Learn how a Global ePharma Company in India utilises Category Affinity Model to increase the conversion rate for OTC drugs.
The Challenge
As one of the leading ePharma companies in India, our client offers a wide range of services in the pharmaceutical industry, catering to both B2C and B2B sectors. These services include diagnostics labs, online doctor consultations, and online surgery booking.
However, our client has been facing challenges in their B2C – over-the-counter (OTC) vertical, specifically in generating healthy conversion rates from push notifications.
Our client wanted to improve the click-through rate and conversion rate of push notifications through targeted marketing strategies, while also optimising costs and implementing effective marketing initiatives.
The Strategy
1. Identify customers affinity to various product categories using ML models
A predictive model was built to identify customers’ affinity to various product categories. A score that provides the probability of the customer purchasing a product in a specific category in the upcoming month was generated.
The Execution
1. Data preparation
Customer transaction level data was used to generate insights for purchase behaviour. Then, clickstream data was gathered to understand the customer behaviour on the platform and application. By combining both data, we obtained holistic information that has the potential to uncover hidden trends.
2. Build a classification model to predict and generate affinity scores
Data analysis was used to identify relationships between the factors and the relevant variables for the business case. Historical data was then utilised to train the model on a monthly level, enabling the generation of insights for the upcoming month. The same model was tested with new data to produce accurate scores and recommendations.
The Results
- Increased 2% to 3% in conversion from push notifications / activation channels
- Reduced ad spend by 20%
- Enhanced customer experience (lower spam rate)
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