Possible innovations in Indian Online Food Delivery sector

Indian online food delivery market is expected to hit $8 billion by 2022 from $4 billion in 2019, as per the Jan 2020 report by BCG and Google.

Please check the report here for more insights : https://image-src.bcg.com/Images/Demystifying-the-Online-Food-Consumer_tcm21-238295.pdf

Indian food tech space is characterised by a duopoly of Zomato and Swiggy with market shares of roughly 50% and 40% respectively. The former acquired Indian operations of Uber Eats in start of 2020 , thus gaining a lead over its close competitor.

Following are some suggestions for next possible basic innovations in the Indian online food delivery space that could enhance the user experience

1 , Ordering System innovations at restaurant

Currently restaurants log into multiple apps , one for each delivery partner and also have a seperate billing system.

Many outlets typically keep multiple smartphones or tablets for tracking the orders from different food delivery service proivders

The following process improvements can be achieved if restaurants have a single software to manage their billing (for both online and at-restaurant orders ) and for tracking online orders from various food ordering platforms :

1.a , Billing software similar to ERP system to accurately track the available quantity of each dish at a restaurant :

• Most restaurants plan the quantity of each dish to be made on a day while purchasing the ingredients at the start of the day or a few days before.
• Restaurants should have an option to input these plannned quantities of each dish at the start of the day into their billing sytem.
• Each dish should have an available quantities indicator that can be seen by ordering users. With each new order, this quantity is auto updated.
• Such a system will also enable auto confirmation of online orders based on the available quanity instead of the existing manual verification.

1.b , Option to pre-book food much ahead of the desired delivery time , with assured availability of confirmed orders

• Given an option, users would like to make their lunch or dinner plans a few hours earlier or even at start of a day instead of just before eating time.
• Current advance ordering systems merely save orders and then release them to the restaurants close to delivery time and so lack assurance.
• For restaurants with ERP like billing sytem, customers should be allowed to order food at any time of their convenience and choose when they want it delivered, thus giving customers an option to order food in advance
• The billing system should reduce the total available quantity of a dish based on such pre-bookings and allow sales of only the remaining quantity for any orders received after that. This gives assured availability of pre-ordered items at the time of desired delivery, even if ordered much ahead

1.c , Accurate estimation of preperation time based on standard dish preperation methods and other simultaneously active orders :

• Based on preperation style and time, dishes can be roughly categorised into following types :

i, Pre-cooked in bulk and packed on order . Eg: idli, poha, thali meals, etc

An example of a restaurant that already cooks the items on menu and keeps them hot and ready to pack

ii, Cooked on order, with short cooking time. Eg : Paratha, Dosa, Pizza at QSRs, Shakes . For such items, more than one order can be cooked parallelly

iii, Cooked on order, with long cooking time. Eg : Dishes in premium restaurants. Each item is individually made and may have around 30 mins cooking time.

• The billing and order management system should give an option for a restaurant to input what is the preparation style (bulk or individual) and preperation time for each dish and how many quantities of an individual dish can be parallelly prepared.
• When an order arrives, an accurate estimate of preperation time should be generated based on the dish category and other parallel orders, etc .

1.d , System generated auto orders to create express checkout option for most in demand items during the heaviest demand slots:

• Based on past ordered items and the time slots in which they were ordered, the billing sytems should be able to offer a rough prediction of potential demand for some of the main items during the busiest slots
• The system can create auto orders for a small percentage of the predicted orders so that the restaurant can keep them ready for express checkout.
• When a customer order arrives for such a high demand item, the system can assign a customer order to an already pre-packed unit , thus creating shorter delivery time and better customer experience
• Food delivery apps can optionally pre-place a pool of delivery staffs near a set of restaurants with express checkout items during the busiest slots

High demands items can be kept at express checkout counter and picked up instantly by a delivery person when a customer order arrives for such an item

2 , Clubbing of multiple orders from the same user or from different users in nearby areas

Order clubbing can increase the profitability of the food ordering apps and the margin can also be passed on to the restaurant partners and users :

