Food industry is adopting big data technology and big data analytics to stay competitive by understanding customer preferences and tastes. People have pretty high expectations for food especially when they are ordering it from a restaurant. The mind-set of people is tuned in such a way - that they want each and every bite of the food to be perfect in taste whether it is the cheesy pizza, their favourite Ham, frozen lasagne or the fries. Restaurants and food delivery apps are revolutionizing the food industry in novel and surprising ways to make sure that the food tastes the same every time and is always on time. Here are some examples on how restaurants and food delivery apps are achieving an increment in revenue by adding big data analytics on their menu to understand customer tastes and preferences.
With the growth of online food delivery industry, the investments in the food delivery business have increased exponentially - from 25 million dollars in 2012; 46 million dollars in 2013; 600 million dollars in 2014 and it was expected to touch 1 billion dollars by end of 2015. Every food delivery chain, restaurant, grocery store, and cafeteria- all businesses in the food industry generate data in the form of customer orders, delivery location, GPS, tweets, images, reviews, blogs, updates, etc. The data generated relates to average wait time, experience with the delivery, taste of the food, menu availability, loyalty card points and product inventory levels.
As mobile trend moves rapidly, food delivery businesses are now combining all the unstructured big data with transactional and sales data for tendency and sentiment analysis – to leverage mobile app analytics that can help them build brand image and affinity towards customers, thereby increasing sales.
Big Data on the Restaurant Menu to increase ROI
With 6 million registered customers in 21,000 stores across 62 countries, 87,000 possible drinking combinations and serving 4 billion cups annually - Starbucks, the popular coffee maker is grinding petabytes of data to leverage big data analytics for refined customer experience. Starbucks collects data on - when and what customers order to provide them with personalized offers on their favourite coffee.
As people become more health conscious about what they eat there is huge demand for fresh and unprocessed items on restaurant menus. McDonalds, the biggest fast food chain is having a tough time rendering a healthy food experience to its diners. In 2015, for the first time ever, since its inception in the US, McDonalds has closed more stores than it has opened. They have had to close 350 stores as the analyses of the stores’ revenue indicated that the poorly attended McDonald outlets, are affecting their revenue results.
Mike Donahue, former chief of communications for McDonald's said-“Closing of these stores will help with overall strategies to expand the business. The only thing that stops growth is relevancy to the customer.”
"As the world's leading Restaurant Company, we are evolving to be more responsive to today's customer. McDonald's management team is keenly focused on acting more quickly to better address today's consumer needs, expectations and the competitive marketplace."- Said Steve Easterbrook-CEO of McDonald’s.
Big Data Analytics in the Food Industry
Quality of food being of prime importance to customers-food chains like McDonald’s, Starbucks, Burger King, Costa Coffee ,Chipotle are leveraging predictive analytics in the food industry to stay competitive. There are many food delivery startups struggling hard with big data to gain competitive edge in the battle of the food delivery apps.
1) Big Data Analytics at GrubHub
Chicago-based GrubHub is making big moves and has entered the on demand food delivery wars by tapping into big data. When the dining halls are closed and the fridge is empty-GrubHub has a special place in the heart of hungry diners. With 5 million active diners, 30,000 take out restaurants across 700 cities, open 24x7 and filling an average of 150,000 orders everyday –GrubHub is using analytics to get insights into the American Stomach to fulfil every food desire. GrubHub uses data about millions of orders made on their platform to help hungry diners choose the right dish from the right restaurant. GrubHub’s revenue increased to $253.9 million from $170.1 million in 2013, a whopping 49% increase- all thanks to big data analytics.
“Pizza is the only thing that can bring Men and Women together”. So next time on a date-do order a Pizza. GrubHub’s stats on Pizza consumption reveal that –Pizza is the definite food choice among the Americans that transcends sex and unites men and women. Men and Women order Pizza at the same rates besides fries and soda. GrubHub’s data analysis reports confirm that women eat healthier than men. GrubHub is leveraging the results of data analysis based on demographics, gender, age, seasonality and ethnicity to provide its hungry diners with lip-smacking food that they are likely to enjoy on-the-go.
“We have a much greater focus on discovery for diners, but we want to more and more surface the quantitative data. Qualitative information is great, but quantitative can shows you what your neighbours love and I think that’s where the future of discovery will be.”- said Matt Maloney, CEO of GrubHub.
GrubHub uses big data to identify patterns in takeout ordering, find the differences between gender specific tastes. This analysis helps restaurants make best use of the menu by providing recommendations and suggestions to diners on what kind of food they are likely to enjoy.
