A rep logs into their sales tool. They see every prospect in the database — thousands of rows, none of them theirs. They click the owner filter. Select their name. Hit apply. Do the same thing in Audiences. Repeat tomorrow. And the day after that.

It takes maybe 30 seconds each time. Multiply that by every view, every session, every rep on the team, and you are bleeding 15 minutes per person per day into a filter dropdown. That is over 60 hours per year per rep — gone, not to selling, but to telling the tool who they are.

We fixed it. MarketBetter now defaults Prospects and Audiences to the logged-in rep's own accounts. Open the app, see your pipeline. No clicks, no filters, no hunting.

Clay is a powerful tool. Nobody disputes that. If you enjoy building enrichment workflows inside a spreadsheet, chaining together dozens of columns, managing credit budgets across two separate credit types, and then exporting everything into yet another platform to actually do something with the data — Clay is your playground.

But here is the question nobody at Clay wants you to ask: why are you building enrichment workflows at all?

Phone calls still close deals. Email open rates hover around 20%. LinkedIn InMail response rates are worse. But when an SDR gets a prospect on the phone, conversion rates jump by 5-10x compared to any text-based channel.

The problem has never been whether calling works. It is everything around it: the manual dialing, the tab switching, the note-taking while listening, the CRM logging after, the coaching sessions where managers try to reconstruct what happened from an SDR's memory. By the time all that overhead is handled, your rep has made 15 calls instead of 50.

MarketBetter's dialer eliminates that overhead. Your reps call directly from the browser — no phone hardware, no third-party dialer tab, no copy-pasting numbers. And after every conversation, AI analyzes the recording automatically: full transcript, sentiment score, key points, objections raised, and recommended next steps. The call goes from "something that happened" to structured, actionable data in your pipeline.

Every B2B sales team has experienced the same frustration: you know exactly who you want to sell to, but translating that knowledge into a prospect list takes forever.

You open your data tool. You set filters — industry, employee count, revenue range, job title keywords. You run the search. Half the results are wrong. The CFO you wanted is actually a "Chief Fun Officer" at a 3-person startup. The healthcare companies include veterinary clinics. The 50-200 employee filter caught a company that had 200 employees three years ago but now has 12.

Traditional B2B search forces you to describe your ideal customer through rigid filters that were never designed to capture nuance. MarketBetter's integration with Exa changes that entirely. You describe who you want in plain English, and the system finds them.

You found the right person. Right company, right title, right timing. Then you go to reach out and realize you have no email, no phone number, and a LinkedIn connection request that will sit in limbo for two weeks.

Contact enrichment should not be a separate workflow. It should happen where you already work — inside the same platform where you build audiences, run sequences, and track signals. That is exactly how MarketBetter's Lusha integration works.

A recent analysis by Brendan Short at The Signal Club broke down the go-to-market tech stacks of the 63 fastest-growing private B2B companies — Stripe, Anthropic, Databricks, Canva, Rippling, Ramp, Deel, OpenAI, and more — across 60 tools and 21 categories.

The headline that should keep every AI SDR vendor awake at night: zero of these companies use an off-the-shelf AI SDR product. Not 11x. Not Artisan. Not AiSDR. Zero.

The companies with the most sophisticated go-to-market operations on the planet looked at the AI SDR category and said "no thanks." That is not a coincidence. It is a signal.

There is a pattern we see in almost every B2B company that comes to us for help with outbound. They are spending $5K to $15K per month on GTM tools. They have somewhere between 12 and 25 active subscriptions. And their pipeline per dollar spent is worse than it was when they had three tools and a spreadsheet.

The problem is not the tools. The problem is that most companies buy tools for the company they want to be, not the company they are right now.

A B2B sales leader recently told us they were spending $8,000 per month with 11x.ai to email 10,000 contacts. Their reaction: "For just sending emails, this feels like a super expensive value proposition."

They are not wrong. At $8,000/month, 11x.ai's cost works out to $0.80 per contact or roughly $1.60 per email when you factor in the platform's standard 5-touchpoint sequences. For context, that is 50-100x the cost of sending the same email through a traditional email automation platform.

The question is not whether 11x.ai sends emails. It does. The question is whether what it does beyond sending emails justifies that price tag — and whether there are platforms that deliver more for less.

Most B2B teams know they should be scoring their leads. Few actually do it well. According to Gartner, only 25-30% of B2B companies have a functioning lead scoring model — even though the data consistently shows that teams with scoring see 30% higher close rates and significantly shorter sales cycles.

The reason is not that scoring is conceptually hard. It is that most guides on the topic assume you have a data science team, a mature data warehouse, and six months to build a predictive model. The reality for most growing B2B teams: you have a CRM, some intent data, and you need something working by Friday.

This guide gives you exactly that. A practical scoring framework you can build in a spreadsheet, validate against your own pipeline data, and deploy into your daily SDR workflow — all without writing a single line of Python.

Last week, Joe Chernov and Bill Binch at Battery Ventures published their AI-Forward GTM Tech Stack — a framework mapping the modern go-to-market technology landscape. If you work in revenue operations, sales leadership, or marketing, you should read it. It is one of the most honest assessments of how fundamentally the GTM stack is shifting.

But frameworks from investors necessarily look different from frameworks built by the people actually running revenue. Battery sees categories. Revenue teams see workflows. And the gap between those two views is where most GTM teams are struggling right now.

Here is what the framework nails, what it misses, and what it actually means for teams building pipeline today.