Advanced Analytics & Decision Science

Revenue & Lifecycle Intelligence

Use modelling and experiments to evaluate pricing, retention and customer lifetime value.

Where the work starts

Understand how revenue is earned, where customers leave and which changes can be tested
Audit historic pricing tests, retention cohorts, and data availability
Agree a plan for modelling, experiments and review

Intended outcomes

Pricing and lifecycle recommendations with measurable impact on revenue and retention
Forecasts and executive dashboards that align finance, marketing, and product
Clear recommendations and walkthroughs so teams can assess and use the findings

Tools in context

Python, R, dbt, Superset, ThoughtSpot, Airflow

Put the next question to work.

Bring the question, the available evidence and the constraints. Speak directly with the practitioner who will do the work.

Talk through an analytics problem