All playbooks

Turn pricing assumptions into a testable strategy.

Define the product, segment, and pricing model. ML Clever structures the tiers, competitive context, and experiment plan.

Move from packaging question to pricing decision.

Use one playbook to define the offer, ground it in context, and plan how the recommendation will be tested.

Pricing tiers designed with ML Clever

Refresh the packaging

Clarify the value metric, feature boundaries, and differentiation across tiers.

Pricing evidence organized in an ML Clever document

Ground the recommendation

Bring competitor benchmarks and buyer context into the rationale behind the model.

A pricing experiment dashboard in ML Clever

Plan the experiment

Define the hypothesis, rollout, success metrics, and decision rules before the test begins.

Build the model, the rationale, and the test plan together.

Design tiers around a clear value metric.

Structure tier names, package boundaries, price points, and upgrade logic around the value customers receive.

Pricing and packaging tiers created with ML Clever

Add competitive and buyer context.

Compare market benchmarks, alternatives, and willingness-to-pay signals without separating evidence from the recommendation.

Competitive pricing research in an ML Clever document

Make the recommendation testable.

Create an experiment plan with rollout steps, metrics, guardrails, and decision criteria.

Pricing experiment metrics displayed in ML Clever

Before you run the pricing strategy playbook.

Yes. It develops pricing tiers and packaging together so the value metric and feature boundaries stay aligned.

Yes. Set the pricing-model variable to usage-based, seat-based, flat-rate, hybrid, or another model.

Yes. Add competitor research or ask ML Clever to gather current benchmarks as part of the analysis.

Build a pricing strategy you can test.