An IT solutions and services company providing services to their client based in the CPG category wanted to expand their business in specific cities in the APAC, Middle East, and South America region.
They wanted to identify restaurants and the POIs in these cities which would be their channel for product distribution. These restaurants and POIs would be identified on the basis of multiple factors like the demography of the population, competition, weather conditions, and other critical POI data that would help them decide if the location would be lucrative for their business.
With many factors to monitor, analyze, and strategize decisions based on location analytics, it was getting difficult for them to do this in-house. This is when they came across Xtract.io location intelligence solutions and decided to implement it for their business.
The following were the depth of data and analytics the customer required help with
They wanted to thoroughly understand the competition within the boundary and influence of attraction that would directly affect their sales. The analysis would further drill down to the nature of the restaurant (take-home/ dine-in/ cloud kitchen) and the distance of influencers from their location. They also wanted to analyze the weather conditions in the location to decide if selling their product in that area would be profitable, as the product was season-sensitive.
The research they were looking to conduct was quite extensive as they wanted to thoroughly analyze factors that may directly or indirectly affect their business. They wanted to make a well-calculated expansion strategy that was majorly supported by location intelligence.
The location intelligence experts at Xtract.io analyzed the challenges and implemented a step-by-step solution.
There were several influencer categories that needed to be identified and analyzed.
The process began by creating a model that included influencers of the restaurant scene, both positive and negative. Our rigorous research helped us zero in on certain categories that would influence the choice of a restaurant in that geography. The categories were – religious centers, public transport, educational institutions, business centers, tourist attractions, banking facilities, commercial centers, and health centers.
The distance between the restaurant and POI was required to be calculated. Identified the distance of each influencing POI from the respective restaurants, restricting to a specific distance. We also aggregated online reviews and ratings of these restaurants, and based on the analysis of the sentiments and the demographic information, we provided insights into the type of audience visiting these POIs.
Apart from POI data, other information, such as area demographics, restaurant data, and event happenings, was required to be analyzed for further hypotheses. We aggregated demographic data by leveraging census information from government websites. We also aggregated data regarding the reputation of the restaurants in the form of reviews and ratings. Additionally, we identified several data points about the restaurant: scale of operation, delivery mode, menu, hours of operation, name, number of associated aggregators, types of cuisines served, and more.
© 2025 Xtract.io Technology Solutions Pvt Ltd | All Rights Reserved | A Mobius Venture.
© 2025 Xtract.io Technology Solutions Pvt Ltd | All Rights Reserved | A Mobius Venture.
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