{"id":5020,"date":"2025-02-07T12:03:06","date_gmt":"2025-02-07T06:33:06","guid":{"rendered":"http:\/\/geoiq.ai\/blog\/?p=5020"},"modified":"2025-09-18T15:11:40","modified_gmt":"2025-09-18T09:41:40","slug":"identifying-high-potential-sites-for-a-global-qsr","status":"publish","type":"post","link":"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/","title":{"rendered":"Identifying High-Potential Sites for a Global, Leading Quick-Service Restaurant\u00a0"},"content":{"rendered":"\n<p>One of the largest Quick Services Restaurant chains sought data-backed insights to identify ideal locations for their new stores. With 800+ existing stores, the client aimed to leverage GeoIQ\u2019s location AI solutions to analyze performance indicators and predict high-revenue locations.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_73 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#About_the_Client\" title=\"About the Client\">About the Client<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#The_Challenge\" title=\"The Challenge\">The Challenge<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#The_Outcome\" title=\"The Outcome\">The Outcome<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Methodology\" title=\"Methodology\">Methodology<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Step_1_Store_data_collection\" title=\"Step 1: Store data collection\">Step 1: Store data collection<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Step_2_Conducting_Maturity_and_Performance_Period_analysis\" title=\"Step 2: Conducting Maturity and Performance Period analysis\">Step 2: Conducting Maturity and Performance Period analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Step_3_Identifying_revenue-defining_features_location_characteristics\" title=\"Step 3:&nbsp; Identifying revenue-defining features (location characteristics)\">Step 3:&nbsp; Identifying revenue-defining features (location characteristics)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Step_4_Building_the_ML_Model_for_Predicting_Average_Daily_Transaction\" title=\"Step 4: Building the ML Model for Predicting Average Daily Transaction\">Step 4: Building the ML Model for Predicting Average Daily Transaction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Step_5_Understanding_the_factors_that_contribute_to_success\" title=\"Step 5: Understanding the factors that contribute to success\">Step 5: Understanding the factors that contribute to success<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Step_6_Identifying_locations_with_higher_revenue_potential\" title=\"Step 6: Identifying locations with higher revenue potential&nbsp;\">Step 6: Identifying locations with higher revenue potential&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#Insights\" title=\"Insights:\">Insights:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#1_Online_Sales\" title=\"1) Online Sales\">1) Online Sales<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#2_Offline_sales_in_Malls\" title=\"2) Offline sales in Malls&nbsp;\">2) Offline sales in Malls&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#3_Offline_sales_in_high_streets_Non-metro\" title=\"3) Offline sales in high streets (Non-metro)\">3) Offline sales in high streets (Non-metro)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"http:\/\/geoiq.ai\/blog\/identifying-high-potential-sites-for-a-global-qsr\/#4_Offline_sales_in_metro_high_street\" title=\"4) Offline sales in metro high street\">4) Offline sales in metro high street<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"About_the_Client\"><\/span>About the Client<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>A renowned global leader in the quick-service and fast-food restaurant industry, that operates in over 100 countries and serves millions of customers daily. Our client\u2019s location strategy lies in opening stores in high-traffic, high-potential zones. As of December 2024, the brand has 848 stores across India.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Challenge\"><\/span>The Challenge<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Accurately forecast the potential number of transactions for both dine-in and online channels on a daily basis.<\/li>\n\n\n\n<li>Scout and identify potential markets in the city list provided by the client.&nbsp;<\/li>\n\n\n\n<li>Pinpoint specific street-level locations to maximise profitability and minimise risk.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Outcome\"><\/span>The Outcome<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>With custom machine-learning models powered by GeoIQ\u2019s location data,<strong> the brand successfully shortlisted 50+ high-potential sites<\/strong> for their next set of stores across India.  <\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Methodology\"><\/span>Methodology<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"723\" src=\"http:\/\/geoiq.ai\/blog\/wp-content\/uploads\/2025\/02\/Artboard-2@2x-1024x723.png\" alt=\"\" class=\"wp-image-5499\" srcset=\"http:\/\/geoiq.ai\/blog\/wp-content\/uploads\/2025\/02\/Artboard-2@2x-1024x723.png 1024w, http:\/\/geoiq.ai\/blog\/wp-content\/uploads\/2025\/02\/Artboard-2@2x-300x212.png 300w, http:\/\/geoiq.ai\/blog\/wp-content\/uploads\/2025\/02\/Artboard-2@2x-768x542.png 768w, http:\/\/geoiq.ai\/blog\/wp-content\/uploads\/2025\/02\/Artboard-2@2x-1536x1084.png 1536w, http:\/\/geoiq.ai\/blog\/wp-content\/uploads\/2025\/02\/Artboard-2@2x-2048x1446.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Effective site selection requires a deep understanding of the target locations, particularly their demographics.<\/p>\n\n\n\n<p>To help the brand shortlist high-revenue potential locations, we built separate machine learning models for both online and offline channels.<\/p>\n\n\n\n<p>The revenue model we built is designed to forecast the revenue range the brand can expect for both channels based on the success-defining variables (KPIs) in a target location, which will be listed later on.&nbsp;<\/p>\n\n\n\n<p>The machine learning model we built will:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Train on the existing data from the stores,&nbsp;<\/li>\n\n\n\n<li>Identify possible factors that contribute to a store\u2019s success, and<\/li>\n\n\n\n<li>Replicate the success factors across new &amp; potential locations to predict their revenue potential and suitability for expansion.<\/li>\n<\/ul>\n\n\n\n<p>Now let\u2019s discuss how we started building the model on a step-by-step basis.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_1_Store_data_collection\"><\/span>Step 1: Store data collection<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>We worked with our client to gather data from their existing stores &#8211; 827 to be precise, across 167 cities which includes both operating and closed stores. The overall timeframe of the data we collected is 24 months.&nbsp;<\/p>\n\n\n\n<p>The collected data includes various store specifications such as Store Code, Store Name, Store Ownership (COCO\/FOFO), Store Opening Date, Store Type (Mall, Tech Park, High-Street), Store Size, Seating Capacity, Closing Date, and Reason for Closing.&nbsp;<\/p>\n\n\n\n<p>To analyze how well the store performs in terms of sales, we\u2019ve collected the number of In-store&nbsp;<\/p>\n\n\n\n<p>Receipts, and the Number of Online Transaction Receipts.<\/p>\n\n\n\n<p>These data were then sanity checked for accuracy, correctness, and quality to ensure there were no obvious errors, inconsistencies, or missing information on the data front.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_2_Conducting_Maturity_and_Performance_Period_analysis\"><\/span>Step 2: Conducting Maturity and Performance Period analysis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>After ensuring the data was clean and well explored, we conducted the following analyses to assess its reliability:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Maturity Period Analysis<\/strong> \u2013 to identify when a store&#8217;s sales stabilize after opening on average. We found that this typically happens within 6 months for both the channels. This ensures that we only train the model based on reliable, stable data.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Performance Period Analysis<\/strong> \u2013&nbsp; to reveal the patterns of sales consistency across both channels over a 12-month period. We&#8217;ve found that sales remain constant throughout this time, which ensures that the data we feed to the model reflects a store&#8217;s long-term success rather than taking temporary sales fluctuations into account.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>Also, by analyzing these periods, we identified the range of data we can consider, ensuring that the model is trained using relevant and stable information.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_3_Identifying_revenue-defining_features_location_characteristics\"><\/span>Step 3:&nbsp; Identifying revenue-defining features (location characteristics)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Using the collected data, we explored all factors influencing daily transactions (both online and offline) with the help of our pre-built geospatial features,(such as the presence of branded restaurants, apparel stores, etc), totalling 3,000.&nbsp;<\/p>\n\n\n\n<p>These features were then defined for various catchment proximities, ranging from as close as 100m and 500m to 15-minute drive times. By correlating these attributes with existing store sales data, the initial model we built was able to identify the <em>possible reasons<\/em> behind a store\u2019s sales.