Data-Driven Customer Intelligence



The first things I want to learn as a marketer joining a new company:


1. Who are the most successful customers.

- stay the longest

- spend the most

- use the product frequently


2. Who churns faster

- cohort

- demographic profile

- pain point

- acquisition channels


Usually this information is difficult to get. The data until the sale is readily available.


After the sale, there's product usage, Satisfaction scores and support tickets. Additionally account managers can score customers based on their gut, customers showing up for QBRs.


Here are some problems I have seen.


1. Our Executive Sponsor left the company and we didn't know.

2. Satisfaction scores are high but the customer gave us notice at renewal time

3. There's high usage of the product but the customer still left


I'm one company, a profitable cohort we discovered was that we were very successful with women entrepreneurs 35 -45 years, doing $2m to $5m, been in business for 3 to 5 years in revenue, and wanted to grow fast.


Our ICP until that time had been business owners 55 to 65 years, mostly male, been in business for 10 years or more and revenue of $15m to $20m.


You can't talk to the 2 groups in Marketing, and Sales the same way.


I love the ability of involve.ai to give you the information on customer segments, cohorts, attributes of a churning customer, and identify expansion customer attributes. Early warning - up to 90 days heads-up on churn signals.


When you are using AI to get driving directions every day, why aren't most sales, marketing and success teams using data-driven ways to discover and agree on who the ideal customer is.


If you are a CEO, and asked who is our best customer, the answer from product, sales, marketing and success is likely to be 4 different answers.


Do you agree?

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