Marketing Segment Flow Prediction

• Developed a novel temporal‐graph‐based model for marketing segments.
• Applied neural embedding techniques for temporal graphs to predict node features and edge weights, used for forecasting churn and flow of customers between segments.
• Designed network centrality & flow‐based measures to identify high‐activity segments, providing insights about market behaviour to marketers.

Data Driven Marketing Spend Optimization

• Developed a system to give optimal spend recommendations across media channels & adsets using historical marketing data, allowing marketing teams to focus on creativity and design.
• Implemented a revenue forecasting model to predict the revenue-budget relationship for each channel and adset, taking into account adstock transformation as well as the diminshing nature of returns. Dealt effectively with sparsity of data at the adset level.
• Implemented a cross-channel optimiser which used revenue forecasting models at both hierarchical levels to provide optimal spend allocations. Incorporated constraints like minimum/maximum channel spend keeping in mind practical marketing strategies.