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2) Big Data Analytics at Munchery
Having delivered 3 million meals till date, within 20 to 40 minutes-Munchery is shaking up the food industry by tapping into the data to deliver tasty and quality chef-made gourmet take-out food to its customers. Munchery does not feature the same menu in two cities. Each chef at Munchery specializes in a different cuisine and they have the menu or cuisine that is popular in that city. Munchery website has menus uploaded on their website so that customers can leave feedback about a particular menu (similar to Amazon product review). Munchery leverages this data for analysis to identify what menu items need improvement or which items should be eliminated from the list. Big data at Munchery helps them refine their delivery menu right from the dishes, ingredients and flavours to best suit the needs of their customers.
Munchery uses Desk.com- a platform developed by Salesforce which connects with Munchery software to track the food preferences, order history of customers, social mentions about the company on Twitter, Facebook and other social media feeds. Predictive analytics on this data, then, helps Munchery to spot and respond to macro taste trends like the ‘Kale Mania’.
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3) Big Data Analytics at Sprig
Sprig- the “Uber” of healthy meals, delivers 100% organic food in 15 minutes or rather it seems like, it delivers orders much before they are placed. The secret ingredient behind the quick deliveries is the big data analytics used to estimate how many customers will order a particular menu item, when and from where. The wow factor as to why people love Sprig is because they proactively position their supplies through big data analysis, to make sure that the delivery time is low. Data science team at Sprig does heuristic predictive analysis to ensure that customers enjoy delicious food in short span of time. What more does a hungry stomach need than food that is hot and tastes good and shows up in just 20 minutes? Nothing apparently.
“There are so many variables when it comes to food that it makes it a harder data problem to solve and a more interesting one as well. We’re concentrating on creating those capabilities on our engineering team so we can do the hard engineering to be as efficient as possible about where our supply goes.”- said Angela Wise, Head of Product at Sprig.
4) Big Data Analytics at DoorDash
The secret recipe driving the rapid success of this start-up, is the data driven approach it follows for food delivery. DoorDash uses big data analytics for everything - right from plotting variables like time of the day and stock inventory to predicting the expected demand and food preparation time. Big data is the key ingredient on the menu of DoorDash -
- The capacity planning algorithm uses big data to predict how many drivers would be required on duty on a particular day.
- The self – learning dispatch system at DoorDash combines machine learning and big data technologies to adapt itself with the constantly changing demand.
- DoorDash’s data driven approach is smarter than humans as it also focuses on food preparation time based on analysis. This helps them ensure that the drivers don’t arrive too late to pick up the food so that it is cold when delivered to customers nor do they arrive too early.
- The logistics software at DoorDash is a combination of machine learning and big data technologies which helps them provide on-demand deliveries that are efficient, fast and affordable.
5) Big Data Analytics at Just Eat
With 200,000 pages of browser history and 400,000 customers -Just Eat is a data pioneer leveraging predictive analytics to understand what customers actually expect from their service. Big data analysis at Just Eat, helps predict what kind of food is likely to be ordered at a specific time and what is the inclination of the customers towards a particular cuisine. This analysis is driving 50% growth in UK and three-fold growth in other European markets.
For instance, using the big data, generated at Just Eat - analysts can predict which areas are most likely to order healthy food or which areas prefer food collection over delivery.
Big Data Analysis results, on food eating patterns and trends, are provided to restaurants to help them cater to a variety of demands and increase the choice of items on the menu which can help them capitalise on growth.
Can we make new recipes with Big Data?
There is good news for all the foodies who do not want to eat the same meal again- Big data and Data Science are all set to revolutionize the way in which new restaurants and eateries come up with novel ideas for recipes that would savour the taste buds of customers.
IBM’s Chef Watson is all set to generate new recipes. The program runs in 5 steps to make sure that the recipes are creative, unusual and savour customer tastes.
- The user can select the variety of recipe they want to eat by just choosing a specific cuisine, the main ingredient and the kind of dish.
- The program works by scanning the big data repository that has all the relationships between various ingredients, various chemical compounds, customer flavour preferences, etc.
- The computer program then generates new and creative recipe ideas based on the inputs provided by the user in step 1.
- Based on quality and uniqueness- the best recipe idea is selected.
- Finally, the recipe is created and produced to the Chef so that they try it out in the kitchen.
As the on-demand marketplace grows, food delivery businesses will need to quickly capitalise all the data that they have on various demand patterns, food preparation time, delivery routes and more - to optimize their services and gain a competitive advantage. Restaurants and food delivery businesses that are not using big data analytics are missing out on a lucrative opportunity to increase their ROI and gain customer satisfaction. If the food industry cannot hoard their big data now then it would be difficult to go back, reconstruct the data and analyse it.
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