2.a , One user can order from multiple restaurants in a single order

• Restaurants should be divided into approximate clusters by considering restaurants in nearby locations as a single cluster.
• When a customer places an order from one restaurant, the user can also order from other restaurants in the same cluster via the same order
• The delivery person can be suggested a suitable order of picking from the multiple restaurants, based on the preperation time of the dishes
• User will get all orders from all restaurants delivered by one person
• Users need to pay only a single fee instead of multiple delivery fees

2.b , Clubbing of orders from different adjacent restaurants by multiple users in the same apartment complex or nearby areas

• Curently orders from different users are only clubbed together in a single delivery if they are from the same restaurant at around the same time.
• As a next step, the above restaurant cluster concept can be used for clubbing of orders from nearby restaurants from different users
• The food ordering apps backend should club together users who are in nearby buildings or streets. All orders from such users in a certain time window from restaurants in the same cluster should be clubbed together.

2.c , Custom lunch menu for office parks, where advance orders from different users are delivered together in the cafeteria

• Food ordering apps should have a different interface and approach for taking orders from users at office locations like large corporate parks
• Users in each location should see a curated menu from select restaurants that can be ordered upto 1 hour before the delivery time.
• Few restaurants can be permanently chosen and other restaurants can be onboarded to this curated list on a rolling basis.
• Orders from hundreds of users will be delivered together in mini vans
• When orders are at the office location, users will be alerted via the app and can pick up from the cafeteria. A staff can be assigned for confirmation.

2.d , Tea runs that can accept pre-orders and instant orders

• Another possible approach for delivering lower priced items like teas, coffees, snacks, etc in a profitable manner is to use a tea run approach
• Multiple flasks of hot beverages and some quantities of popular snacks are carried by a delivery person in a few designated routes that have many office buildings or residential complexes
• The tea runs will be during desginated 30 mins slots like 9:30am, 10am, 4pm, 5pm, etc during the typical tea break times
• Users can pre-order for an upcoming tea run or see if any unbooked flasks are available in any of the active tea runs in their areas and then book.
• Delivery persons will be placing the items at front desks or security gates for self pickup by users so that they can service multiple orders quickly

2.e , Area based groupon type large discounts on select dishes for group buying by users

• Food ordering apps can provide a single dish or few dishes in a groupon like discounted manner. Users who opt for any of these dishes via clubbed delivery should get major discounts.
• This can be typically run for dishes matching a day’s special event. For example, Bengali dishes during Durga Puja festival, etc
• Restaurants can bid for the opportunity to offer the featured dishes

2.f , Ideal format of clubbed order delivery for max profitability

• This is a suggestion of an ideal max target for clubbing of orders and the resultant increased delivery person payments, restaurant profits, etc

Example of a delivery person who uses a solid bag which is ideal for stacking of multiple orders

• Ideal max clubbing for pre-booked items : 50 packs of price INR 100 each .
• All these should be delivered by one delivery person in a 2 hours window
• Restaurants pay 10% (INR 10) to the food delivery platform & get INR 90
• Each user should pay INR 10 as delivery fee on top the price of INR 100. So total price a user for such an order box is 100(price) + 10(fee) = 110
• Margin per trip for ordering app = 50 boxes * 20 rupees per box = 1000
• A full time delivery person will work for 10 hours = 5 * 2 hours = 5 trips
• Margin per person per day = 5 trips* 50 boxes* 20 rupees per box = 5000
• Margin in 30 days per full time delivery person = 30 * 5000 = 150,000
• Salary to delivery person + bike costs + insurance = 50k per month
• Allocation for part-time crew fees + delivery hub rents, etc = 50k pm
• Revenue to food ordering app after all payments = 150k -50k -50k = 50k
• Approximate delivery staff per major food ordering app = 100k
• Monthly revenue potential = INR 50k * 100k = INR 5B ~ USD 60M
• Annual revenue potential = INR 60B ~ USD 720M

3 , Enhancements in user’s ordering screen

Food ordering apps can upgrade the user experience from the current format of showing a list of restaurants and dish suggestions as follows :

3.a , Comparison of mulitple food carts from different restaurants

• Currently users can build only 1 cart with items from 1 restaurant.
• Give a user an option to create multiple baskets with items from different restaurants so that she/he can compare based on price, expected delivery time, etc and take a final decision from the created options
• UI/UX wise the baskets should float around on the screen as small bubbles so that the user can click on any one to expand it and see full details
• If some of the baskets built by a user are from restaurants in the same cluster, then the user can also be given option to order from those multiple baskets from different restaurants.

