It all comes down to this one simple word.
I have been using an amazing Chrome extension for the past several months — Zest. Yam Regev and his band of misfits came up with Zest a while back, and I have simply been in love with the app since I discovered it. How it works is fairly simple — every time I open up a new tab in Chrome, it shows me a bunch of interesting articles that marketeers are sharing with the community.
A screenshot from today’s zest screen.
Off late, my Zest feed has been full of stories about Artificial Intelligence, and Machine Learning. It is all the buzz, all the rage today. I have come across at least a few dozen startups in the past six months who are into ML/AI. There are more than 4,000 startups listed on AngelList who are doing something or the other in this space, and if you look at some of the work they have been doing, it is truly min-numbing. But, just as it with any other business segment, a lot of these promising startups will fail to stay afloat after a while. Many would struggle to find product-market fit, many would die down before they can start raking in a sustainable revenue stream, and quite a lot of them would end up with the frustration of wondering why their prospect customers do not see the immense value they are offering — which in their eyes is just so apparent!
That is a problem we witness with a lot of product companies. Amazing products but lukewarm response, at best. Why? Three main reasons:
Just yesterday, I was talking to the founder of a startup who are offering retailers a simple, yet elegant solution to ‘measure and segregate’ the footfalls in their stores. How? By leveraging the CCTV cameras already present there. Their solution can tell you — with accuracy — how many customers have been walking into the stores, their gender, age group, and their overall emotional state. I am slightly skeptical about the last one, but even if only the first three work, it is an incredible value add to retailers everywhere, and the potential applications I see of such a system go beyond just retailers.
The simplicity of it. It is a product that won’t need much explaining. It offers a value to the retailers — segregate and analyse their footfalls in an amazingly intuitive manner.
Because, even as an outsider, I know retailers already use processes to keep a tab on the footfalls in their stores. Some retailers would use a simple manual click-counter to keep a count of people entering their stores every hour. So, as a product, a retailer would (a) See the value of a system like this, (b) Appreciate how this product does what they already do (without the hassle of having an attendant do this with accuracy), and then adds more value on top of it (gender segregation, real-time comparative analysis with historical data, predict footfall in advance if correlated to external causes).
I don’t think so, but that’s a good starting point.
Think of it this way — any offline business where you may see a value add in segmenting your consumer base, this has a real application. Sky is the limit. Take the example of restaurants. If I know what kind of people are walking by at what time, and I map it across what kind of menu items are popular with different segments, I may be in a better position to keep my kitchen prepped, supplies stocked up and wait times short. I may even be able to break down my menu into smaller sub-menus and whip out the relevant menu to the people sitting at the tables. More focussed menu, less confusion in deciding on options, better conversion rates, increased profitability.
Do these few simple things well, and you would have moved an inch closer to both finding the product-market fit, and having a healthy ARR.
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