Building a Machine Learning Model to Predict the Next Best Product for Your Customers: A Step-by-Step Guide

In the competitive world of e-commerce, predicting the next best product for your customers based on their purchase history and browsing behavior is critical for driving personalized recommendations, increasing conversions, and boosting customer lifetime value. This guide will provide you with a focused, practical approach to building a machine learning (ML) model tailored specifically for this task, optimized for both performance and SEO relevance.

1. Define Your Objective and Success Metrics Clearly

• What exactly is the “next best product”? Typically, it's the product with the highest purchase likelihood given past transactions and browsing patterns.

• Define KPIs: Are you targeting improvements in click-through rate (CTR), conversion rate, average order value (AOV), or customer retention?

• Consider latency requirements: Do you need real-time predictions or batch processing?

• Ensure compliance with data privacy regulations like GDPR and CCPA.

Having clear goals will shape your data collection, feature engineering, and model selection strategies.

2. Collect and Prepare High-Quality Data

Aggregate multiple data streams to capture customer signals fully:

• Purchase History: Include transactional data such as product IDs, purchase timestamps, quantities, and prices.

• Browsing Behavior: Capture viewing time, page clicks, search queries, cart additions, and categories browsed.

• Customer Metadata (Optional): Include demographics, loyalty tier, device type, and location for richer context.

Data Preparation Best Practices:

• Clean data to remove missing or inconsistent entries.

• Unify identifiers like Customer IDs and Product IDs across datasets to enable seamless integration.

• Handle cold-start scenarios with fallback rules or content-based recommendations for new customers or products.

• Align browsing and purchase events along timelines to capture interaction sequences accurately.

3. Feature Engineering for Predicting Next Best Product

Feature quality profoundly impacts model performance. Key engineered features include:

• Recency, Frequency, Monetary (RFM) metrics assessing customer purchase behavior.

• Browsing Session Patterns: Time spent per product page, category diversity, frequency of searches.

• Temporal Features: Day of week, time of day, seasonal purchase trends.

• Product Attributes: Price, category, brand, user ratings, discount presence.

• Sequential Data: Encode purchase and browsing event sequences for temporal modeling.

Use techniques like one-hot encoding or embedding layers (for deep learning) to represent categorical variables, and normalize continuous features like price or session time.

4. Label Creation and Dataset Construction

For supervised learning, your label is the actual ‘next product’ purchased after a sequence of interactions. Consider:

• Modeling the exact product SKU for fine-grain predictions.

• Grouping products into categories or clusters to reduce label space complexity.

• Generate negative samples (products not purchased) to balance training and improve ranking capabilities.

Ensure train-test splits respect temporal order to prevent data leakage.

5. Select and Build Your Prediction Model

Choose from models suited to recommendation systems:

• Collaborative Filtering: Matrix factorization or nearest-neighbor methods leveraging user-item interactions.

• Multi-Class Classification Models: Random Forest, XGBoost, or LightGBM trained on engineered features predict the next product class.

• Sequence Models: RNNs, LSTMs, or Transformer architectures that model sequential dependencies in purchase and browsing behavior.

• Hybrid Models combining collaborative and content data enhance accuracy.

• Contextual Bandits allow adaptive recommendations that improve based on user feedback.

Example: LSTM model in TensorFlow to predict next product from purchase sequences

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense

vocab_size = number_of_unique_products + 1  # Include padding token
embedding_dim = 50
sequence_length = 10  # Last 10 products in sequence

model = Sequential([
    Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=sequence_length),
    LSTM(128),
    Dense(vocab_size, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

6. Incorporate Browsing Behavior with Multi-Modal Models

Enrich your model by fusing purchase and browsing data streams:

• Merge features via concatenation or attention mechanisms.

• Use separate embedding and sequence encoders for browsing and purchase inputs, then combine.

Multi-Input TensorFlow Model example:

from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Concatenate
from tensorflow.keras.models import Model

purchase_input = Input(shape=(sequence_length,))
browsing_input = Input(shape=(sequence_length,))

purchase_emb = Embedding(vocab_size, embedding_dim)(purchase_input)
purchase_seq = LSTM(64)(purchase_emb)

browsing_emb = Embedding(vocab_size, embedding_dim)(browsing_input)
browsing_seq = LSTM(64)(browsing_emb)

merged = Concatenate()([purchase_seq, browsing_seq])
output = Dense(vocab_size, activation='softmax')(merged)

model = Model(inputs=[purchase_input, browsing_input], outputs=output)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

This approach enhances context understanding and prediction accuracy.

7. Evaluate Model Performance Using Ranking Metrics

Accuracy is insufficient for recommendation quality assessment. Use metrics such as:

• Precision@k and Recall@k: Measuring relevant items in the top-k recommendations.

• Mean Reciprocal Rank (MRR): Measuring position of first relevant recommendation.

• Normalized Discounted Cumulative Gain (NDCG): Weighted relevance based on rank positions.

Regular A/B testing with live users is crucial to validate business impact.

8. Deploy, Monitor, and Iterate

• Deploy models using scalable APIs through platforms like TensorFlow Serving, AWS SageMaker, or Google AI Platform.

• Consider batch processing for large-scale offline recommendations or real-time inference for dynamic personalization.

• Continuously monitor recommendation effectiveness, customer engagement metrics, and model drift.

• Incorporate customer feedback collected via surveys or polls using tools like Zigpoll to close the feedback loop and refine models.

9. Ensure Ethical Use and Compliance

• Abide by privacy laws such as GDPR and CCPA.

• Transparently communicate data usage and allow users control over personalization.

• Mitigate biases in training data to avoid unfair targeting.

10. Recommended Tools and Frameworks

• Data processing: pandas, Apache Spark

• Feature engineering: Featuretools

• Machine learning: scikit-learn, XGBoost, LightGBM

• Deep learning: TensorFlow, PyTorch

• Recommendation libraries: Surprise, implicit

• Experiment tracking: MLflow, Weights & Biases

• Deployment: Docker, Kubernetes

11. Real-World Case Study Summary

• Collect one year of purchase history and six months of browsing logs.

• Preprocess and engineer features capturing sequences and user-product interactions.

• Develop baseline models with matrix factorization.

• Implement LSTM-based hybrid model combining purchase and browsing sequences.

• Achieved significant lift in Precision@5 (0.35 vs baseline 0.12).

• Deploy model on AWS Lambda, integrated into website to deliver personalized recommendations.

• Use Zigpoll surveys to collect user feedback post-recommendation and adapt model monthly.

12. Best Practices for Long-Term Success

• Start with simple models before moving to complex architectures.

• Engage cross-functional teams spanning data science, marketing, and engineering.

• Use experimentation platforms for rigorous A/B testing.

• Protect customer privacy and ensure data security.

• Automate end-to-end pipelines from feature engineering to deployment.

• Prioritize relevancy in recommendations over volume to avoid user fatigue.

Building a machine learning model for next best product prediction relies on strategically combining purchase data, browsing behavior, and advanced modeling techniques. By following this structured approach, leveraging sequence models, integrating multi-modal data, and continuously iterating with customer feedback, you can create personalized recommendations that boost engagement and revenue.

Explore Zigpoll today to actively gather customer preferences and enhance your ML model’s effectiveness through targeted surveys.

Start diving deep into your customer data, develop predictive models using the strategies here, and accelerate your success with actionable user insights!