Variational Autoencoders for Improved Customer Segmentation

We recently implemented variational autoencoders (VAEs) to improve customer segmentation and targeting efforts.

Customer segmentation is crucial for developing targeted marketing campaigns, product recommendations, and personalized experiences.

However, traditional segmentation methods like K-means clustering can be limited in finding complex patterns in high-dimensional customer data.

This case study explains how variational autoencoders enabled us to develop more nuanced customer segments and drive better marketing performance.

Business Context?

eCommerce company selling various consumer products collects extensive customer data, including purchase history, browsing behavior, demographics, and survey feedback.

We divide customers into segments to create targeted email campaigns, online display ads, and product recommendations tailored to customers' interests.

However, our previous K-means clustering approach led to broad segments that needed to capture the full complexity of customer preferences and traits.

We wanted to implement more sophisticated machine learning to uncover granular customer segments that better reflect real-world behaviors and needs.

Here are a few key points on using variational autoencoders for customer segmentation:

- Variational autoencoders (VAEs) are a deep generative model that can learn complex distributions over high-dimensional data like customer attributes and behavior. This makes them well-suited for clustering customers into segments.

- A VAE consists of an encoder network that compresses the input data into a latent representation and a decoder network that reconstructs the data from the latent representation. The latent space captures important properties of the data.

- For customer segmentation, the VAE can be trained on customer attributes like demographics, purchase history, web activity, etc. The encoder maps each customer to a point in the latent space. Customers with similar attributes will be close together in this space.

- The latent space can be clustered using k-means or Gaussian mixture models to define distinct customer segments. The decoder can be used to understand the key attributes of each cluster.

- VAEs can learn more nuanced segments than traditional methods like k-means on raw data, as the latent space is a compressed representation that captures complex distributions.

- Key advantages of using VAEs include handling mixed attribute types, capturing nonlinear relationships, scalability to large datasets, and the interpretability of the latent space and decoder model.

- Overall, VAEs are a promising unsupervised technique for segmenting customers based on firmographic, behavioral, and transactional attributes. The major challenges are model complexity and training data requirements.??

VAEs Presented the Solution

After evaluating algorithms like matrix factorization and hidden Markov models, we selected variational autoencoders as the best machine-learning method for enhancing our customer segmentation.

VAEs are neural network that learns complex distributions over data and compresses them into interpretable latent representations.

Unlike K-means which cluster data into distinct groups, VAEs learn a continuous latent space that can capture nuanced customer differences.

VAE Implementation?

We implemented a VAE model using customer data, including past purchases, browsing history, item views, and survey responses.

The model was trained to reproduce the input customer data from a 32-dimensional latent embedding space.

We interpreted each dimension as representing different customer interests and preferences learned solely from their observed behaviors.?

We clustered customers within this 32-dim latent space using K-means to generate customer segments for targeting.

The VAE-derived segments proved far more reflective of underlying customer traits than our original segments.

For example, one segment captured customers with a high predicted interest in fitness gear and sportswear despite never explicitly labeling customers by these categories before.

Business Impact

The adoption of VAEs led to significant improvements in marketing performance:

Email clickthrough rates increased 22% from better-personalized messaging.?

Online sales attributed to campaigns rose 14% from more precisely targeted ads.

Recommended product clickthroughs rose 19% as suggestions better-matched customer interests.

The granular VAE-powered segments also enabled us to develop new products and content tailored to underserved customer needs.

In the future, we plan to rebuild segmentation periodically as new customer data arrives to improve targeting continuously.

Key Takeaways

VAEs can learn nuanced data representations without explicit labeling.?

Continuous latent embeddings capture segmentation complexities lost in discrete clusters.

Periodic retraining maintains highly relevant segments as new behaviors emerge.

By implementing VAEs for segmentation, we achieved significant gains in marketing performance and developed deeper data-driven customer understanding.

This case study demonstrates the power of variational autoencoders and modern AI for critical business needs like customer targeting.

Other companies can follow our lead using VAEs to enhance marketing through precise user insights.

For Case Studies on Marketing using Machine Learning, visit: www.kiranvoleti.com