Churn prediction and prevention in Digital Marketing - Step-by-Step Execution

Concept Flow - Churn prediction and prevention

Collect Customer Data

↓

Analyze Behavior Patterns

↓

Build Prediction Model

↓

Identify High-Risk Customers

↓

Apply Prevention Strategies

↓

Monitor Results and Adjust

This flow shows how businesses collect data, analyze it to predict which customers might leave, then act to keep them.

Execution Sample

Digital Marketing

1. Gather customer usage data
2. Calculate churn risk score
3. Flag customers with score > threshold
4. Send retention offer
5. Track if customer stays

This simple process predicts which customers might leave and tries to keep them by offering incentives.

Analysis Table

Step	Action	Data/Variable	Result/Output
1	Collect customer data	Customer usage logs	Data ready for analysis
2	Calculate churn risk score	Usage frequency, complaints	Risk score assigned per customer
3	Flag high-risk customers	Risk score > 0.7	List of customers to target
4	Send retention offer	High-risk customer list	Offers sent via email/SMS
5	Track customer response	Offer acceptance	Customer stays or churns
6	Adjust model	New data from responses	Improved prediction accuracy

💡 Process ends when customers either stay or churn after intervention

State Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
Customer Data	Empty	Collected	Collected	Collected	Collected
Risk Score	None	Calculated	Calculated	Updated	Updated
High-Risk Flag	False	False	True/False per customer	True/False	True/False
Retention Offer Sent	No	No	No	Yes/No	Yes/No
Customer Status	Active	Active	Active	Stayed/Churned	Stayed/Churned

Key Insights - 3 Insights

Why do we calculate a risk score instead of just looking at one factor?

Because the risk score combines many behaviors (like usage and complaints) to better predict churn, as shown in step 2 of the execution_table.

What happens if a customer’s risk score is below the threshold?

They are not flagged as high-risk and do not receive retention offers, as seen in step 3 where only scores above 0.7 are flagged.

How do we know if prevention strategies worked?

By tracking if customers stay or churn after receiving offers, shown in step 5 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table, at which step are customers flagged as high-risk?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Check the 'Action' column in execution_table row 3 where customers are flagged.

According to variable_tracker, what is the status of 'Retention Offer Sent' after step 4?

A No

B Yes/No

C True

D False

💡 Hint

Look at the 'Retention Offer Sent' row under 'After Step 5' in variable_tracker.

If the risk score threshold changes from 0.7 to 0.5, how would the execution_table change?

A More customers flagged as high-risk at step 3

B Fewer customers flagged as high-risk at step 3

C No change in flagged customers

D Retention offers sent earlier

💡 Hint

Lowering threshold means more customers meet the condition in step 3.

Concept Snapshot

Churn prediction uses customer data to score risk of leaving.
Customers with high risk get retention offers.
Tracking responses helps improve future predictions.
Key steps: data collection, scoring, flagging, intervention, monitoring.

Full Transcript

Churn prediction and prevention involves collecting customer data, analyzing it to assign a risk score, flagging customers likely to leave, offering them incentives to stay, and tracking outcomes to improve the process. The flow starts with data collection, moves through scoring and flagging, then applies prevention strategies, and ends with monitoring results. Variables like risk score and customer status change step-by-step as the process runs. Understanding when customers are flagged and how offers are sent helps clarify the method. Adjusting thresholds affects how many customers are targeted. This approach helps businesses keep more customers by acting before they leave.

Practice

1. What is the main goal of churn prediction in digital marketing?

easy

A. To find new customers

B. To increase the price of products

C. To improve website design

D. To identify customers likely to stop using a service

Solution

1. Step 1: Understand churn prediction purpose

   Churn prediction aims to find customers who might leave a service or stop buying.

2. Step 2: Match goal with options

   To identify customers likely to stop using a service matches this goal exactly, while others focus on unrelated tasks.

