They keep opening pricing and never pick a plan.

Connected

Repeat pricing views, time on page, plan-toggle events, and return visits.

Example eligibility rule

Trigger after at least three pricing-page visits across two or more sessions in 14 days, or after two visits with a seat-count change and plan comparison, with no plan selected. Exclude current customers checking an invoice.

Three, four, five visits to /pricing. No plan selected. The default comparison table does not answer the question they are stuck on, usually "which one is for us" or "what happens if we outgrow it".

Avoid this assumption

A popup that says "wait, 20% off" is not direction. It is pressure. Direction looks like a recommended plan with a reason.

Repeat pricing visits are a loud "help me choose" signal. A calculator, a recommended plan, or social proof for their size is a different experience that can be tested cleanly.

Reach

How many eligible people can you reach in the chosen channel and time period?

Cost

What will content, delivery, QA and ongoing maintenance cost?

Return

What incremental outcome would cover that cost, and what is it worth?

Planning inputs, not measured results. Estimate reach × incremental outcome rate × value per outcome, then subtract cost. Use a range and a consistent time period.

Example format: Recommendations

A recommended plan based on company size or stated seats, with an explanation.

Compare lighter and deeper responses

Choose the lightest response that solves the need.

Assist: Offer useful help

No modal. A quiet "not sure? compare in two questions" link.

Adapt: Change a meaningful part

A recommended plan based on company size or stated seats, with an explanation.

Transform: Tailor the journey

Interactive chooser plus a human "talk to us" for edge cases, still tested against the table.

What to watch out for

• Leading with a discount because you can, not because they asked.
• Recommending the most expensive plan by default.
• No way to see all plans after a recommendation.

Control

Static pricing table.

Treatment

Chooser or recommended-plan module for repeat visitors.

Success measure

Plan select / trial start from pricing.

Guardrail

Discount-seeking and support tickets about "wrong plan".

Among visitors who met the pricing-consideration rule, compare the chooser with the static table. Measure plan selection and qualified trial or demo starts, then inspect recommendation overrides and wrong-plan support contacts.

Win or lose, record what worked, for whom and why. What you learn becomes the next signal.

• Mutiny B2B website personalization tool that identifies visiting companies (via IP/firmographic match) and swaps page content (headlines, proof, CTAs) by account, segment, or industry. As of April 2026 it relaunched as an AI GTM agent, expanding beyond the original website-personalization product.
• Demandbase Personalization ABM platform module that identifies visiting accounts via AccountID (firmographic, intent, technographic signals) and swaps page copy, images, CTAs, and links for those accounts, plus auto-fills company data on forms.
• Optimizely Personalization Delivers real-time, audience-targeted experiences inside the same platform as Optimizely's A/B testing and feature-flagging tools, with audiences built from behavioral, CRM, and Optimizely Data Platform signals.
• Kameleoon Audiences Experimentation platform with an AI Predictive Targeting feature that scores each visitor's likelihood to convert (0-100) and lets you target or exclude by that score.

They keep coming back to the same product. They still won't buy. Answer what's stopping them. Don't just say buy again.

The logos and quotes are for a different kind of company. Put proof from companies like theirs on the first screen.