References
Banking Industry
GAP ANALYSIS - IDENTIFYING CUSTOMER POTENTIAL VS. CUSTOMER VALUE ACHIEVED
The Bank classified its customer base mainly by the business their clients did with the Bank, yet not fully able to securely identify customer potentials where an income proof was not available to the Bank. Further, insufficient transaction data from their customers did not yield enough information for modelling customer potential.
MMS.IND profiled Bank customers on consumer lifestyle affinity segmentation data, income, and a range of product purchase affinities.
Based on the customer profiling, the Bank identified approx. 30% undersold customers, i.e. clients having a significant higher potential to bank against their current customer value to the Bank. Only within three months of the project, the Bank realised on an average a 24.8% conversion rate on re-targeting identified undersold customers, a significantly higher conversion rate than it used to be before.
QSR Sector
LOCATION ANALYSIS - IDENTIFYING MOST PROFITABLE LOCATION FOR NEW OUTLETS
Newly added stores during the first year of the brand working with the MMS.IND micro-market segmentation data, become profitable at store level within shortest time compared to before. “Double digit top-line growth for the full year was driven by new stores and improved store performance” as stated by the company.
MMS.IND Data also helped the brand to improve performance in existing stores, wherein the consumer information served better sharp-targeted marketing and sales activities in a stores' catchment areas attracting measurable more consumers into the store.
Banking Industry
EARLY WARNING SYSTEM FOR CUSTOMER CREDIT CARD DEFAULTING
At the point of underwriting a new customer for a credit card or loan, the Bank runs a credit score verification process of the applicant with the Indian Credit Bureau. The problem: nearly 30-40% of the applying customers, especially for personal loans, do not have a credit score in India. In the absence of a credit score for the loan / credit card applicant, the Bank was looking for detailed and absolutely reliable consumer profile information to build into their approval models for significantly downsizing default ratios.
MMS.IND profiled the Bank's customers into consumer lifestyle affinity segments integrating income and product purchase affinities. Historical default customers were profiled and identifies among the mosaic of 35+ consumer lifestyle affinity & income combination 5 to 10 high risk segments, in which the risk for defaulting lies 4-5 times higher than in other lifestyle affinity - income combinations.
The Bank integrated the MMS.IND consumer profiling by pulling the information in real-time via API connections at point of underwriting new loans, granting credit limits to build a Early Warning System for Defaulting, helping to significantly bring down the default ratio.
FMCG Sector
PRODUCT MIX ON INDIVIDUAL STORE LEVEL AND SALES PREDICTION
One of the top three biggest FMCG companies in India, defined following key problem statements for which MMS.IND was requested to provide consumer data based solutions:
- Most granular level of customer targeting is city-ward / PIN Code level, thus marketing and sales campaigns run without precision focus on specific target groups (high spill-over / waste to non-targeted customer segments)
- “One size fits all” customer acquisition approach misses on different consumer behaviour patterns linked to different local markets and high costs of reaching target groups due to “carpet-bombing” marketing and sales activities.
Applying the MMS.IND micro-market data and store catchment area profiling tools, the company's outlets were analysed on customer potential in their catchment areas. The micro-market segmentation of total cities / Rural districts supported the brand to identify product-wise high potential / priority markets, in which (a) product sales was benchmarked against potential for this product to sell and (b) optimal new store locations were identified to expand the business to.
esteemed key Clients
