Swiggy

Market Potential On City Locality Level

Delivery Optimization / Franchise Model Implementation / Cloud Kitchen Establishment

Swiggy, India’s leading online food ordering and delivery platform, faced challenges in expanding into new cities and optimizing delivery logistics.

KENTRIX provided a solution by using its Geomarketeer tool to analyze over 40+ data parameters for individual locations, enabling Swiggy to select high-potential cities and optimize network expansion.

This consumer data-driven approach also helped in evaluating sales potential for restaurants, cloud kitchens, and franchise partners. Swiggy’s growth trajectory over recent years reflects the success of this data-driven strategy, showcasing how KENTRIX’s solutions have been instrumental in supporting Swiggy’s expansion and operational efficiency.

The Challenge

City Selection

Swiggy operated in their first 17 cities when they desired to expand their current offerings into new cities using a consumer data-driven approach. The objective was to understand the macro-economic and other factors driving the propensity to order home delivery for food for the towns/cities beyond those currently served. This is across 3,200+ cities and towns on equal available data levels!

Delivery Optimization / Franchise Model / Cloud Kitchens

Post selecting high-potential cities and towns for a phased roll-out over two years, Swiggy intended to understand on the ground the optimal network expansion for their own cloud kitchens, the sales potential of restaurants and kitchens of prospective franchise partners, and last but not least, optimizing the delivery logistics with a consumer data-driven demand forecasting.

The KENTRIX Solution

For the ‘deep-dive’ on-ground location-based objectives as detailed above, the KENTRIX Geomarketeer solution delivered the required granularity to evaluate more than 40+ data parameters individual locations– even within the same PIN codes or city localities.

Swiggy implemented an end-to-end consumer-driven approach that has the same variables from the same sources available for the city-wise screening across the country and for the inner city location analytics: On the desired granularity of consumer market data and variety the company was looking for Swiggy’s growth ratio over recent years speaks for itself.

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