3.b , Nutrition planner

• An overall list that helps users to decide based on variety and nutritional recommendations, festival types, etc
• The food ordering app can track the items a user has consumed in the past days and suggest variety based on recommended nutrtional inputs

3.c , Multiple modes for browsing

• Basic mode : Standard option currently provided by food delivery apps
• Repeat mode or Quick order mode : Suggestions based on past purchase history . The app can derive the user’s weekly or montly purchase pattern and suggest quick order options accordingly.
• Wow mode : Dish based browsing with photos and short 5 sec videos that are posted by the restaurants as well as by other users . This is aimed more at incentivising users to try dishes they have not tried before.

3.d , Sweet box ,Cooler Section, Hot kettle, etc on checkout screen

• Many restaurants that cater to a particular genre of food items do not have a wide vareity of sweets and beverages in their menu
• While having lunch or dinner, users may prefer to have sweets and beverages to accompany the food , but they are restricted to ordering only those that are made or stocked at the restaurant they are ordering from
• Instead users should be shown a wide selection of sweets and beverages at the checkout screen so that they can order what they want
• Food ordering apps can create tie-ups with bakeries, grocery stores, dessert and beverages outlets to provide the menu options
• The delivery person should pick up the ordered item from the partner store that is nearest to the user’s location

3.e , Video call based ordering or customisations

• Many QSRs offer customisable combos from already cooked item. Eg: Eat.Fit. Currently users can customise from the app screen
• User experience can be improved if they can in person instructions regarding quantity of each items they want and also option to see the dishes live and then decide what options to choose.
• Allow users who pay an extra fee to have video call with the restaurant , via the food ordering app, while there order is being packed

Salads, combo packs, etc are typically made from multiple ready to eat ingredients and are prepared on arrival of a customer order. For such items, an option for a customer to video call and give instrucations could be ideal

3.f , Wishlist of items that are currently not available

• Many speciality items in low demand may not be available frequently
• Give an option for user to add non available special items to a wishlist
• Restaurant can see such wishlists and then confirm the order if they can

3.g , Option to choose reusable containers like Milton, Cello

• Tie ups with container makers like Cello and Milton to introduce reusable containers for delivery to ensure more freshness of delivered food when it reaches the customers hands, be it hot or cold type food.
• Customers need to pay an additional container fee for such orders
• An area based reverse supply chain system to collect back the used containers at the end of a day from multiple users in area who have ordered food in such containers
• Containers to be cleaned and reused again and kept at busy restaurants

4, A single user ordering on behalf of multiple users

A better UI/UX experience is needed for frequently ordering for a group like the members of a household, office colleagues, room mates, etc

4.a , User profiles and user groups

• An ordering user should have an option to create a group of user profiles
• Each member of an ordering group should have a profile with food preferences , allergies, etc. Other users of the app should be able to share their profiles with another user who wants to order for them.
• A user can create multiple groups for different purposes like family members, roommates, office colleagues, etc

4.b , Chat and select option

• Make a shareable template for major chat apps like whatsapp, messenger
• The ordering user can then share a set of options with others via any of the chat app
• Other users should be able to +1, add customisation comment , etc for the options and the curator should be able to directly submit this to the food ordering app instead of having to manually enter the same in the food app

4.c , Curated orders to a venue for events, etc

• An in app option for organiser or admin to create a curated list of available options for people attending an event
• Ideally the options should be shareable in other apps through which the attendees RSVP for the event
• Option for the curator get choices of the users and the submit the order in bulk to the food ordering app
• For large number of attendees, the curator should be able to provide menu options from multiple restaurants and not just one restaurant.

