{"id":5183,"date":"2017-04-05T20:17:26","date_gmt":"2017-04-06T00:17:26","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/grubhub-changing-the-online-food-ordering-landscape\/"},"modified":"2017-04-09T16:52:40","modified_gmt":"2017-04-09T20:52:40","slug":"grubhub-revolutionizing-the-food-ordering-landscape","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/grubhub-revolutionizing-the-food-ordering-landscape\/","title":{"rendered":"Grubhub \u2013 Revolutionizing the food-ordering landscape"},"content":{"rendered":"

Grubhub has been connecting diners with local restaurants since its inception in 2004. It is a two-sided platform with diners and restaurants as the two major stakeholders. Few add a third stakeholder \u2013 drivers \u2013 to complete the puzzle. Currently valued at ~USD 2.9B, GrubHub reported annual revenues of USD 493 million and net income of USD 49.6 million in 2016 with a 36% and 30% y-o-y increase respectively over 2015.[1]<\/a> The number of active diners rose to 8.17 million and daily grubs to ~275,000, both with 21% increase y-o-y. Big data has contributed significantly to product improvement. This has helped the company add more than one million active diners during 2016, while sales and marketing expenses rose by only 21 percent.<\/p>\n

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Value Creation<\/strong><\/p>\n

Let us see how Grubhub creates value for its diners and restaurants:<\/p>\n

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Diners: Discovery<\/strong><\/p>\n

The extensive data collected from tens of thousands of daily order reviews help Grubhub put the right restaurant and the right meal in front of diners at the right time[2]<\/a>. \u00a0Just like Amazon, Grubhub\u2019s recommendation engine is based on several factors including past orders and their cuisines, past reviews, popular restaurants (based on other diners\u2019 reviews) in the neighborhood, similar diners\u2019 ordering habits, etc. This helps Grubhub curate and feature on homepage the list of restaurants the diner might like. Figure 1(a), 1(b) and 1(c) show such recommendations from my Grubhub homepage.<\/p>\n

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Figure 1a: Recommendation: Popular near me<\/strong><\/figcaption><\/figure>\n
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Figure 1a: Recommendation: My go-to restaurants<\/strong><\/figcaption><\/figure>\n

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Figure 1a: Recommendation: Restaurants serving my favorite cuisine<\/strong><\/figcaption><\/figure>\n

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Figure 1a: Popular dishes on restaurant page<\/strong><\/figcaption><\/figure>\n

Next, within a specific restaurant page, Grubhub leverages data to show diner the previously ordered and the most popular dishes, as shown in figure 2. This list is curated from thousands and thousands of orders fulfilled by the restaurant on Grubhub\u2019s platform. Another important opportunity here would be to suggest sides\/ desserts based on what similar diners have ordered in the past, something along the lines of what Amazon does.<\/p>\n

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Further, GrubHub recently launched its skill on Amazon Alexa allowing diners to re-order their past orders directly on Alexa[3]<\/a> and without using their laptop\/phone. This is another manifestation of how data is being leveraged.<\/p>\n

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Figure 3: Reviews and ratings<\/strong><\/figcaption><\/figure>\n

Once the order has been fulfilled, Grubhub nudges the diner to leave feedback on quality of food, correctness of order, timely delivery, etc. The real-time feedback captured from diners feeds into the reviews and ratings which show up as the user is making the decision. One challenge here – GrubHub makes sure to remove the fraud ratings, such as ones fed by the restaurants (at times) on single-item orders (eg. water bottle) to ensure the sanctity of the rating system. What would be further helpful is to capture feedback (data) dish-wise eg. how spicy was the curry, etc. This can further help the company help the diners make better choice about various dishes on the menu.<\/p>\n

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Restaurants: Order and customer management<\/strong><\/p>\n

Grubhub, in 2016, launched a new product for its restaurants \u2013 Grubcentral – designed to provide actionable insights to restaurants based on the orders fulfilled by the restaurant. This is aimed at helping restaurants optimize the inventory, delivery footprint, pricing, etc.[2]<\/a><\/p>\n

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Drivers: Order fulfillment<\/strong><\/p>\n

While Grubhub\u2019s website and 10K do\u00a0not mention this aspect explicitly, they will need to revamp their delivery and order tracking system. This again can leverage data science. Based on the past order delivery data collected from drivers and time of the day, weather, etc., Grubhub can predict the ETA in real time. They can also accordingly optimize the delivery route for the drivers enabling them to do multiple pick-ups and deliveries on the way, similar to Uber Pool model.<\/p>\n

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Industry: A Year in Delivery[4]<\/a><\/strong><\/p>\n

Being the market leader, GrubHub also has been a pioneer in publishing the major trends in the food ordering industry in United States. This interesting press release by the company mentions how Grubhub analyzes orders by date and identifies the most frequent dishes by popular events like Game of Thrones finale, President election, etc. The study also published geography\/ city wise popular dishes and other similar user habits, as shown in figure 4.<\/p>\n

Here\u2019s a video on \u2018A Year in Delivery\u2019:<\/p>\n