CLV

An overview of a clv project Jimmy hosang

Summary

CLV projects require multiple actions to deliver their goals. These are: 1.  Engagement + Accessibility 2.  Data + Governance 3.  CLV Model Build + Prescriptive Analytics

1. Engagement + accessibility

Collaboration is key

Engagement with Subject matter experts across marketing, pricing, channel and customer operations is key

to creating accurate datasets

Collaboration breeds support. Support breeds advocacy

Democratize findings. Reveal the process. Show your workings

2. Data + governance

Clv as a hygiene level

Clv is a key performance indicator

Performance indicators – profit, loss

Operational indicators – sales,

retention

Building the data in a hierarchy allows ownership flows

document assumptions, spread ownership, govern work streams

3. Model build + Prescriptive analytics

But before we begin…

Assess the team: we want specialists

sas + Sql+ r + python + BI + Segmentation + survival analysis + soft skills = our analysts

Assess the systems

Proc expand; Do we have sas 9.4?

Do we have enterprise miner? How fast are our servers?

How powerful are our pc’s? Run;

So back to the clv model…

Segmentation: defines what type of customers you have by the similarities between groups

Survival and proportional hazard: predicts likelihood to leave and probability to cancel

Build the model iteratively.

Start simple.

Generate outputs.

Build complexity.

Create feedback loops. A good clv model can take months. Keep engagement high with an iterative approach

And finally… prescriptive analytics

Sensitivity analysis: How does retention rate effect clv? What are the impacts of cross-sell/up-sell? How can we flex operational performance? What is our marketing effectiveness?

Thank you for listening