Analytics applied to Mobile Food Tech Apps

Contents

• Introduction
• Indian Market Share in Food Tech
• Spending Patterns
• Eating Patterns
• Various useful Analytics
• Pros & Cons

Introduction

In today's modern times people are so tech savvy that they are extensively dependent on their smartphone to make choice of what they should eat and which restaurant they eat at. People select their favourite dishes from variety of menu's of every possible cuisine out there. People easily pick the restaurant to eat at by checking other people's reviews on many restaurants thereby making their decision very easy. Then there are deals and coupons and offers all the time everywhere. There is homemade food, home deliveries, table reservations and more at the tap of a button on your smartphone. With emergence of such food tech as they call it, it has also become very difficult for "mom & pop" shops and old school merchants which are not online to run their business.

Indian Market Share in Food Techs.

The market size for food in India, which was estimated at Rs 23 lakh-crore in 2014, is set to reach a whopping Rs 42 lakh-crore by 2020, as predicted by Boston Consulting Group. Another special trait of the food industry is that many people strongly believe that it tends to fare better in times of an economic crisis because of the ever hungry consumers’ desire for more variety.
Lets face it! People eat. And they eat thrice a day. Expats and migrants from other states tend to eat out more and spend money on food.
The food tech apps have bridged the gap between restaurants/eateries and tech savvy consumers, the couch potatos, the deal grabbers and more...
VCs from the United States and all over are heavily focused on the Indian food tech market with 3.5 billion dollars spent in the first few months of 2015 alone..


Spending Patterns

In the western countries the spend versus volume is lower than in countries like China and India but the spend makes for it. According to a survey about consumer expenditures in the year 2013 it has been estimated that the average person spends around $6600 annually on food. In 2014 the expenditure increased by 2.4 percent. In countries like India, sheer volume beats the price hands down.
It is also estimated that the expenditure of the average person on food will only increase in the coming years. Consumers spend 10% of their disposable income on fast food every year. With these numbers we can clearly come to the conclusion that the average person does not think twice before spending on food which can satisfy his appetite.
Patterns of spending include Cuisine patterns, Rate patterns, Luxury, Ambience, Taste, cost and more. Analytics on each of these items can be very rich and with all the tapping of the industry there is still so much more to explore and exploit.

Eating Patterns

When it comes to the eating patterns of the average person, a survey conducted in  the year 2011 the average person consumed nearly one ton of food. That’s 1,996 pounds of food a year. At least 1 in 4 people preferred to eat some type of fast food every day. The study also revealed some other interesting numbers as well, People ate: 632 lbs. of dairy products (including 31.4 lbs. of cheese), 415.4 lbs. of vegetables (most popular being corn and potatoes), 273 lbs. of fruit, and 183.6 lbs. of meat and poultry. Over a whopping 10 billion donuts are consumed in the US every year. Similar numbers goes to Idlis and Parathas in India.

Various useful Analytics

Analytics are important for both merchants and consumers and especially the merchants to drive their business to success and increase footfall.
The web and mobile apps of the food tech industry collect a wealth of information about eating and spending and choices and preferences of people that it can be used and customized to increase revenue easily. A simple analytic can tell that in a specific area filled with college kids and IT folks a specific type (cuisine) food will fly higher versus the other., for eg. Burgers and Pizzas and fast food. People distribution across the city, number of people eating out within 2-3 kms or 5 kms, average spend per person in the locality, affinity to offers and deals, preference for reservations and value added services etc
With the churning of data comes useful analysis. In a locality teeming with teens and college students, buy 1 get 1 offers, 20% discount etc runs very well and footfall can be increased thus increasing revenue. Friday evenings are more popular for pub hopping and fine dining and merchants can create customized deals & attractive offers which is appealing to the consumers.

From a consumer perspective, data related to reviews and ratings help making the right dining choices, get to know their savings by using deals and offers etc.


Pros & Cons of Analytics and Food Industry

There are a wide variety of food applications available these days. The consumer can select the restaurant based on the rating provided by others. The applications these days are smart enough to remember previous food orders and a customized menu can be shown to the customer, which will only contain foods the customer specifically prefers.
Many applications offer coupons, deals, which the customer can avail and order the food at special prices with discounts. Lot of data like amount saved in a month/year etc can be shown which can be interesting to the consumer. This can also ensure that your appetite does not burn a hole in your pocket. All these benefits right at the comfort of your couch. Moreover this can ensure better consumer satisfaction. Merchants can get access to a whole wealth of information for better revenue models and advertising spends.

But there is a flip side to the whole thing as well. Some people don’t support the idea of application saving their preferences, many strongly look at it as a intrusion of privacy. We can also say that the advancement of technology induces the customer to a very unhealthy lifestyle and the customer may also lose touch with the outside world, by not going out to eat more and not socializing enough.