Situation
A large private equity fund whose 30% of portfolio comprised of e-commerce companies reached out to JMI to leverage its data analytics capabilities to identify actionable insights to increase gross sales for portfolio companies. Its specific briefs were to:
- Identify the customer segments for selling the best product mix in specific geographies.
- Forecast revenues with the recommended product mix (from above analysis) based on demand trends.
JMI Implementation
- Collected large amount of customer data of its e-commerce portfolio companies from a range of data sources such as transactional/purchase data, demographic data, customer interactions etc.
- Used JMI proprietary similarity metrics with clustering techniques and decision tree to define top segments based on customer characters, geo-factors, market trends, macro-economic factors and others.
- Identified that two of its e-commerce companies have lower market penetration in top tier cities which has a high impact on its top line revenue.
- Used classification methods to score the degree of customer’s propensity for purchasing particular products of each company based on historical data obtained from the targeted market segments.
- Implemented time-series and artificial neural networks to predict revenue that is likely to result from newly identified segments and conducted back-testing using historical marketing data for validating the hypothesis.
- Cross-validated data and model accuracy using partitioned data and prebuilt ML/ AI models.
Value Delivered
- Provided insights on targeting the most significant customer segments in the specific markets.
- Classified the right product mix based on customer types and their preferences.
- Increased QoQ revenue by c.2-4% for all its e-commerce companies
- Reduced revenue forecasting errors for initial quarters by c.15%.