References

Banking industry

GAP ANALYSIS - IDENTIFYING CUSTOMER POTENTIAL VS. CUSTOMER VALUE ACHIEVED

MMS.IND; Customer Potential Analysis India, Consumer Profiling on Income, Up-sell Cross-sell Target Audience India, Cross-sell Conversion Uplift India, Consumer Enrichment India
Sample call monitoring feedback analysis: 47.3 % conversion rates achieved on preselected enriched customers by MMS.IND consumer data.

Customer Profiles and Customer Segmentation

Gap Analysis: Identifying actual customer potential set against customer value exploited by the Bank

 

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

MMS.INd India, Location Analysis for new stores, Profitable locations for new stores india, optimisation of distribution network india, targeting high potential local markets India,
QSR sector brand store locations, micro-market segmentation showing monthly household-level income, locations of office buildings by number of employee count.

The newly added stores during the first year of the brand working with Geomarketeer, the MMS.IND micro-market segmentation data tool, become all 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” was stated by the company.

 

MMS.IND Data also helped the brand to improve performance in existing stores, wherein the consumer information served for more sharp targeted marketing and sales activities in the location’s catchment areas to attract more consumers into the store – online and offline.


banking industry

EARLY WARNING SYSTEM FOR CUSTOMER CREDIT CARD DEFAULTING

MMS.IND India, Early Warning System for Customer Default Behaviour, Risk Management India, Customer Risk Profiling India
Analysing historical default customers, identifying specific customer lifestyle and income constellations peaking among them. These are the high-risk clusters, serving as early-warning template on new customers applying for a credit card.

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. Further, the history file of credit card non-repaying / default customers were profiled to identify a “default consumer profile” as a template for the Bank.

 

Based on the customer profiling delivered, the Bank identified that their risks for defaulting in repayment lies 4 to 5 times higher in specific consumer lifestyle segments with the integrated income, age and family status profiles.

 

The Bank integrated the MMS.IND data on customer income and lifestyle affinity-segmentation into their Risk Models helping to significantly bring down their default rate.

fmcg sector

PRODUCT MIX ON INDIVIDUAL STORE LEVEL AND SALES PREDICTION

MMS.IND Store Catchment Area Analysis, Store Catchment Area Potential India, Store Performance Benchmarking India
Mapping store location and product sales onto micro-market segmentation of cities, identifying for each store catchment customer- and sales potential.

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.