4.d , Multi location ordering option (like people in a zoom party)

• Multiple people in different location can chat and see the same screen and order together
• One or few admins can plan the menu and choose the delivery locations
• Assured delivery of items in set time bucket. Use of buffers to ensure similar time slot delivery
• A polling option to see and choose menu. Single payment and confirmation

4.e , Custom offers for each user group instead of same general offers

• Dynamically generate combos and offers that will incentivise the user instead of one size fits all offers

5 , Delivery hub and spoke model innovation

Hub and Spoke model can be employed in Indian food delivery space :

5.a , Local hubs for storage of food for customer pick up

• Hub is a place for customers to pickup their orders instead of being delivered directly to their houses. This avoids the customer having to coordinate with multiple delivery persons for multiple orders.
• In large residential and commercial buildings, electronic lockers like Qikpod can be setup near the main entrance gate.
• Another option is to sign up local shops with hot and cold store options
• Ideally there should be a hub within 5 mins walking distance of a any user

5.b , Part-time delivery crew of local residents , college students

• Delivery spokes can be added to the hubs by using local residents and college students who are interested in part-time jobs for picking up orders from a hub and delivering to the houses of customers
• Such deliveries are of short distance and so done by cycle or by walk
• Ideally employed during peak lunch hours and peak dinner hours
• Roughly a part-time delivery person who works 2 hours per day for 22 days in a month should be able to earn around INR 5k per month

5.c , Express food packs at hubs for pick up without ordering

• Few quantities of the most popular dishes in an densely populated area can be kept ready at a hub , with appropriate hot or cold storage
• While opening the food ordering app, a user can see such pickup options available , if any , at the nearest hub and book
• A user can also walk in to a hub, see available options and then buy.

Hot storage shelf used by snacks counters. A smaller version of such a container that can keep packed foods warm can be kept at shops that sign up as pickup counters

5.d , Assured basic delivery mode during bad weather by using hubs

• Currently during rainy weather, the delivery process breaks down with many restaurants shown as not serviceable. Also bike delivery will take more time than usual and so all customer orders are delayed.
• Clubbed order delivery using Auto Rickshaws : Users can be provided an option to order via a clubbed delivery and several user orders will be delivered together by an auto rickshaw. The food delivery company should have a set of auto rickshaw drivers who are part of delivery during rains.
• If the order is deliverable only to a hub, then the user has the option to pay a bad weather convenience fee to get it delivered to home by a full time delivery person or a part-time crew member

6, Introduction of home chefs, reviewers, curators , etc in the platform

6.a , Home chefs who can prepare user odered dishes

• Home chefs can sign up on the platform by indicating the kind of dishes they can make on order. For a user, this experience is like having home cooked meals , but without having to hire an onsite cook at her/his home.
• When a user wants a home cooked meal or a custom dish that is not in standard restaurant menus, she/ he can give the order in the home chefs section of the platform a few hours in advance and get it delivered.

6.b , Home chefs with own menu items

• Home Chefs can also sign up for selling own menu items, like a restaurant
• The maximum orders that they can take in a slot must be restricted to ensure adequate service delivery to the interested users

6.c , Food enthusiasts to post reviews for new restaurants

• Onboard the most active users of the platform who give both positive and negative reviews in a balanced manner, as amateur food critics
• Invite them to try out new outlets or less reviewed restaurants on the platform for free and to post pics and reviews.

6.d , Curators who could help users to try new premimum dishes

• Platform could offer the service of food enthusiasts who can help users to try out new experiences by suggesting restaurants and dishes.

6.e , Gourmet experience at home

• Option to bring a premium restaurant experience at home by offering the assistance of a restauarant crew for serving the ordered food.
• Unlike a catering service, this is meant for a small number of people like a small family or a mini gathering.

7, Kitchenless restaurants and eating spaces

7.a , Restaurants with no kitchen

• Eating areas can be set up in major commercial or residential spaces where people can get tables and chairs like in a restaurant and they can order food via a food ordering app from a set of nearby quick service restaurants
• This is to cater to the user need for having the experience of going out instead of eating at home or office, but without being restricted by the limited menu items of a standard restaurant or food court.
• For busy timings, users can pre book seats like a restaurant.

