How Fake Orders Are Screwing Food Ordering Business
In 2015, Food tech was the most celebrated sector with investors funding amounting to USD ~300 million. Then came the downturn in 2016 which witnessed closure of Dazo, Peeto, TinyOwl, SpoonJoy and several others. Recent news of $15mn funding of Swiggy is being touted as somewhat initial signs of reviving confidence in the sector. I believe food sector has got its due course correction and from now will increasingly give rise to newer innovative models and entrants, while current ones will move towards stabilization.
Being an integral part of food tech , my numerous interactions with Foodpanda, Zomato, Faasos have unearthed a niggling issue that still plagues the online food delivery players — Fake Orders.Fake Orders are those orders which are intentionally done to either hurt the player (by other competing players) or are done by testers who test various websites for bugs/errors. To make it more clear, fake orders are not those orders which happen due to change of customer decision post placing the order and customer voluntarily canceling the order. In fact , even players like Dominos and Pizza Hut, ( which receive more than 60% of their orders over phone) have been experiencing fake orders too.
Although fake orders are prevalent across the e commerice business, they are more scary for food tech companies since food is perishable and can’t be sold to some other customer in most of the cases (cant be stored beyond a certain time).
Basis my interactions, the amount of fake orders range anywhere 5–10% of total orders received by the online food delivery players. These orders hurt the most as in most of the cases, kitchen has done their part of making a sumptuous meal and delivery boy has reached the delivery address to realize that there are no takers for the order at that address. All the resources including the chef’s effort, ingredient used, cooking process, packaging, delivery boy’s effort , fuel consumed etc have been utilized already finally leading to nothing. Bigger chains such as Dominos, McDonalds etc have policies wherein they discard/throwaway these unowned orders and don’t sell them again. However, some smaller kitchens who can’t afford such luxuries decide to resell them and in some cases without even telling their customers, further leading to dissatisfied customers and fall in repeat purchase. They might have won the battle but lost the war.
Number of orders is the most important metric for everyone
Every food tech company or internet kitchen has few basic metrics to track (1) No of orders (2) Cost of Acquisition (3) Repeat Frequency (4) Avg Ticket Size. Of of these , No. of orders is the most important metric for food tech companies, which gets reported to investors to prove that their product is scalable. Some companies report their numbers net of cancelled orders, however some do this correction at the time of auditing of financials. This over reporting or wrong reporting is a cause of concern for VC/Investors, which also came out publicly in case of Foodpanda.
What measures do companies deploy to counter such fake orders
Most of the companies don’t take this as a serious enough issue, however everyone agrees that they face this issue on a daily basis. I checked up with few companies on what measures do they deploy for the same:
1. Customer Registration — Some players have a mandatory customer registration process prior to placing the order. However there are exceptions to the case with leading players such as Freshmenu and all restaurant wesbites built by limetray.
3. Call centers — Bigger companies such as Freshmenu, Faasos, Dominos etc deploy call centers to confirm orders before processing the same. Ironically, Calling and confirming every order just questions the genesis of online order as a business model.
4. Ban the Customer– Very few internet kitchens have gone to the extent of banning such users from placing any order on their platform. However, Ordering platforms such as Zomato, Foodpanda and Swiggy are not in a position to take such a stern action.
Magnitude of loss
On demand food ordering does more than 1.1 lac orders a day with an avg ticket size of Rs. 200 which makes the daily gross order value to be Rs. 2.2 Crs a day(,Rs. 700 Crs annually) . Even if I take a conservative estimate of 5% fake orders , it translates to mind boggling figure of Rs. 35 Crs annually. Along with the industry, this figure also continues to grow year over year.
Breakthrough Ideas to counter Fake Orders
1. Usage of Predictive Analytics — Rather than calling every customer , companies should use predictive analytics to identify orders (extremely high order value , too many orders within a short span of time , unprecedented as per the order history) that are beyond a threshold score to be passed on to call centers for confirmation.
2. Common Keywords for Bug Testers — Bug testers are critical resource to developers as they help build more secure online world whether be it in terms of customer information or transaction security. These bug testers have no interest in food or harming the company by placing fake orders, however in their process of finding bugs, they place such orders which are not real.In order to avoid getting misguided by them, Companies can assist bug testers through their console area of source code by mentioning “Use code “BUGFOODIE” in case you are trying to gatecrash to help us save food.”
3. Tracking Real Time Bounce of Emails — Most of the food ordering portals send a confirmation/receipt mail to the email id used for placing the order. Companies should have a real time monitoring of whether the mail has bounced to the receipient, which is a good indicator of a fake transaction.
In case your company deploys any such novel technqiues to counter fake orders, do let me know at firstname.lastname@example.org