Carpet bombs for the right audience.
It’s wise to limit a target audience to avoid coverage loss. But what if the main characteristic is so broad, it almost matches the whole country?
The situation
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TWINT is comparable to a mix of iDEAL and Tikkie. With more than 3.5 million users or 45% of the population, it’s safe to call TWINT the definitive Swiss payment app. As «Swissness» is highly valued in Switzerland, payment solutions such as Google Pay, Apple Pay or Revolut aren’t perceived as competition. In that position, TWINT rivals credit cards and cash – thus their goal is to promote their app to the rest of the country as well as create brand ambassadors.
The challenge
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TWINT’s biggest challenge is their target audience’s main characteristic: «They use money». To solve this problem, TWINT uses personas in their social media campaigns. Those however, may restrict results in such an abstract context. What does a person that uses money look like? Any answer can only be a broad assumption.
The approach
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With the «Carpet Bomb» strategy we stopped making any assumptions. It consists of multiple phases and uses Facebook’s machine learning algorithm to its full potential.

We start off as broad as we can – with Switzerland.

- In a first phase, like carpet bombs, we drop a video ad wherever it lands; on everyone. After around a week this should yield us a broad group of users who watched the video.

- The second phase consists of retargeting those users with for example «Watched 75% of the video». Our main goal here is to narrow down the audience in order to get the most engaging, ideal users. This phase can be repeated until satisfaction.

- We can now construct an audience based on those users using Facebook’s Look-alike audiences. It generates an audience that closest resembles the group above.
The result
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The product is a data-based audience made up of users that are most likely to be interested in TWINT and its services. We stopped relying on assumptions and can now work with a tailor-made audience.
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