In the process of writing ‘Making Effectiveness Work’ for the IPA, I came across a lot of misleading commentary on the challenges (and benefits) of Bayesian approaches to MMM. Here are 5 of them…

Customer Lifetime Value (CLV) is a fundamental business metric, yet many organisations struggle to measure it accurately. This is often due to simplistic approaches rather than a lack of adequate data. In this post, I outline key principles for measuring and building trust in CLV estimates—an essential step toward informed, customer-centric decision-making.

Accurately measuring customer lifetime value allows companies to grow profitably by acquiring higher value, committed customers at a lower cost; by ensuring the profitable ones stay customers for as long as possible; and by developing your relationship with them to ensure they engage with your brand as much as possible.

The resurgence of interest in MMM presents an opportunity to shake up marketing science, organising measurement around the critical decisions marketers face.

But many providers are burrowing deeper into existing rabbit holes - spending huge amounts of time and money building overly complex, granular models, which produce results that ultimately have a limited impact on decision making.

A better approach is to focus analytics to where it can have the most impact and to organise measurement around business-critical decisions.

How to predict subscriber retention and estimate lifetime value

We collaborated with researchers at the Manchester Centre for Genomic Medicine on a paper looking at the impact of introducing point-of-care genotyping on routine clinical practice.

Is data science becoming a market for lemons?

COVID 19 testing meets Bayes’ theorem.