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Analytics6 min

Dynamic Pricing

Dynamic Pricing is the pricing-intelligence module of the Analytics track. Whether you're setting a price for the first time or optimizing an existing one, it gives you seven rigorous methods — from AI-generated strategies to profit/revenue optimization, willingness-to-pay studies, churn-aware lifetime value, segment elasticities, conjoint feature valuation, and live price experiments — each explained in plain language with charts and projections.

At a glance

Level
Beginner
Duration
6 min
Product
Intelligence Navigator
Prerequisites
A business profile; a dataset for data-driven methods
Audience
Owners, founders, firms, technical & non-technical teams
Subjects
Pricing strategiesoptimizationwillingness to payLTVelasticitiesconjointexperiments

Seven pricing methods, one workspace

Dynamic Pricing organizes everything into seven categories, each built for a different situation — some need no data at all, others run on your transaction history or survey responses. Pick the method that fits where you are, and the module guides you through what to map.

  • Pricing Strategies — AI-generated strategy recommendations (value-based, freemium, tiered, usage-based, subscription) from your company profile — no dataset needed
  • Profit & Revenue Optimization — data-driven optimal price from historical transactions, competitor positioning, and market intelligence
  • Willingness to Pay — Van Westendorp and Gabor-Granger survey analysis for new products with no transaction history

See Dynamic Pricing in action

Watch: pick a method, map your fields, and get an optimal price with revenue projections.

Optimize for profit, revenue, or lifetime value

The optimization method finds the price that maximizes profit or revenue from your actual transactions — map price, quantity, date, product, and an optional cost column for margin analysis — and projects revenue at the recommended point with an Intelligence Score across six pricing principles. The LTV method goes further: a churn model shows how price affects retention, balancing a higher price against higher churn to find the price that maximizes customer lifetime value.

  • Optimal price with a revenue projection at the recommended point
  • Retention-adjusted LTV curve — the price that maximizes lifetime value, not just the next sale
  • Segment elasticities — per-segment price sensitivity with stable estimates even for small segments

Value your features and design your tiers

Conjoint and MaxDiff analysis tells you which features customers actually value and how much each is worth in dollars — so you know what belongs in Pro versus Enterprise, what to charge for an add-on, and how to rank the roadmap by willingness to pay.

Test prices with real experiments

Run live A/B price tests with statistical rigor. Enter each arm's visitors, conversions, and price — no dataset required — and the module reports conversion and revenue-per-visitor lift, a significance test against control, and a Thompson-sampling bandit that shifts traffic toward the winning price automatically. Then automate the follow-up: Sentinel Intel can watch the metric and AI Operations Center can run the workflow on a schedule.

Best practices

  • Use Willingness to Pay for new products with no sales history; use Optimization once you have real transactions
  • Map a cost column in optimization so the recommendation maximizes margin, not just revenue
  • Prefer the LTV method for subscriptions — the cheapest churn-beating price usually beats the highest sticker price
  • Validate a recommended price with a live experiment before rolling it out broadly

Next steps

Once your prices are optimized, Forecast shows where revenue is headed — next in the Analytics track.