The Splattr blog
Practical guidance on prospecting strategy, outbound execution, and AI-powered sales workflows.
The homegrown system is incredible. The reps still just want to know who to call and why.
There is a growing gap between the AI systems RevOps builds and what front-line sellers actually use. Here is why it happens and how to close it.
Describe what you sell in vague terms and every account looks the same. Get specific and your priorities sort themselves.
Generic ICP definitions score every account the same. The fix is describing your offer with specific, niche examples so your prospecting priorities become obvious.
Most recruiters work in one direction. Running it backwards finds the accounts that need exactly who you have.
Instead of finding a hiring company and digging for a candidate, start with your strongest candidate and find the companies that need exactly that person right now.
Nobody cares what you know until they know that you care. Cold outreach is no different.
The fastest way to lose a cold prospect is to open with your pitch. Open with what you noticed about them instead, and the odds of a meeting go up.
Every job posting is a snapshot of a company's internal needs. Years of them tell the whole story.
You can reconstruct a company's real tech stack from its job postings. A single posting is a snapshot; years of hiring history reveal how the stack evolved and what matters most.
I sell one, and I still tell people to start somewhere else.
A prospecting tool is for genuinely cold outreach. When you have a warm intro available, use it first. Here is where a tool actually earns its place.
A number you have to trust blindly is not intelligence. It is a guess with a decimal point.
Most AI prospecting tools hand you a score and ask you to trust it. A useful score exposes every signal behind it, each with a date and a source a rep can verify.
Most reps treat their whole list as equally ready. The ones who win read the signals.
A practical guide to B2B buying signals: what they are, the five kinds that matter, and how to turn a signal into the opening line of your outreach.
The person who matches your title filter is usually a gatekeeper. The buyer sits one room over.
The contact who matches your job-title search is often closer to a gatekeeper than a buyer. The person who feels the pain sits in a role you would never think to filter for.
Splattr now supports the Model Context Protocol, so your AI tools can prospect, enrich, and report without switching tabs.
Splattr's MCP integration lets Claude, Cursor, and other AI tools take direct action inside your pipeline: finding prospects, enriching contacts, and generating reports without leaving your workflow.
The same account can be a perfect fit for one offer and a poor one for the next. Static scoring cannot tell the difference.
A prospect's quality is not a fixed property of the account. It depends on the offer. That is why static scoring breaks down and scoring has to run through the offer's lens.
A sales coach put it better than I ever could: you have to say something painful enough to get on their list.
People book a first meeting for one reason: they have a pain and you gave them a reason to believe you can solve it. Here is what that means for how you prospect.
AI handed everyone a bigger shovel. Digging faster only helps if you are standing over something worth digging for.
AI made prospecting faster, which mostly produced more lists and longer sequences. The work that actually changes a quarter happens before any of that, in choosing the right accounts.
Five new marketing roles only means something if you know what normal looks like for that company.
A single signal like a burst of new job postings is easy to misread. Without a company's hiring history for context, you cannot tell routine churn from a real shift.
My first sales job was cold calling for a consulting firm. I failed constantly, and it taught me the one thing that matters.
Cold calling as a college intern was mostly failure, with the occasional accidental win when the timing happened to be right. AI is what makes that timing predictable.
You cannot run deep personalization on a thousand people. The score tells you where the effort is worth it.
A practical model for matching outreach effort to prospect score, from low-effort familiarity at the bottom to fully custom outreach at the top. This is how our own team runs it.
Our biggest competitor is not another vendor. It is a project that never quite reaches done.
Building your own GTM AI system is seductive and rarely finished. Here is why the internal-build path stalls, and who ends up needing a tool most.
Your feed says AI already replaced the sales team. The teams we talk to tell a different story.
The AI sales content on LinkedIn is mostly people selling something. The real conversations with business development teams sound very different. Here is the gap.
When you sell one product to one buyer, an agent is easy to dial in. Services sales is not that world.
Most AI go-to-market tools assume you sell one product to one kind of buyer. Consulting and staffing sell many offerings to many buyers, and generic tools fight them.
The demo is always impressive. The edge cases are where the real story starts.
Agentic AI demos in sales look incredible. Adoption stalls on flexibility and edge cases. The durable approach keeps agents in their lane and humans in the driver's seat.
A rep does not need another research document. They need three answers, fast.
Many agentic prospecting tools flex their sophistication with long research reports. Reps do not need more to read. They need to know who to contact, why, and what to say.
A practical framework for moving from volume outreach to precision outreach.
Learn how agentic prospecting helps sales teams find better-fit accounts, personalize faster, and improve conversion outcomes.