Jayanth Krishnan provides an article that explains how the revised metrics of CLV2.0, inspired by jobs theory, reveals the limitations of customer lifetime value (CLV) metrics and provides better customer insight, consequently lowering your acquisition cost and creating demand.
CLV is a sound quantitative metric provided the data fed to it has a sound basis in customer’s fundamental approach to making choices. CLV2.0 is a revised metric inspired by Jobs theory, that addresses CLV’s limitations in addition to making growth and margin the lingua-franca of the whole organization.
Recap of the problem
CLV does not help you design or build a great product. It’s great for the nerds to crunch numbers and for C-suite to develop strategy. JTBD thinking is great to figure out customer needs, but it does not tell me which customer is valuable to the business.
We did the research
We went deep and researched the foundations of JTBD. We studied the behavioral psychology assumptions embedded in the theory. We studied the foundational ideas embedded in demand influencing theories and other alphabet soup theories surrounding the simple (but elegant idea) of Jobs.
We went meta. We looked at the philosophy of CLV, the different research streams into CLV including marketing science, finance, management science and what management strategy has to say on the topic. We realized that the foundations of customer lifetime value have long been forgotten in the frenzy that has followed big data/data science and computational prowess of contemporary Machine Learning (ML) methods.
Key points in this article include:
- The problem of biased data
- How JTBD theory addresses demand generation
- Who the formula helps
Read the full article, CLV.2.0 – Generating Demand, not Just Estimating Demand, on LinkedIn.