Case 1
Client: One of the largest commercial credit bureaus in the world
Opportunity: Improve the matching on commercial credit to 100% and collect revenue for all answers
Solution:
- Segment existing response rate and revenue coverage on the queries
- Analyze the factors due to which revenue is not earned including sub-optimal matching algorithm, data not available, data can’t be productized and external data sources not used
- Simulated profitability if external data sources are used based on richness of data
- Analyzed the correlation between data quality, buy rate and pricing
- Defined control and test group of customers to understand walkaways and ways to prevent it
Result:
- Improved match rate to 100% driving $48 MM annualized revenue
Case 2
Client: One of the largest commercial credit bureaus in the world
Opportunity: Analyze market potential for a new product line; create a plan to double revenue in 3 years
Solution:
- Analyze renewal, upsell, cross-sell and attrition data to develop predictors for these behaviors
- Define the product segments aligned to small businesses (core targets) needs
- Segment the customer data using decision tree classifier models
- Test the models with un-seen data and further improve their precision
Result:
- Developed the plan to double the revenue using the models in 2.5 years
Case 3
Client: One of the largest credit-card networks globally
Opportunity: Build cutting edge product recommendation algorithm for the premium card portfolio
Solution:
- Focus on restaurants, the sizeable discretionary & elastic spend
- Analyze hundreds of millions of transactions to understand customer behavior individually and as part of social networks spanning merchant establishments
- Build powerful meta-data for restaurants in the network by reinterpreting existing data and integrating Zagat data
- Build an algorithm taking into account cuisine preferences, geographic footprint and spending preferences of a customer
Result:
- Algorithm patented by the client
- Algorithm predicted with as much as 50% accuracy where a person will dine or lunch in the near future leading to a range of marketing opportunities
Case 4
Client: One of the largest credit-card networks globally
Opportunity: Improve response rate to email marketing campaigns
Solution:
- Analyze customer profile and email response behavior to form preliminary hypotheses based on historical data
- Craft integrated tests encompassing key hypothesis for each element of the campaign – population selection, timing & creative – to generate experimental insights
Result:
- 20% increase in response rate to emails