This is an illustrative scenario, not a real client. The brand and the figures are composites, used to show how a Worth failure typically appears and how it gets fixed. They are not the results of a specific audit.
A Worth problem is painful because it strikes at the finish line. The product is right, the customer is sold, and a single wrong number ends the deal. To make the pattern concrete, here is a composite example. For the framework behind it, see the Worth pillar guide.
The Setup
Picture an analytics platform that had recently simplified its pricing, lowering its entry tier and making it far more accessible to mid-market teams. Call it the kind of brand that had just removed its biggest objection. Yet the sales team kept hearing it anyway: prospects would open a call saying "we assumed you were out of our range." The new, lower price did not seem to be landing.
So the team asked an engine directly. "How much does this platform cost, and is it worth it for a mid-market team?" The answer quoted the old, higher entry price and added that the tool was "generally aimed at larger enterprises."
The Diagnosis
In this illustrative example, the Worth picture was poor. Across price and value prompts run logged out, the brand landed in the Marginal band for Worth, even though its Appearance and Showdown were healthy. Three things stood out.
The engine quoted the retired, higher price, because the brand's public pricing page had moved the current numbers behind a "request a quote" form, so the only readable figure left online was an outdated third-party listing. It framed the tool as enterprise-priced, echoing an old comparison article written before the pricing change. And it offered no value story for mid-market teams, because the site described value in broad benefit language rather than concrete outcomes.
None of this appeared in the brand's analytics. The mid-market prospects who were told it was out of range simply never booked a call.
The Work
The fix followed the Worth playbook. The team published a clear, public, text-based pricing page with the current tiers and what each included, so the real number was readable again. They restated their value in concrete outcomes for mid-market teams, the hours saved, the reports replaced, rather than vague benefits. They added an honest ROI framing a model could cite. And they updated the outdated comparison article that had started the "enterprise-priced" narrative, adding a current, citable counter-source.
This was a quarter of focused pricing and value content work, not a pricing change, because the price was already right. The tactics are detailed in How to Improve Your Worth Score.
The Result
In this composite, re-measuring after a quarter showed the brand moving from Marginal into the Strong band for Worth. The engines now quoted the current entry price, described the tool as accessible to mid-market teams, and paired the price with a concrete value story. The "we assumed you were out of our range" objection faded from sales calls.
The lesson generalizes. The brand did not have a pricing problem. It had a readability and value-story problem that AI turned into a wrong, deal-ending answer, and it cost real opportunities until it was found and corrected. Once the error was visible and traced to its sources, the fix was straightforward and the score moved.
See Your Own Picture
The hardest part is knowing what AI tells customers about your price, because the ones who believe a wrong number never book the call. A HiBot audit measures your Worth across engines and ranks what to fix first. You can start by pressure-testing your own pricing with The Pricing-Page Readiness Checklist for AI. Download the ANSWER whitepaper to see the method, or request an AI visibility audit at hibot.com.
