January 2022: Burger King goes vegan

View this email in your browser Spotted in the wild:

Vegan Burger King Pop-up in Cologne

In order to diversify their range, Burger King wants to get data from customers and validate the demand for vegan burgers. How? By using the Facade Pretotype , which is when you have an existing product and test some attribute of scale. This method effectively uses your existing platform and resources (in this case, stores) to validate before scaling.

“Burger King has been offering a vegetarian whopper in its branches for a while, and recently customers of the fast-food chain have also been able to enjoy plant-based nuggets. Now Burger King is taking the next step on the way to a meat-free world and has opened a vegetarian Pop-Up-Store in downtown Cologne. ” I’d love to know what the earlier pretotypes were and their XYZ Hypothesis. What does ‘good’ look like for Burger King to scale this? And thank you Cesc Vilanova for spotting this in our Pretotyping Community on Slack. Your Amazon voucher is on its way to you!

Want your in the wild featured next month (and an Amazon gift card to boot)? Hop on over to our Slack channel!

What’s working well? What could be better?

Burger King is clearly running a deliberate experiment process here. The next step is a Provincial Pretotype to test OPD (Other People’s Data) vs YODA (Your Own DAta). Success in Cologne doesn’t mean you can assume success in Spain, so this is a wise move! The risk they have is that there are many variables to track, so they could get a false positive on the data. The way to mitigate this is to run multiple experiments, ensuring accurate results.

What are the takeaways?

• Like Burger King, it’s essential to know what you’re measuring before investing in a pop-up store and infrastructure.
• This experiment is an excellent example of measuring and learning from actual customer activity in the real world. Add these lessons when you conduct your next experiment!
• Be deliberate and systematic about experimentation.

Remember, experimentation feels like you’re slowing down, but rapid learning always pays dividends downstream.

Experiment Velocity is your friend! Our most successful customers set an annual target of experiments to run, usually 60 to 100 per year. Then they drive towards that by measuring how many experiments they run per month. This process builds momentum and exponentially improves the number of experiments you’ll get out the door as your company gets better at experimentation. As Jocko Willink, ex-Navy Seal and coach, says,

“Slow is smooth, smooth is fast…”

Slowing down the rush to ‘build something’ gains insights fast and reduces waste by building the wrong thing too early.

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Until next month, happy innovating! Leslie

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