Groupon’s stock price is down to 13.5% of their IPO, LivingSocial did a down round and laid off over 80% of their employees, and Amazon Local shut down its service. The deal space seems to be dying. But why? It’s because they forgot about what most people want. Let me explain.
“[The] daily deals business model was broken and [we] predicted that it would be unsustainable”
This is how a deal works: a vendor drops their price by usually around 50%. For every deal used, half of the remaining 50% goes to the deal site, and only 25% stays with the vendor. The down side for the vendor is obviously the huge cut in margins, or even taking a loss, and vendors often struggle to get deal users to return. Some argue that this cost is too great to maintain vendors as customers, but the vendors I spoke to in NYC say they like deal sites over other advertising methods because of the guarantee that spending will convert to foot traffic. Deal sites do make money, but they’re just not GROWING, which explains the value issues. But why aren’t they growing?
You are bombarded with content, and for every deal you like, you will see 100 that you don’t.
The user-facing side of the deal business is focused at getting consumers to try new products, restaurants and experiences that they normally wouldn’t, in return for a massive price cut. Users are presented with available deals and can pick and choose what they want to try. Here is where the problems start. The volume of deals is MASSIVE! Groupon and LivingSocial use every available channel to reach their users. They have a website, an app, daily emails, push notifications, everything! As a user you are bombarded with content, and for every deal you like, you will see 100 that don’t interest you. Also many deals are far away, or only valid at certain times, and you have to “purchase” deals ahead of time. Travel and pre-purchase mean that you have to plan pretty far in advance to use a deal.
Deal sites are offering most people a value proposition that does not resonate.
From interviewing users for a product we’re working on, I think I know why this is. There is a small core group of users, that enjoy hunting through reams of deals. For most people, however, this just isn’t attractive. It requires planning which inevitably causes millennial FOMO, and behavioral changes such as adopting searching patterns and leaving the comfort zone of known neighborhoods. Deal sites are offering most people a value proposition that does not resonate. Of course Groupon’s growth didn’t continue its hockey-stick. So why do deal sites still stick to the practice of showing users everything? Why not make a solution that is more attractive to a larger audience?
The answer in two parts. Part one is the business model. Clients are paying to get users to try new things. Their existing users enjoy hunting for deals on new things to do and try, and would be alienated by any targeting. In that sense, maintaining the status quo protects an existing business model. Part two is the data. In order to accurately predict if a user will like a deal or not, you need a lot of data. And not just a purchase history of deals, but information about the user themselves. You need to know the user’s behavior, such as where they go, and what they do there, and you need to know personal attributes and preferences of the individual, such as their income bracket, or what their interests are. This is all information that people may, or may not, be willing to give to a deal site in return for better deal recommendations. Even if they are willing, they don’t have access to that data themselves and no way to bring it to the table for deal targeting.
We’re trying to solve these problems in a unique way. Tuba is a mobile app that works with deal sites to retarget deals based on the personal characteristics, preferences and location of the user. It is currently in Beta, focused on food and drink deals for Android in the Google Play Store (iOS coming soon). The idea is to give the deal site an avenue to reach a wider user audience which doesn’t want to commit to planning, searching, or big behavior changes for deals. The focus is on building an app that learns to understand the user and give them a way to use deals spontaneously, presenting them with deals they like, that are close enough to use, right when they are deciding where to go. The app uses machine learning to quantify the user’s preferences and attributes, which doesn’t take data in return for deals, but rather gives the user ownership of their own data profile. With no customer acquisition of it’s own, Tuba does not compete with deal sites, but rather provides a wider audience with a better way to interact with the deal industry. Tuba wants to bring the beleaguered deal industry’s focus back to where it should have been all along: on what people want!