{"id":27783,"date":"2022-11-25T12:45:01","date_gmt":"2022-11-25T07:15:01","guid":{"rendered":"https:\/\/blazeclan.com\/studies\/a-leading-pizza-restaurant-chain-boosts-its-end-customer-experience-through-predictive-time-model-for-accurate-order-estimates-2\/"},"modified":"2023-03-03T18:36:55","modified_gmt":"2023-03-03T13:06:55","slug":"a-leading-pizza-restaurant-chain-boosts-its-end-customer-experience-through-predictive-time-model-for-accurate-order-estimates","status":"publish","type":"case_study","link":"https:\/\/blazeclan.com\/anz\/studies\/a-leading-pizza-restaurant-chain-boosts-its-end-customer-experience-through-predictive-time-model-for-accurate-order-estimates\/","title":{"rendered":"A Leading Pizza Restaurant Chain Boosts its End-Customer Experience through Predictive Time Model for Accurate\u00a0Order Estimates"},"content":{"rendered":"<h2><strong>About Customer<\/strong><\/h2>\n<p>The customer is a leading pizza restaurant chain in Malaysia and focuses on being the best pizza delivery firm. With over 240 pizza places located strategically across the region, the customer is increasing its stores for serving all pizza lovers in the nation. It continues to grow as a major ecommerce entity and has led several firsts in its digital platforms.<\/p>\n<h2><strong>The Challenge<\/strong><\/h2>\n<p>The customer wanted to achieve absolute accuracy for their processes, right from pizza preparation to delivery. They wanted to develop a predictive time system for calculating the pizza order ready time, wait time, and arrival time.<\/p>\n<p>Accuracy of the prediction time was supposed to equal the actual process timings, considering the delays caused due to several factors such as spike in orders, issues with pizza preparation and process, unpredictable delivery-bound circumstances, etc. Depending on the predictive time model, the customer sought to improve the processes, thereby enhancing customer experience and the overall business.<\/p>\n<p>Key requirements&nbsp;included<\/p>\n<ol>\n<li>Calculating actual time of delivery to the consumers\u200b<\/li>\n<li>Notifying the consumers about delays<\/li>\n<li>Notifying the delivery people about delays in picking up the orders based on predicted pizza preparation time<\/li>\n<\/ol>\n<h2><strong>The Solution<\/strong><\/h2>\n<p>The customer partnered with Blazeclan to develop the predictive time model for improving their consumer experience. Understanding the customer\u2019s processes, rules of business logic, and the way they calculate time for order ready, wait, and arrival was a major challenge. Another key challenge was testing the model in the dummy environment by adhering to all the aforesaid scenarios.<\/p>\n<p>Blazeclan developed the predictive time model to meet the customer\u2019s requirements effectively using a rule-based framework. Major factors were considered for achieving high consumer experience, such as information from the customer, historical data, and near-real-time calculated data.The model was built keeping in mind all three stages of the order life cycle, namely,<\/p>\n<p><strong>1. Order Ready Time \u2013<\/strong> This included scenarios of the unhidden as well as hidden orders.<br \/>\n<strong>2. Order Wait Time \u2013<\/strong> This included scenarios where the order is ready without the delivery person to pick it up or the order is ready with the delivery person available right on time.<br \/>\n<strong>3. Order Arrival Time \u2013<\/strong> This included scenarios where the delivery person arrives at the customer\u2019s door before\/right on time or the rider returns to the store.<\/p>\n<h2><strong>Simplified Diagram of the Deployed Predictive Time Model<\/strong><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone wp-image-38236 size-full\" src=\"https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1.jpg\" alt=\"\" width=\"2048\" height=\"958\" srcset=\"https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1.jpg 2048w, https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1-300x140.jpg 300w, https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1-1024x479.jpg 1024w, https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1-768x359.jpg 768w, https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1-1536x719.jpg 1536w, https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/11\/2-2048x958-1-1568x733.jpg 1568w\" sizes=\"(max-width: 2048px) 100vw, 2048px\" \/><\/p>\n<div class=\"row\">\n<div class=\"col-md-6\">\n<h2><strong>Benefits Achieved by the Customer<\/strong><\/h2>\n<ul>\n<li>The call center staff has access to the pizza arrival prediction time, which helps them in responding to consumer queries about their order&#8217;s status.\u200b<\/li>\n<li>Store managers, using the pizza arrival prediction time, can&nbsp;accurately determine the estimated time of an order&#8217;s arrival to the consumer.<\/li>\n<li>The delivery person, with access to the prediction time, can ensure timely delivery of the orders.<\/li>\n<li>Having the foresight of predicted pizza delivery life cycle helped the customer in achieving high efficiency across their business operations.<\/li>\n<\/ul>\n<\/div>\n<div class=\"col-md-6 mt-md-4\"><img decoding=\"async\" class=\"aligncenter size-full wp-image-28497\" src=\"https:\/\/blazeclan.com\/wp-content\/uploads\/2022\/03\/Value-Additions-Image-Dominos-Predtictive-Time-Model-Case-Study.jpg\" alt=\"\" width=\"925\" height=\"297\"><\/div>\n<\/div>\n<h2><strong>Tech Stack<\/strong><\/h2>\n<table class=\"table table-hover\">\n<tbody>\n<tr>\n<td>AWS Lambda<\/td>\n<td>Amazon API Gateway<\/td>\n<td>Amazon CloudWatch<\/td>\n<\/tr>\n<tr>\n<td>Amazon&nbsp;EventBridge\u200b<\/td>\n<td>AWS Parameter Store\u200b<\/td>\n<td>MongoDB<\/td>\n<\/tr>\n<tr>\n<td>Visual Studio Code\u200b<\/td>\n<td>Git\/GitLab\u200b<\/td>\n<td>Postman\u200b<\/td>\n<\/tr>\n<tr>\n<td>SonarQube\u200b<\/td>\n<td>Node.js<\/td>\n<td>\u200b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>About Customer The customer is a leading pizza restaurant chain in Malaysia and focuses on being the best pizza delivery firm. With over 240 pizza places located strategically across the region, the customer is increasing its stores for serving all pizza lovers in the nation. It continues to grow as a major ecommerce entity and [&hellip;]<\/p>\n","protected":false},"featured_media":14084,"parent":0,"menu_order":0,"template":"","case_category":[3966,3986],"case_tags":[857,4161,4146,4371,4376],"class_list":["post-27783","case_study","type-case_study","status-publish","has-post-thumbnail","hentry","case_category-advanced-analytics-anz","case_category-data-analytics-and-insights-anz","case_tags-advanced-analytics","case_tags-data-analytics-anz","case_tags-data-lake-anz","case_tags-machine-learning-anz","case_tags-predtictive-analytics-anz"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A Leading Pizza Restaurant Chain Boosts its End-Customer Experience through Predictive Time Model for Accurate\u00a0Order Estimates - Blazeclan<\/title>\n<meta name=\"description\" content=\"The customer partnered with Blazeclan to develop the predictive time model for improving their consumer experience. 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