In today’s fiercely competitive market, sustaining growth and profitability hinges on adeptly retaining customers. Research indicates that acquiring a new customer can be up to five times more costly than retaining an existing one. Businesses often struggle with retaining customers, leading to increased churn rates and decreased revenue. By leveraging the power of advanced analytics and insightful exploration, businesses can navigate the challenges of rising churn rates and data fragmentation, offering actionable solutions to elevate customer engagement strategies.

The Cost of Acquisition vs. Retention

Understanding the financial implications of customer acquisition versus retention is crucial. Studies show that:

• Customer churn costs U.S. providers $168 billion per year (CallMiner).
• U.S. companies could save over $35 billion annually by focusing on keeping existing customers happy (CallMiner).
• In 2013, e-commerce businesses paid an average of $9 to acquire a new customer. By 2022, this had risen to $29 (a 222% increase).
• Customer retention is vital for the 61% of small businesses that report over half their revenue comes from repeat customers.

Predicting customer behavior and implementing strategic retention measures can significantly enhance revenue stability and customer satisfaction.

The Relevance of Predictive Analytics

In the current retail paradigm, accurately forecasting repeat purchases is essential for cultivating customer loyalty and maintaining competitiveness amidst rapid e-commerce evolution. Sophisticated data analysis techniques and a proactive approach to understanding dynamic consumer behaviors are vital. Loyal customers are worth up to 10 times their initial purchase value over their lifetime (White House Office of Consumer Affairs).

Changing Consumer Behavior

With the continual evolution of market dynamics and intensifying competition, comprehending and predicting customer purchase behavior emerges as indispensable for businesses aiming to retain their competitive edge.

Loyal customers are worth up to 10 times their initial purchase value over their lifetime. (Source: White House Office of Consumer Affairs)

Data and Technological Advancements

Technological advancements have simplified the collection and analysis of customer data, providing invaluable insights into customer behavior and preferences. For instance:

• A 2% increase in customer retention has the same effect as decreasing costs by 10% (Leading on the Edge of Chaos, Emmett Murphy & Mark Murphy).
• Even a 5% increase in customer retention can translate into profit boosts ranging from 25% to 95% in the retail sector.
• Existing customers have a significantly higher likelihood of making purchases, with a 60% to 70% chance compared to just 5% to 20% for new prospects.
• Repeat customers spend about 67% more than new customers.

Emphasizing Customer Retention

Businesses are shifting focus from acquiring new customers to nurturing existing ones, driven by the high costs of customer acquisition and the potential for greater profitability from loyal patrons. Addressing challenges such as rising churn rates, data fragmentation, and forecasting amidst market turbulence requires a strategic blend of data-driven insights, technological prowess, and agile decision-making.

Impact of Solving the Problem

Addressing these challenges engenders a cascade of benefits for businesses, including engagement rates and conversion rates through targeted marketing campaigns grounded in predictive analytics. Embracing predictive and advanced analytics furnishes a competitive edge in the data-driven marketplace, while tailoring products and services to individual preferences begets happier customers and enhanced satisfaction. Enhanced understanding facilitates targeted marketing initiatives, personalized offerings, and superior customer experiences, fostering loyalty and satisfaction. Preemptive identification of at-risk,abou to lose customers ,risks like market fluctuations or supply chain disruptions helps mitigate negative impacts, while building enduring relationships through data-driven strategies yields sustained revenue streams and fortifies the business’s market position.

Architecture Overview 

The data architecture for customer retention can be divided into several layers:

• Data Source: Retail data including customer transactions, product purchases, inventory levels, and sales channels.
• Raw Layer: Initial landing zone for ingested data in Snowflake tables, preserving raw, unprocessed data for access.
• ML Layer: Conducts exploratory data analysis (EDA), feature engineering, model training, and version control.
• Consumption Layer: Interface for users to interact with analytical outputs and models via a Streamlit application.
• Ingestion Layer: Secure transfer and storage of raw data files on Amazon S3.
• Transform Layer: Ensures data quality and prepares data for analysis.
• Analytics Layer: Repository for storing predictions and results from model deployment.

Methodology

The project encompasses comprehensive customer behavior analysis, predictive modeling for new purchases, customer segmentation through data clustering, and the utilization of historical data for insights. Here’s a step-by-step implementation in Dataiku:

1. Data Ingestion: Ingest data from Snowflake or other sources into Dataiku, including product names, order numbers, stock numbers, quantities, unit prices, customer income, customer education, customer IDs, and order dates.
2. Data Preparation: Clean the dataset, handle outliers, and perform feature engineering to categorize products and calculate customer ages.
3. Data Splitting: Perform stratified splitting based on the target variable to ensure representative training data.
4. Grouping Data: Aggregate data by customer ID to derive key metrics such as income, monetary value, recency, and frequency.
5. RFM Analysis: Perform RFM (Recency, Frequency, Monetary) analysis using a Python recipe.
6. Model Development: Develop models for RFM segmentation, behavioral segmentation, and purchase prediction.
7. Model Deployment: Deploy the best-performing models and score predictions on test data.
8. Evaluation: Use evaluation metrics to assess model performance.
9. Visualization: Create visual representations of the RFM model and segmentation results.
10. User Interface: Develop a Streamlit UI within Dataiku for accessible end-user interaction with the models.

