Growth on Existing Customers

Enhance the profile of your customers or prospective customers using more than 95+ consumer profile scores identifying their income, lifestyle affinity, product purchase behavior etc.

Customer Profiles and Customer Segmentation: Best Practice Profiles: Identifying actual customer potential set against customer value exploited.

Precision Targeting with Hyper-Personalized Offers: Customer Retention and increasing the business value with each customer.

Customer Product Affinities: Identifying 1:1 high potential customers/prospects for specific product propositions.

Up-sell/Cross-sell: Mapping own products to the consumer enhancement classifications, identifying optimal product propositions, highly relevant for each customer.

Optimize your distribution system

Residential building precise customer potential information for maximum quality location and market analytics.

Sales Performance Monitoring: Understand a Retailers/Wholesalers/local market (PIN Code/Locality) sales potential product-by-product

Competitive Intelligence: Analyze your competitors and understand what drives their traffic.

Economic Potential/Customer-Product Affinities: Understand WHERE ‘bottom-up’ from a residential building precision is the right target audience for a specific product.

Product Demand in Stores: Know the demand for your product to keep the right stock.

Target Audiences for various product ranges: States/Cities/PIN Codes/Localities/Down to a residential building precision.

Growth on New Customers

Using consumer profile insights in Digital advertising for sharp targeted campaigns reaching highly selected audiences by criteria of expenditures, income, and product purchase affinities.

Empowering deep consumer insights for companies using behaviour insights from more than 91.5 Cr Indian Consumers

Persona 360 - New Filters Available for META (FB/Insta/Pixel)

· Indian Consumer Income Classification

· Indian Consumer Lifestyle Affinity Segmentation

· NCCS Reference

· Financial Services Affinity (Banking, Insurance)

· FMCG and Retail

· Automotive Behaviour (First Hand/Second Hand)

· Two Wheelers Affinity

· Travel Behaviour (National/International)

· Geography (Different types of Urban/Rural)

· Fashion & Apparel/Jewellery & Gold/Interior

· Purchase Behaviour (Product and Price Categories)

·  Real Estate Affinity

· And more..

Early Warning System to Assess Customer Risks

Real-time income and consumer psychographic profile for better credit/financial product offering decision making.

Complements bureau score through an overlay of delinquency profiles, customer behavior and income levels at Residential Building/Household-level.

Helps in a more robust credit assessment resulting in a more accurate approval/rejection percentage at the most granularity possible.

Identifies negatively areas and green zones within the negative rated PIN codes for higher customer reach and sales force productivity, define top precision geo-limits (OGL) for various products.

Aids credit team to identify pre-qualified consumers at their exact place of living.

         Data Focus

The data is consumer first with real first-party household precise data on consumption patterns - built across 80+ anonymized transaction-based data.

The data is built on household level and not thus restricted by areas-more granularity in the data.

         Approach

Building a consumer profile on household-level data through online and offline sources-tracing back to real purchases and brand affinities.

Bottom-up approach.

         Model Basis

Robust affinity algorithms are built with continuously incoming transaction data for the total country consumer base, detecting change/continuity in spend behavior timely.

Real data refreshed 6 months and 12 months.

        Technical parameters

The data is organized in Residential Building Master, once in regular DB format, as well as geo-polygons in a stack of different granularities (Building–GPS, micro-markets (precision hexagons), administrative layers such as localities, PIN Codes, Sub-districts, Districts, states.

          Use Cases

Wide range of use cases: Offline/Online targeting of customers, Data Enrichment for Up-sell, Cross-sell models as well as EWS (Early Warning System) for customer risks. Location-based use cases ranging from precision TAM–SAM–SOM from territory, store footfall potential catchment areas, down to individual building customer potential;

Extremely versatile data with flexible territories and precise customer data.