Feed them to Claude Code with the audit prompt from Step 1
3. Pick the top 3 with highest reactivation potential
4. Research what's changed for each one (Step 2 prompt)
5. Write one reactivation email for each using the "New Development" approach
6. Send them through MarketBetter with a 3-touch follow-up sequence attached

If even one of those three replies, you've just generated pipeline from something you'd otherwise written off. And you did it in less than 30 minutes.

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This is Part 9 (🔴 Advanced) of our 10-part series. Final post: Part 10: The Complete AI SDR Playbook — Putting It All Together →

MarketBetter detects when cold leads come back to life. Don't miss the signal. Book a demo to see return visitor alerts in action.