Why not Store Trucks?

(image source: http://decisivecravings.com.au/grocery-shopping-for-good/)

The food trucks are slowly showing up on Indian streets. People are opening up to eat gourmet or exotic (or sometimes even normal) street food out of a truck.

There is one thing that I have not seen yet on Indian streets, which I am thinking, might just work. The daily staple/grocery value chain is growing on a fairly healthy rate. The unhealthy or unplanned players have fallen (or falling). The two or three giants are innovating and moving forward.

The value chain started off with hyper-local. Guys like Grofers or Amazon Kirana, take orders from customers, purchase the goods from your local kirana shop and deliver it home. People did warm up to this idea, but was not that wildly successful. Then of course, the concept of Amazon Pantry is slowly taking shape in the country as well. You can buy everything from your tooth paste, to mustard seeds online through either of the big online retailers – the key mindshare is on Amazon and BigBasket now.

My proposal is somewhere in the middle, is where the Indian market place can really boom. This is where you will understand why I mentioned food trucks at the beginning of this article. Why not trucks with store-branded staples strategically positioned at different points. I have seen this with pop-up trucks selling vegetables, but this could very well work for staples as well.

Reasons why this might just work:

  • Online grocery ordering is a very planned activity, where you sit down and think, and look at your pantry, and decide what to top-up for the month. In my personal experience, invariably, every time after I have finished ordering from big basket, I always end up with 2-3 items that I might have missed.
  • There are always staples that might last you until half of next month, and you end up buying more stuff ‘just in case’. “Just-in-time” inventory would be perfect here, but we do not want to do this online thing at the last moment. What if, I do not get the immediate slot. What if, the item I am order is not available in Express delivery (90 min).
  • With the urban household in India, there is a significant population which travels using company provided transportation. A significant portion of this (and the rest of the) population living in housing societies, apartment complexes and the likes.
  • “On your way back from work, please get …..” is a very oft heard phrase in India urbania.
  • Store trucks with the most basic staples such as rice, wheat, lentils, spices, instant foods, would be the right middle point to be able to achieve the above task.

I wonder if BigBasket or Amazon is listening?

Inference and Insights

This came up in a recent meeting with the Product team at work. We were talking about data and how in recent times, we have gotten a ton of it. We were also talking about how some of this data was actionable, and some was just there. In some areas, data was just becoming very hairy and unmanageable.

We spoke about Inferences vs Insights.

Inferences: You look at all your data. You crunch what you require. You ignore/delete what does not matter to you. You separate out key metrics and secondary metrics.

Insights: These are trends and patterns that you spot in your inferences. You then distill them, cross-reference them, and derive insights out of them. Because of a drop in metric x1, and a corresponding metric x2, the resulting derived metric had a double increase to x3. Add to it, seasonal variations, of a corresponding period in the last year, you get an insight as to whether what you have ‘built’ has been worth it or not.

Getting inferences is more or less a science, but distilling insights is almost an art. It takes experience, and an open perspective to effectively derive insights.

Insights also serve another purpose. They serve as basis for hypothesis, and experiments. You get insights using a subset of data, for a period of time, which leads you to make a hypothesis, which in turn you experiment for a different period of time (or a different subset of data), to prove it.

The above was the effective crux of the discussion that we had, and I thought this might be valuable as food-for-thought (if not anything else), for the few folks who read my blog. 

img src: https://www.linkedin.com/pulse/big-data-insights-found-unexpected-places-austin-wentzlaff