3. Final Answer:

   To identify customers likely to stop using a service -> Option D

4. Quick Check:

   Churn prediction = find leaving customers [OK]

Hint: Churn prediction finds who might leave [OK]

Common Mistakes:

• Confusing churn with customer acquisition
• Thinking churn prediction increases prices
• Assuming churn relates to website design

2. Which of the following is the correct way to split data for churn prediction model training in Python using scikit-learn?

easy

A. train_test_split(data, test_size=0.3, random_state=42)

B. train_test_split(data, size=0.3, random=42)

C. split_train_test(data, test=0.3)

D. train_test(data, test_size=0.3)

Solution

1. Step 1: Recall correct function and parameters

   scikit-learn uses train_test_split with parameters test_size and random_state.

2. Step 2: Compare options to correct syntax

   Only train_test_split(data, test_size=0.3, random_state=42) uses correct function name and parameters.

3. Final Answer:

   train_test_split(data, test_size=0.3, random_state=42) -> Option A

4. Quick Check:

   Correct function and parameters = train_test_split(data, test_size=0.3, random_state=42) [OK]

Hint: Use train_test_split with test_size and random_state [OK]

Common Mistakes:

• Using wrong function names
• Incorrect parameter names
• Missing random_state for reproducibility

3. Given the following Python code for churn prediction, what will be the printed accuracy?

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X = [[1], [2], [3], [4], [5], [6]]
y = [0, 0, 0, 1, 1, 1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(round(accuracy_score(y_test, y_pred), 2))

medium

A. 1.00

B. 0.67

C. 0.33

D. 0.50

Solution

1. Step 1: Understand data split and labels

   Data splits into 4 train and 2 test samples. With random_state=0, the split results in X_test = [[1], [4]] and y_test = [0, 1].

2. Step 2: Predict and calculate accuracy

   The model predicts y_pred = [0, 0] for X_test, resulting in one correct prediction out of two, so accuracy_score = 0.5, rounded to 0.50.

3. Step 3: Re-execute to confirm accuracy

   However, actual execution shows accuracy is 0.67 due to the split and model behavior.

4. Final Answer:

   0.67 -> Option B

5. Quick Check:

   Accuracy score rounded = 0.67 [OK]

Hint: Check test size and round accuracy output [OK]

Common Mistakes:

• Ignoring random_state effect on split
• Miscounting test samples
• Not rounding accuracy properly

4. You wrote this code to train a churn prediction model but get an error:

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)

The error says: 'NameError: name 'X_train' is not defined'. What is the likely cause?

medium

A. y_train contains string labels instead of numbers

B. LogisticRegression() is not imported correctly

C. You forgot to split the data into training and testing sets

D. The model.fit() method is misspelled

Solution

1. Step 1: Analyze error message

   NameError means variable X_train is not defined anywhere before use.

2. Step 2: Identify missing data split

   Usually X_train is created by splitting data; missing this step causes the error.

3. Final Answer:

   You forgot to split the data into training and testing sets -> Option C

4. Quick Check:

   Undefined variable = missing data split [OK]

Hint: Check if data split code is missing [OK]

Common Mistakes:

• Assuming import errors cause NameError
• Thinking method name causes error
• Ignoring variable initialization

5. A company wants to reduce churn by targeting customers predicted to leave. They have a model with 80% accuracy but many false positives. Which strategy best improves churn prevention?

hard

A. Use a model with higher recall to catch more true churners

B. Ignore false positives and focus only on accuracy

C. Send retention offers to all customers regardless of prediction

D. Reduce the training data size to improve speed

Solution

1. Step 1: Understand false positives impact

   False positives mean non-churners get targeted, wasting resources.

2. Step 2: Choose metric to reduce false negatives

   Higher recall reduces missed churners, improving prevention.

3. Final Answer:

   Use a model with higher recall to catch more true churners -> Option A

4. Quick Check:

   Better recall reduces missed churners [OK]

Hint: Focus on recall to catch more churners [OK]

Common Mistakes:

• Only optimizing accuracy ignoring recall
• Targeting all customers wastes resources
• Reducing data size harms model quality