Customer 360 and data analytics for a global automobile manufacturer
Overview
A global car manufacturing company wanted to increase its response rate create customer 360 model view of unique customers.
Client’s data had many inconsistencies, inaccuracies and duplicate entries; unusable for data analytics
- TransOrg developed a customer 360 model view of unique customers, from multiple data sources, viz., sales enquiry, sales, after-sales, post-service feedback, value added services and old vehicle buy-back
- Develop data driven customer analytics use cases
Solution
- Analyzed customer data and fixed inconsistencies viz., incorrect, missing values, wrong customer tagging, duplicate entries etc.
- Assigned a unique identifier code to all records of the same customer after consolidating customer data from various data sources:
- Data exploration: Filled missing values and explored data distribution
- Deduplication rules on data: Identified ‘KEY’ customer identifiers common across data records
- EDA: Identified all available data fields and their fill rates
- Customer view development: Finalized KPIs for customer 360
- Developed data driven use cases from customer 360 database
- Customer loyalty segments based on historical transactions
- Churn prediction, customer retention and targeted marketing
Approach
- Exploratory data analysis to understand pattern in customer behavior for buying insurance products
- Used Travel insurance customers (76% of customers were buying Travel) to find opportunity for cross-sell
- Developed different propensity models to predict likelihood of customer having Travel insurance for buying another insurance product
- Compared past campaign conversion with existing cross-sell campaign conversion
Campaign Channel
- Campaign data was limited only to email channel
- Proposed test vs control group strategy to identify right channel to run cross-sell campaigns
Impacts
Increased response rate on targeted marketing campaigns for multiple products by up to 48%
Identified drivers behind customer churn from client’s value-chain, for example:
- Vehicle model
- Lifetime service attributes
- Post-service feedback
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