1. Customer Journey Analytics via Pathways:​

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• ​The project aims to identify marketing interactions effective at different stages of customer journey.

•  Defined success metrics for various stages of the customer journey (Awareness, Consideration, Engagement, Loyalty, Advocacy) through data-driven analysis and collaboration with brand teams.

•  Utilized community detection algorithms to identify key patterns and success metrics within customer journey stages, enabling more targeted marketing strategies.

• Developed and implemented data preprocessing techniques to prepare extensive marketing data, prescription data and web traffic data for analysis in a customer journey modeling framework.

• Identified and prioritized key marketing drivers using Elastic Net modeling, narrowing down from fifty potential predictors to the most impactful ones.

• Utilized advanced clustering techniques (KMeans, GMM, etc.) to segment marketing drivers, enhancing the understanding of customer interactions and behaviors.

• Developed regression models using XGBoost to identify the main marketing variables driving desired outcomes at each journey stage. Utilized SHAP (Shapley Additive exPlanations) values to pinpoint key marketing factors influencing success metrics.

• Employed Structural Equation Modeling (SEM) to construct and analyze customer journey pathways, determining critical nodes and optimizing marketing efforts at each stage.

• Calculated success probabilities for recommended pathways at each stage by analyzing HCP interactions and prescriptions.

• The project was selected for poster presentation at the PMSA Conference 2023.

• We published full paper in the PMSA (Pharmaceutical Management Science Association) Journal Spring 2024.

• Received Bronze award for this project from Genpact.

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2. Assessing the Impact of eSales Aid on Sales using Statistical model : 

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   A. Impact of eSales calls vs. Non eSales calls:

•  Determine how the use of eSales tools during calls affects sales performance compared to traditional calls.

•  Conducted data preparation through segment filtering, selling team categorization, and application of relevant criteria, including data cleaning, outlier detection, and various transformations.

•  Used regression model to quantify the impact of eSales calls on sales, controlling for other variables. 

 

   B. Impact of Messages and their combinations : 

• Overall Level: Assess the general effectiveness of different types and sequence of messages on sales.

• Segments: Evaluate how various customer segments respond to different messages.

• Cleaned the data, create features for message types and combinations, and segment customers. Explore the data to identify initial patterns and different across segments and specialties.

• Used regression models to quantify the influence of message types and combinations, providing insights into optimal messaging strategies.

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3. Identification of Contribution of Media variables :​

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• Conduct market mix modeling to find contribution of media variables and return on investment for reputed clients.'

• Prepared and analyzed media, non-media and base variables data sets using python. 

• Calculated summary statistics and performed EDA.

• Employed multiple linear regression models to ascertain the predictive ability and identified crucial areas for targeting.

• Used different transformations like log-transformation, adstock transformation etc. 

• Calculated return on investment (ROI) for media variables to understand their impact.