I built the instrument that closes the diagnostic gap.

I am not a salesperson. That matters here.

I am Wilton Blake. Seventeen years inside B2B revenue engines, working in content, sales enablement, and pipeline operations across early-stage startups, growth-stage SaaS, and enterprise platforms managing more than 1,500 franchise brands.

DecisionScope came out of a question I could not stop asking.

I spent years producing content for revenue teams. The demos. The decks. The case studies. The campaigns. I watched the same pattern across every company I worked with. The content was good. The campaigns hit their numbers. Pipeline filled.

Then conversion stayed flat.

Win rates did not move. Qualified deals died before anyone said no, and nobody could explain why. The marketing team blamed sales. The sales team blamed leads. The CRO blamed the market. Everyone had a theory. None of them survived contact with the data.

What I noticed, sitting one layer above the deal, was that the content was not the problem.

The buyer was not ready.

The collateral was being asked to do work it could not do, because the buyer had not yet decided they had a problem worth solving. Or could not articulate what success looked like. Or did not have the room internally to move.

None of that was a content problem. None of it was a sales problem either, in the way sales teams talk about sales problems. It was a readiness problem, and nobody was diagnosing it.

I was not trained on a sales framework. I came to this from the outside.

That is part of why DecisionScope looks different from BANT or MEDDIC or Challenger. Those are seller-side qualification tools written by people who carried a quota. They tell a rep how to advance a buyer who is already in motion. They have nothing to say about the buyer who is not.

I built the dimensions to measure that earlier moment.

Problem Conviction. Evaluation Clarity. Outcome Confidence. Organizational Readiness. Each one names a thing that has to be true before a buyer will move. If any one of them is missing, the deal stalls. You can usually feel which one. The diagnostic just makes you say it out loud.

The research validates what I was already seeing in the field.

Dixon and McKenna (Harvard Business Review, 2022) put a number on the pattern: 56 percent of inaction losses come from buyer indecision, not status quo preference. Eighty-seven percent of opportunities contain moderate-to-high indecision. Ebsta and Pavilion (2024) confirmed it independently, finding 61 percent of lost deals attributed to buyer indecision.

None of that generated the dimensions. It gave the pattern a name.

The dimensions themselves stand on older and broader research.

Indecision decomposes cleanly into three uncertainty types in the academic literature: valuation, information, and outcome (Germeijs and De Boeck, 2003). Status quo bias, formally identified in Samuelson and Zeckhauser (1988) and replicated across health-plan and retirement decisions, has thirteen documented countermeasures across cognitive, rational, and psychological dimensions.

Loss aversion, the endowment effect, and status quo bias share an underlying asymmetry of value (Kahneman, Knetsch, and Thaler, 1991).

When buyers cannot verify outcomes, trust does the work that evidence cannot. That is well established in the procurement literature.

The Organizational Readiness dimension exists because buying is a multi-stakeholder process, not an individual transaction.

Webster and Wind's buying center model from 1972 is still cited in current research. The current data is harder. Forrester's 2026 figure puts the average B2B buying group at thirteen internal stakeholders and nine external influencers. Gartner has shown that 74 percent of these groups demonstrate unhealthy conflict during decisions, and that groups achieving consensus are 2.5 times more likely to report a high-quality deal.

Win rates peak at four to five stakeholders and decline past six.

None of that is mine. I built the dimension on it.

What I added was the diagnostic.

The four dimensions are mine. The protocols that resolve each one are mine. The check measures observable behavior, not seller optimism. Every question is calibrated against what the buyer said or did, not what you hope they meant. Same answers, same score, every time. No AI guessing at your situation.

That is the work. Diagnosing why qualified deals stall. Separating the deals dying from indecision from the ones you are actually losing to competitors. Prescribing different fixes for each.

If your pipeline activity does not match your win rate, that is the gap I built this for.