News, Data management

Learning about people's drink preferences with data collected by Rentle

By Akseli Lehtonen on August, 1 2019
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Yesterday Rentle was co-hosting Kiuas Accelerator's BBQ party. Thank you all who came and visited our booth, it was a pleasure to meet and chat with you all. We had super fun evening and based on our observations (and statistics) so did you. Wonder if the 300 extra drinks had anything to do with it 🙂

What party?

A little background for those who were not present or don't know what party we are talking about:

  • Kiuas Accelerator is the most recognised startup accelerator program in Finland.
  • Rentle is a member of the ongoing program.
  • In addition to the coaching sessions, workshops, and other more official program, Kiuas throws open BBQ parties every other Wednesday at Startup Sauna.
  • Yesterday it was our turn to take part into the arranging committee.

Keeping the guests hydrated

Like proper hosts, we thought that the guests must be thirsty. So we hired Son of a Punch cocktail artists to make drinks to our guests. The kind people from Beverage Partners Finland sponsored us with quality drinks to be used for the cocktails. We estimated that 300 drinks would keep the guests happy in addition to the beer and cider provided by Kiuas. Well, it took a bit over three hours from the guests to drink 300 cocktails until we ran out. 

Cocktails

The bartender on the right is having Rentle's software open on a tablet, where she receives the orders. The guests in the middle are making orders with their smartphones. Smooth! 🍹

The catch

Of course we had a catch in offering people free drinks. We wanted them to order the drinks using our software that's normally used for renting out sports equipment such as skis, snowboards, bicycles, and so on. The funny part is that the software fit more than well to this kind of usage. Or how otherwise would it be possible to serve 300 drinks in approximately three hours without causing a major chaos? That's approximately 1,67 drinks served per minute and 1 drink served every 35 seconds. And all this with a software that's meant for renting out equipment.

How everything happened?

Well of course, a big thanks goes to the skilful bartenders. Without them this stunt wouldn't have been possible. But we'd like to keep some honour ourselves as well.

  1. In less than 5 minutes we educated the bartenders on how to use Rentle. They had never heard about us before and they managed to handle all the orders without any help from our side.
  2. We posted QR-codes around the event that instructed the guests to order drinks. The guests hadn't used our service before, so this was also a great usability test for Rentle's self check-in solution.
  3. First we followed that everything runs smoothly but soon were able to relax as we realised that our help wasn't needed.

With the stunt we were able to demo our service for 190 people.

The evening in numbers

The bartenders from Son of a Punch had designed a drink menu with three drinks (we came up with the names 😅):

  1. Surfing grandma - Aperol, rhubarb, strawberry, lemon
  2. Investor's Aprés Ski - Hendrick's gin, cucumber, apple, basil, lemon
  3. Tour de France Wake-up - Cointreau, cold brew coffee, vanilla, orange

Drink orders

How the orders of the drinks distributed. Interesting ðŸ¤”

Either there were a lot of investors in the guests or the guests just had a taste for more sour drinks. But what else did we learn from the numbers?

Drinking stats

7 cocktails. Someone had a nice evening! 🥳

Based on the data, we know that 1,5-2 cocktails per visitor is an optimal amount if there is additional beer and cider in the party. We also learned that most people prefer drinks with fruits and berries instead of coffee.

The point of this blog is not to encourage using our service for ordering drinks but to clarify, how much one can learn about people's preferences with data. We will definitely use all this information when we next time throw a party. But imagine how much a rental shop could learn about their business and consumer preferences if they collected and analysed all the data they could.

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