Results

• RFM Analysis Segmentation: Segment customers into groups like Champions, At-Risk, Recent, and Loyal customers based on recency, frequency, and monetary values.
• K-Means Clustering: Refine customer segmentation with K-means clustering to understand preferences and purchase patterns.
• PCA for Enhanced Segmentation: Use Principal Component Analysis (PCA) for accurate segmentation.
• Predictive Modeling: Predict customer repeat purchase probability with high accuracy using Pycaret and LightGBM.
• Personalized Discount Strategies: Develop personalized discount and campaign recommendations tailored to each customer segment.

Step-by-Step Implementation in Dataiku

1. Data Ingestion

We start by ingesting data from Snowflake or any other data source into Dataiku. The dataset includes:

• Product names
• Order number
• Stock number
• Quantity
• Unit price
• Customer income
• Customer education
• Customer ID
• Order date
• Birth Year

2. Data Preparation

Data preparation is a critical step to ensure the dataset is clean and ready for analysis. Using Dataiku’s Prepare recipe, we perform the following tasks:

• Cleaning and Outlier Handling: Address negative values in unit price and quantity, which occur due to order cancellations. Since these records constitute only 1.72% of the data, they are removed to maintain data integrity.
• Feature Engineering:
  • Product Categorization: With 3000 distinct product names, deriving insights is challenging. We use zero-shot classification and word cloud techniques to categorize products into 10 groups. However, due to imbalance (65% of products in one category), we further categorize products into two groups: home accessories and other products.
  • Age Calculation: Derive the age of customers from their birth year.

3. Data Splitting

We use a Split recipe to perform stratified splitting based on the target variable, ensuring the training data is representative. This split data is then used for calculating the next purchase prediction.

4. Grouping Data

Using the Group recipe, we aggregate data based on customer ID to derive key metrics:

• Income: Most recent income of the customer.
• Monetary Value: Total spending, calculated from quantity and unit price.
• Recency: Time since the last purchase, derived from the order date.
• Frequency: Number of purchases made by the customer.

5. RFM Analysis

We perform RFM (Recency, Frequency, Monetary) analysis on the training data using a Python recipe. This analysis helps in understanding customer value and behavior, which is essential for effective segmentation.

6. Model Development

We develop three models to address different aspects of customer analysis:

1. Model 1: RFM Segmentation
   • Segments customers based on recency, frequency, and monetary value.
   • Customers are grouped into 4 categories:
     1. Recent Customers
     2. Loyal Customers
     3. Champions
     4. At Risk Customers
2. Model 2: Behavioral Segmentation
   • Segments customers using all available customer personal information.
   • Customers are grouped into 4 categories:
     1. High Income, Champions/Loyal Group
     2. Moderate Spenders, High Loyal and At Risk customers
     3. Education Focused, Varied Spending Customers
3. Model 3: Purchase Prediction
   • Predicts whether a customer will make a repeat purchase in the next three months.

7. Model Deployment

We deploy the three best-performing models and use them to score predictions on the test data.

8. Evaluation

Using the Evaluate recipe, we obtain metrics to understand the performance of each model. This includes assessing accuracy, precision, recall, and other relevant metrics.

9. Visualization

We create visual representations of the RFM model and the segmentation model to analyze trends in both the training and test data. These visualizations help in validating model performance and understanding customer segments better.

10. User Interface

To make the solution accessible to end-users, we develop a Streamlit UI within Dataiku. The UI includes:

• Inference option: Allows users to input data and get predictions.
• Bulk inference option: Enables batch processing of multiple records for prediction.

Conclusion

By following this structured approach in Dataiku, businesses can effectively segment their customers and predict future purchasing behaviors. This enables targeted marketing strategies, improved customer retention, and ultimately, increased sales.

Dataiku provides a robust platform for this implementation, allowing for seamless data integration, preparation, analysis, and deployment. With the addition of a user-friendly UI, stakeholders can easily interact with the models and gain actionable insights.

Addressing customer retention challenges through predictive analytics and data segmentation in Dataiku offers a strategic advantage. By focusing on customer behavior, businesses can:

• Improve targeted marketing campaigns.
• Enhance customer engagement and satisfaction.
• Optimize resource allocation.
• Drive long-term profitability.

Implementing customer segmentation and prediction using Dataiku enhances understanding of customer behavior and drives data-driven decision-making in marketing and sales strategies.

Future Enhancements

Future iterations could incorporate real-time data streams, external data sources, advanced recommendation engines, and strategic bundling to further enhance customer retention efforts.

By following this structured approach, businesses can effectively segment their customers, predict future purchasing behaviors, and ultimately, improve customer retention and increase sales.