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.

2. SaaS
Customers
SaaS Subscription Hierarchy

3. SaaS
Customers
SaaS Subscription Hierarchy
Churn is Expensive

4. SaaS
Customers
The startup company that I consulted for provides an analytics
platform for customer retention to SaaS companies

5. SaaS
Customers
0 0
Deliverable: Metric that identifies which
customers to contact first.
+
Happy Unhappy Very Unhappy

6. 8 GB
100+ companies
100+ tables

7. 8 GB
100+ companies
100+ tables
Subscription price, period,
quantity, renewal history

8. 8 GB
100+ companies
100+ tables
Subscription price, period,
quantity, renewal history
Frequencies
Contents

9. 8 GB
100+ companies
100+ tables
Subscription price, period,
quantity, renewal history
Frequencies
Contents

10. 8 GB
100+ companies
100+ tables
Subscription price, period,
quantity, renewal history
Frequencies
Contents
Random
Forest
Classifier
(Scikit-learn)
Churn? Yes or No

12. 81%
19%
True Non-Churn
True Churn
Hypothetical Total Number of
Customers = 10000

13. ?
RF gives out feature importance scores of
individual companies
Random
Forest
Classifier
(Scikit-learn)
?

14. ? Random
Forest
Classifier
(Scikit-learn)
?
Churn?
RF gives out feature importance scores of
individual companies

15. → →
False Positive
False Negative
Predicted
ChurnNon-Churn
True
Non-ChurnChurn
Confusion Matrix
928 49
45073
Precision-Recall Curve
Precision
Recall
All customer numbers are hypothetical (scaled).

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

20. •
•
•
•
•

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

22. SaaS
1
SaaS
2
….
SaaS
4
amount_per
_day
client_note_
total_count
last_sub_
duration
sentiment
sub_
duration
churn?
Customer 1 1 0 …. 0 $10 1 365 -0.1 1000 Yes
Customer 2 1 0 …. 0 $9 2 365 0.2 1230 No
Customer 3 0 1 …. 0 $12 4 700 -0.4 700 Yes
Customer 4 0 0 …. 1 $20 0.2 500 0 780 No
⁞ ⁞ ⁞ ⁞ ⁞
Customer
10000
0 1 …. 0 $10 0.1 180 0.5 180 No
* Hypothetical numbers are used.

23. SaaS companies showing the large numbers of
churn cases

24. customers
(70%)
(30%)
Train set Validation set