Increasing number of purchases by customer through predictive modeling
- Increase the Conversion Rate in those Customers with a Higher Propensity to Buy.
- Our Client is an ecommerce with 1M purchases/year.
- We will create a predictive model that will predict the purchase probability of each of our customers in the next month based on behavior and interactions with us in recent years.
- With customers with the highest probability of purchase we will apply retargeting strategies to increase the possibility of closing the sale.
- Obtain, clean, and prepare data needed for the project.
Define and Implement Predictive Models (Machine Learning)
- Featuring Engineering: Select/create input variables
- Comparison of two algorithms of predictive models in Artificial Intelligence and Machine Learning: Neural Networks using Tensorflow vs XGBoost.
- Fine-tuning: Tune parameters / hyperparameters, iterate over the output, compare performance to finally selected best option.
- Apply trained model to real data: The output is a list of Customers with High probability to Buy.
Define strategy for retargeting:
- Define and implement A/B testing for email campaigns
- Evaluate performance of the tests
- The application of the model drives to an increase of +25% in Conversion Rate
- This CR drove to an increase of 4% Month over Month in Number of Purchases
The correct application of predictive models in our customer base has allowed us to detect specific behaviors at the customer level: customers with a high probability of buying during the next month. Actions on that information – sending emails with specific content – have substantially improved a key element of our business: the number of sales.