{"id":170,"date":"2023-02-10T13:28:15","date_gmt":"2023-02-10T13:28:15","guid":{"rendered":"http:\/\/geoiqfrontendblogsprodwordpress-env.eba-dpx723ys.ap-south-1.elasticbeanstalk.com\/index.php\/2023\/02\/10\/geoiq-for-lenskart-a-retail-expansion-case-study\/"},"modified":"2025-01-15T14:28:25","modified_gmt":"2025-01-15T08:58:25","slug":"geoiq-for-lenskart-a-retail-expansion-case-study","status":"publish","type":"post","link":"http:\/\/geoiq.ai\/blog\/geoiq-for-lenskart-a-retail-expansion-case-study\/","title":{"rendered":"GeoIQ for Lenskart \u2014 A Retail Expansion Case Study"},"content":{"rendered":"\n

In this study, we will discuss how retail major Lenskart has benefitted from its partnership with GeoIQ, a location-based intelligence provide<\/em>r.<\/em><\/p>\n\n\n\n

Having access to location-based data is immensely helping businesses that are running their decision-making engines on data. It has been established fairly well that data is the new fuel for businesses to improve their ROIs, reduce costs, and have better engagement with users and customers among other benefits. Talking specifically about the retail sector, location-based data and intelligence is a game-changer, especially for their expansion plans.<\/em><\/em><\/p>\n\n\n\n

Location-based data and intelligence as a solution is in a very nascent stage. There could be a plethora of use cases for the same depending on business requirements that have not been thought of yet. Moreover, the technology behind these solutions makes them flexible and versatile to any use case that a business could think of. It is a very powerful use of real-world data to drive business decisions that can have an unimaginable impact on optimizing performance, cost, engagement, or any other metrics crucial for the business use case.<\/p>\n\n\n\n

Specifically for retail analytics companies, the biggest use case lies with optimizing their expansion plans. The traditional methods of approaching the problem of expansion with physical location scouting and deploying field teams are based more on judgment rather than logic. Hence, there is no way to validate the decisions made through these practices. Moreover, there is no way to predict the performance and revenue of the new stores as there is no data backing these decisions. Modern machine learning techniques used in location-based intelligence make this process more reliable and predictive.<\/p>\n\n\n\n

Today, real-world data is readily available and we have developed sophisticated technologies to transform this data into a structured and usable format. The location data<\/a> from various sources, once collected and transformed, becomes a gold mine to draw insights into the physical expansion of retail businesses. The brief idea behind optimizing the expansion plan for a business is to identify the best existing stores and find new locations using real-world data with similar conditions: socio-economic, demographic, level of infrastructure, population density, and many more. When businesses eliminate the judgment factor, and back site selection by real-world data, they can even predict the revenue and performance for each site they consider with minimum scope of error.<\/p>\n\n\n\n

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