Geomarketing for hyperlocal food delivery service

FMCG and Retail | Food Delivery

The Challenge

Expanding business and tapping into new consumer segments requires companies to carry out data-driven strategic planning, particularly streamlining efforts through a targeted approach and optimizing resources, building an optimal distribution system and cutting off wasteful investment. 

An Indian hyperlocal food delivery service, which currently runs on a business-to-consumer (B2C) model, wanted to expand its business operations into the business-to-business (B2B) segment. They planned to do this by partnering with corporate parks, local shops, merchants, and restaurants across the country. 

PIN Code / Ward-level and Locality data was not good enough to analyze the potential of future partners. Granular market insights and consumer information was required on a large scale for many Indian cities. 

The Solution

Geomarketeer has customized micro-level demographic data on income and spending patterns, that can be integrated with custom client data and additional POI layers to reveal intelligent granular insights. 

Geomarketeer’s interactive web-map was used to visualize customized catchments with micro-market data on income and expenditure patterns, combined with POI layers locating potential store-, restaurant-partners for the food delivery service company.  

Geomarketeer enabled location analysis, market segmentation, and partner/customer potential identification across micro-markets in various Indian cities. In custom delivery zone territories, the micro-market data was used to build precise delivery polygons customer potential reports. Based on this, reports were generated to provide concrete numbers for a statistical understanding.  

The Result

Geomarketeer’s micro-market data on income, customer lifestyle affinity segmentation and expenditure patterns helped the food delivery company to understand the consumer habits in smallest catchment areas, their delivery zones. This enabled our client to design location-specific custom offers. This was further used to select and target high potential store / restaurant partners using POI layers.  

The company expanded their business operations successfully in a hyper targeting of high potential local markets approach. Micro-market data about consumer behavior patterns enabled the client to understand the density of foot traffic by consumer profiles in different locations. This resulted in commercial benefit by streamlining the partnership strategy with local merchants, stores, and restaurants for better expansion. 

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