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_4_Building_the_ML_Model_for_Predicting_Average_Daily_Transaction\"><\/span>Step 4: Building the ML Model for Predicting Average Daily Transaction<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Once we had ensured the reliability and identified the timeline we should consider for our data, we proceeded to build an ML model to predict average daily number of transactions.&nbsp;<\/p>\n\n\n\n<p>Given the complexity and differences in sales patterns across various locations, we developed separate machine-learning models for different segments to achieve more accurate predictions.<\/p>\n\n\n\n<p>To forecast transactions nationwide, we split the modelling approach into specific sales channels and location categories as outlined below:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Online number of transactions<\/strong>: <em>(Across India)<\/em><\/li>\n\n\n\n<li><strong>Offline number of transactions<\/strong>:<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Malls.<\/li>\n\n\n\n<li>Metro Cities.<\/li>\n\n\n\n<li>Non-Metro Cities.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_5_Understanding_the_factors_that_contribute_to_success\"><\/span>Step 5: Understanding the factors that contribute to success<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>We ran prediction models on different locations to identify areas that result in a higher daily number of transactions.<\/p>\n\n\n\n<p>By analyzing the patterns of successful stores, the model identified which specific factors and attributes from the 3,000 variables influence the sales of a store for both online and offline channels.<\/p>\n\n\n\n<p>We then selected key attributes of a location that affect the store both positively and negatively.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_6_Identifying_locations_with_higher_revenue_potential\"><\/span>Step 6: Identifying locations with higher revenue potential&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Using the insights gained from the previous steps, we moved forward with running the model across India and identifying locations that offer the highest potential for revenue generation.<\/p>\n\n\n\n<p>By leveraging the variables and patterns identified through the machine learning models, we were able to pinpoint areas that exhibit higher revenue potential.<\/p>\n\n\n\n<p><em>Below are the key defining attributes that directly influence sales:<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Insights\"><\/span>Insights:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Online_Sales\"><\/span>1) Online Sales<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>i) Top location Attributes that impact online sales <strong>positively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Residential rent<\/td><td>7%<\/td><\/tr><tr><td>Presence of large apartments<\/td><td>7%<\/td><\/tr><tr><td>Presence of pizza joints<\/td><td>6%<\/td><\/tr><tr><td>Footfall in fast food joints&nbsp;<\/td><td>6%<\/td><\/tr><tr><td>Presence of dessert stores<\/td><td>6%<\/td><\/tr><tr><td>Presence of gyms<\/td><td>4%<\/td><\/tr><tr><td>Presence of hotels&nbsp;<\/td><td>4%<\/td><\/tr><tr><td>Presence of fast-food joints&nbsp;<\/td><td>4%<\/td><\/tr><tr><td>Commercial building area<\/td><td>3%<\/td><\/tr><tr><td>Total building area<\/td><td>3%<\/td><\/tr><tr><td>Presence of food delivery and takeaway restaurants&nbsp;<\/td><td>3%<\/td><\/tr><tr><td>Households with income above 10L&nbsp;<\/td><td>1%<\/td><\/tr><tr><td>Seating availability of the stores&nbsp;<\/td><td>1%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>ii) Top 3 location Attributes that impact online sales <strong>negatively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute&nbsp;<\/td><td>Impact<\/td><\/tr><tr><td>Footfall in nearby shopping complexes<\/td><td>11%&nbsp;<\/td><\/tr><tr><td>Presence of stores in Tier 3 cities&nbsp;<\/td><td>8%&nbsp;<\/td><\/tr><tr><td>Online delivery of local Indian cuisines<\/td><td>7%&nbsp;<\/td><\/tr><tr><td>Presence of stores in the state &#8211; Gujarat&nbsp;<\/td><td>6%&nbsp;<\/td><\/tr><tr><td>Presence of stores in the retail format &#8211; Malls<\/td><td>5%&nbsp;<\/td><\/tr><tr><td>Presence of branded cinemas&nbsp;<\/td><td>4%&nbsp;<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Offline_sales_in_Malls\"><\/span>2) Offline sales in Malls&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>i) Location attributes that impact offline sales in malls <strong>positively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Presence of branded apparel stores<\/td><td>14%&nbsp;<\/td><\/tr><tr><td>Presence of branded cosmetic stores<\/td><td>13%&nbsp;<\/td><\/tr><tr><td>Presence of burger joints<\/td><td>10%&nbsp;<\/td><\/tr><tr><td>Number