Anuva Sharma

• New Customer On-boarding(Active thread): By leveraging the concept of Markov chain, carrying out path analysis to achieve a significant increase in customer activation behavior.

Customer Segmentation:

• Grouped available online features into different buckets depending on the kind of value proposition they provide to customers, followed by measuring customer activity along those features resulting in customer level value proposition scores
• Segmented customers into 5 groups based on their online activity and measured the worth of these customers by taking in consideration their online activity as well as their in-store behavior, developing a complete picture of these customers
• Leveraged the segmentation results to build a look-alike model to target similar offline customers, resulting in increased online customer base

Tools used : Python, Clustering, Silhouette score, SQL

Conversion funnel Analysis:

• Leveraged the basic concepts of the visit conversion funnel to find the leakage points in the app/website and provided recommendations to fix those leakage points for a retail based client
• Product in next shopping trip, prediction feature showed significantly low product add percentage

Tools used : Python, SQL, Funnel Visualization method

Clicks to Bricks Analysis:

• Profiling of web and app customers to identify the penetration percentage of various customer segments
• Performed analysis on customers' web and app behavior, observed online behavioral patterns of customer and relate them with their in-store behavior to understand customers' purchase pattern
• Provide business solutions to enhance customers' experience on online platform, resulting in increased customer engagement.

Tools used : Python, SQL, Adobe clickstream data, EDA

Turnover Forecasting:

• Built an explainable forecasting model to quantify the impact of various controllable and uncontrollable factors on B2B sales
• Identified vital promotions that affected sales and enabled promotions team to find the idea time period for promotions

Tools used : Python, Linear regression, Random Forest, SQL

• Predict which employee has a higher chance of getting promoted and help the organization to expedite the appraisal and promotion process

Tools used: Classification, python

• Sales Prediction Model Build a Machine Learning Model on the transactional data to predict the purchase amount of a given customer during a special Sales day

Tools used: Regression, python