Customer lifetime value (CLTV) is an estimate of the total revenue a customer will provide to the business and plays a critical role in the strategic decisions of businesses. CLTV forecasts are used to evaluate the effectiveness of marketing campaigns, increase customer loyalty and optimize the profitability of the business. In this article, we will step by step perform CLTV forecasting and customer segmentation using machine learning.
What is CLTV?
Customer Lifetime Value (CLTV) is an estimate of the total revenue a customer will generate over the course of their relationship with the business. CLTV is used to better understand customer relationships and increase customer loyalty.
Important CLTV Components:
Purchasing Frequency: How many times the customer makes purchases in a certain period of time.
Average Order Value: How much the customer spends on average for each purchase.
Customer Lifespan: The duration of the customer's relationship with the business.
Gross Margin: The profit margin obtained from customer expenditures.
CLTV Estimation Project Step by Step
In this project, we will perform CLTV prediction with machine learning models using customer data. Here are the steps we will take:
Step 1: Data Loading and Preparation
First, we will load the datasets and perform the cleaning operations. This step is critical to making the data analyzable.
Load datasets and view the first 5 rows.
Detect and clean missing and outliers.
Step 2: Feature Engineering
We will create the necessary features to predict future customer behavior.
Calculate total spend per customer, average spend, and spend standard deviation.
Add labels for CLTV calculation.
Step 3: Modeling
We will predict future CLTV using machine learning models.
Separate the data set into training and testing sets.
Make predictions with Random Forest regression model.
Evaluate model performance.
Step 4: Analysis and Visualization of Results
We will add the estimated CLTV values as a column and divide all customers into 4 different groups based on their CLTV values. We will also add these group names as a new column.
Add the predicted CLTV values.
Divide customers into groups based on their CLTV values.
Evaluate model performance and analyze results.
Visualization of CLTV Forecasts
We will evaluate the performance of the model and analyze the results.
Comparison of actual and predicted CLTV values.
Visualization of error distribution.
Conclusion
In this project, we tried to predict future CLTV using customer data. We modeled customer behavior by doing feature engineering and made predictions with the Random Forest regression model. By adding the estimated CLTV values, we segmented customers based on their CLTV values and analyzed these segments. Such forecasts are valuable for optimizing marketing strategies and strengthening customer relationships.
CLTV forecasts provide businesses with more in-depth information about their customers, and they can use this information to make strategic decisions. Using customer segmentation, you can develop customized marketing strategies for each segment and increase customer satisfaction.
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