of active stores&nbsp;<\/td><td>9%&nbsp;<\/td><\/tr><tr><td>Presence of branded restaurants&nbsp;<\/td><td>8%&nbsp;<\/td><\/tr><tr><td>Presence of frontage in the stores&nbsp;<\/td><td>7%&nbsp;<\/td><\/tr><tr><td>Presence of dessert shops<\/td><td>5%&nbsp;<\/td><\/tr><tr><td>Presence of branded eyewear shops<\/td><td>4%&nbsp;<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>ii) Location attributes that impact offline sales in malls <strong>negatively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Households with Income above 20L&nbsp;<\/td><td>5%<\/td><\/tr><tr><td>Availability of online delivery of pizza&nbsp;<\/td><td>11%<\/td><\/tr><tr><td>Presence of stores in the state, Uttar Pradesh<\/td><td>14%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Offline_sales_in_high_streets_Non-metro\"><\/span>3) Offline sales in high streets (Non-metro)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>i) Location attributes that impact offline sales in metro high streets <strong>positively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Presence of shopping complexes<\/td><td>9%&nbsp;<\/td><\/tr><tr><td>Presence of store in the state &#8211; Punjab&nbsp;<\/td><td>8%&nbsp;<\/td><\/tr><tr><td>Presence of high-end branded restaurants<\/td><td>7%&nbsp;<\/td><\/tr><tr><td>Cost for two at cafes in the immediate surrounding&nbsp;<\/td><td>7%&nbsp;<\/td><\/tr><tr><td>Presence of food delivery and takeaway restaurants<\/td><td>7%&nbsp;<\/td><\/tr><tr><td>Presence of branded gyms<\/td><td>6%&nbsp;<\/td><\/tr><tr><td>Area (sqft) of a store<\/td><td>5%&nbsp;<\/td><\/tr><tr><td>Commercial building area in the vicinity<\/td><td>4%&nbsp;<\/td><\/tr><tr><td>Presence of small apartments&nbsp;<\/td><td>4%&nbsp;<\/td><\/tr><tr><td>Online sales of continental food&nbsp;<\/td><td>3%&nbsp;<\/td><\/tr><tr><td>Presence of Italian restaurants<\/td><td>1%&nbsp;<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>ii) Location attributes that impact offline sales in metro high streets <strong>negatively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Presence of South Indian restaurants<\/td><td>10%<\/td><\/tr><tr><td>Presence of Company-owned Company Operated stores (COCO)<\/td><td>10%<\/td><\/tr><tr><td>Presence of residential buildings&nbsp;<\/td><td>10%<\/td><\/tr><tr><td>Commercial rent<\/td><td>10%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Offline_sales_in_metro_high_street\"><\/span>4) Offline sales in metro high street<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>i) Location attributes that impact offline sales in metro high streets <strong>positively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Meal cost for two at North Indian restaurants<\/td><td>12%<\/td><\/tr><tr><td>Commercial rent&nbsp;<\/td><td>10%<\/td><\/tr><tr><td>Area of the store&nbsp;<\/td><td>9%<\/td><\/tr><tr><td>Presence of branded restaurants<\/td><td>9%<\/td><\/tr><tr><td>Presence of fast food joints&nbsp;<\/td><td>8%<\/td><\/tr><tr><td>Cost for two at dessert restaurants<\/td><td>8%<\/td><\/tr><tr><td>Footfall in nearby offices<\/td><td>6%<\/td><\/tr><tr><td>Seating of the store<\/td><td>5%<\/td><\/tr><tr><td>Cost for two &#8211; beverages<\/td><td>5%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>ii) Location attributes that impact offline sales in metro high streets <strong>negatively<\/strong>:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Location Attribute<\/td><td>Impact<\/td><\/tr><tr><td>Presence of beauty salons<\/td><td>12%<\/td><\/tr><tr><td>Presence of biryani joints&nbsp;<\/td><td>8%<\/td><\/tr><tr><td>Distance from secondary roads&nbsp;<\/td><td>5%<\/td><\/tr><tr><td>Online delivery affluence for the pizza&nbsp;<\/td><td>3%<\/td><\/tr><tr><td>Meal cost for two at Chinese restaurants&nbsp;<\/td><td>1%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>These identified key location attributes that influence both online and offline sales, will help us pick the right locations for our client.<\/p>\n\n\n\n<p>By understanding the positive and negative factors affecting sales in different regions\u2014whether in malls, high streets, or metro\/non-metro areas\u2014we can identify prime locations with the highest revenue potential.<\/p>\n\n\n\n<p>By applying these insights to future site selection, the client can strategically expand and maximize profitability across both channels.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>Leveraging these valuable insights, GeoIQ enabled the brand to efficiently <strong>shortlist 50+ high-potential sites<\/strong>, ensuring profitability and sustained growth in the future!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Utilising our location data, we have helped a global, leading QSR brand shortlist 21 high revenue potential in just two 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