7.b , Kitchenless hotels and lodges like oyo rooms, etc

• Food ordering apps can tie-up with lodges and budget hotels to provide a a limited menu options that will be delivered in a quick manner
• This helps such hotels to eliminated the cost of maintaining a kitchen
• The customers of such places get the advantage of having a variety menu instead of the limited items available in budget hotels with own kitchens

8, Innovations to help restaurant partners

8.a , Temporary kitchen and restaurant staff on hire

• The food ordering plaforms can train and onobard a few kitchen staffs for standard restaurant activities. These could be part-timers or full-timers
• The restuarants can then hire such staff from the plaform for some peak days or weeks or even just for the busy hours during a particular day
• Restaurants contribute to their salaries based on the duration of hiring

8.b , Option to accept orders for non menu items from users

• Customers can create a wishlist of items that are not available for ordering from any restaurants, but they would like to have
• Restaurants or chefs can see such orders and place their interests
• Example : Traditional foods that are eaten during certain festivals

8.c , Advanced ordering from far away restaurants for weekends and special occasions

• Food ordering platforms typically restrict a user’s ordering option to restaurants within a certain distance limit.
• Some premimum restaurants or renowned traditional outlets in a city may have only one or very few physical locations. Customers in far off location should be able to order from such places a few hours or days in advance
• Deliveries in such long distance cases be done via three wheelers

8.d , User preferences map for new restaurant opportunities

• Food ordering outlets can publish a map of customer orders based on genre of foods and the major locations from which demand arises
• They can create an addtional layer to indicate potential new restaurant opportunities and the food type missing in any particular area.
• Restaurants can access these details for a price to know new opportunities
• As a next step, food ordering players can provide ready to use real estate and equipments on lease to speed up the creation of new restaurants that could serve some urgent opportunities. Can give preference to women Self Help Groups

9, Improvement of customer hand over time

9.a , Option to give easily understandable delivery instructions

• Food ordering platforms should identify a set of easy-to-locate universal landmarks in each pincode area and list them in the add address section. These are the places that even a new delivery person can easily locate.
• When a user adds a new address , in addition to creating a pin on the map, she/he should also add instructions to reach the location from the nearest universal landmark from the above list
• The instructions should be added by choosing from a set of standard micro instructions like turn left, take stairs, etc that can be translated accurately to any language for aiding the delivery person

9.b , Incentivise users for speeding up delivery handover time

• Users should be encouraged to speed up the handover time from when a delivery person reaches thier location to the customer taking the delivery.
• For users who enable very fast handovers by meeting the delivery person at the map point or by using delivery hubs, give them micro discounts for their next orders or free premium samples from FMCGs, etc
• For user’s who cause excessive handover delay for multiple times, introduce small penalties like disabling of discounts for next few orders

10, Enhancement of earning potential

10.a , Sharing of delivery staff with other food delivery players during peak hours to work in a hub and spoke model

• During rush hours and option to share delivery persons between different delivery players to improve user experience in different plaforms
• Ideally use it in a hub and spoke model , wherein the final delivery touchpoint to the customer will be a person from the relevant ordering app

10.b , Sharing of delivery staff for ecommerce delivery , grocery delivery during non rush hours

• Allow delivery person to earn additional income by taking workloads for ecommerce delivery, grocery delivery during non rush hours

10.c , Tie ups with social commerce platforms to replace 2 hours of meal preparation time of stay-at-home women with part-time jobs

• To increase employment rate and income among stay-at-home women, food delivery platforms can tie-up with social commerce players
• Women should be incentivised to replace the approximate 2 hours of meal preperation , that includes vegetable shopping, actual cooking and cleaning of utensils, with 2 hours of part-time phone based jobs.
• They should have economical options to outsource their cooking to nearby restaurants that make similar types of dishes with some monthly packages.

AppMap (https://appmap.xyz)

Ideal for viewing on laptops or tablets . This page offers a summary of the apps used by consumers in different countries around the world . It lists the main food ordering apps used by users in some of the largest economies. The list is not accurate and is meant to give a rough idea. Uber Eats , Just Eat Takeaway.com and Delivery Hero have presence in a large number of countries around the world either directly or by acquiring major players in different countries. Doordash, the top player in USA, also joined the global expansion spree by acquiring Finland based Wolt in Nov 2021.