What if Ditto Uberfies?

In my last exciting post, I talked about how Ditto unleashes the power of semantic data and how awesome it would be if you could harness the power of the data behind a location in foursquare or Gowalla or a dish in one of my favorite applications,foodspotting.

You may recall that Ditto allows users to express their intent in a particular neighborhood and that it does it in such a way that it does not leave much to the imagination. In other words, we KNOW you want coffee, what kind you want and where you are.

Imagine businesses were to begin taking advantage of this and providing a user reasons to come see them. What if businesses could monitor for people who needed them similar to the way that Uber allows black car drivers to see if people need a ride during down time. The example I always use (and I am waiting for this to happen) is that I am in a neighborhood, I say that I want a latte and a coffee show responds with “Hey Mike, come on in. We will save you a spot next to the window and you can use our free wi-fi. Do you like caramel in your latte? What kind of milk?” I would be thrilled and I would act immediately without worrying about whether they were giving me a dollar off.

If businesses could use Ditto similar to how Uber is used, to fill times when they are not full, to attract new customers with or without offers and people could do everything from accept a deal to pay with Ditto (similar to what is now happening with Zaarly) it would open up a great deal of possibilities for interesting data and segmentation. Take a look at this concept of a merchant side ditto application that allows a coffee shop to monitor the area “Bananatown”.

Notice that each of the people who want coffee in Bananatown have a series of stats. These are based on their actions through the Ditto application.

  • Probability: This ratio is the amount of times a person has acted versus the number of times they have expressed an intention to act. Below it is also the true number (in this case 48/50). This tells the merchant how likely the person is to act on an offer. Mike is extremely likely.
  • Offers: The next number tells how many offers Mike currently has and whether or not he has acted on one.
  • Average / Remaining: This tells the merchant on average how long Mike takes to act and how much time has elapsed since then, making it easy for them to tell whether they should act.

I also imagine having ways to tell what kinds of offers that Mike responds to – deals, invitations etc. I see businesses being able to set thresholds based on certain types of activators and automatically pushing an offer within a neighborhood (geo-fence).

How else would you like to see the data segmented? Do you think that this kind of merchant model is the key to Ditto user adoption?

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