The document discusses an analytics platform developed by a startup company to help SaaS companies reduce customer churn. It describes how the platform uses a random forest classifier with customer data to predict churn and identify the most important customer attributes and segments. Metrics are proposed to determine which customers to prioritize contacting based on their predicted probability of renewal and potential change in satisfaction.
16. Metric: Sign of change in the satisfaction level of a customer
when contacted today.
0 3 6
Number of Communications per Week
Emma
1.0
0.8
0.6
0.4
0.2
0
ProbabilityforRenewal
9 12 15 18 21
17. Metric: Sign of change in the satisfaction level of a customer
when contacted today.
0 3 6
Number of Communications per Week
Emma
1.0
0.8
0.6
0.4
0.2
0
ProbabilityforRenewal
9 12 15 18 21
18. 0 3 6
Number of Communications per Week
Customer A
Customer B
1.0
0.8
0.6
0.4
0.2
0
ProbabilityforRenewal
9 12 15 18 21
Metric: Sign of change in the satisfaction level of a customer
when contacted today.
Emma
Olivia
19. Metric: Sign of change in the satisfaction level of a customer
when contacted today.
0 3 6
Number of Communications per Week
Emma
Olivia
1.0
0.8
0.6
0.4
0.2
0
ProbabilityforRenewal
9 12 15 18 21
21. Startup Co.
(SaaS)
SaaS
1
SaaS
2
SaaS
3
SaaS
4
Companies showing high feature
importance scores
SaaS 1: Database
SaaS 2: Web design
SaaS 3: Advertisement
SaaS 4: Analytics platform / dashboard
SaaS
Group
1
Group
2
Group
3
Group
4
Customer segments showing high feature
importance scores
Group 1: Age > 40, Asian
Group 2: Age < 40, Asian
Group 3: Age > 40, Non-Asian
Group 4: Age < 40, Non-Asian