1. Data scientists and designers work together in the process. We see that data science comes as an afterthought in several companies when the solution is already found. This means that they do not design the answers for AI, which can cause a lot of challenges. At Squared, data scientists are involved from the start to exploit the potential of AI more efficiently, thereby creating better future-oriented innovations. This way, designers are educated and steered along the way on realistic AI solutions that are human-centred and data-driven, which leads to leveraging the power of Machine Learning and AI.
2. We don’t stop after testing: we want to deliver. We want the solutions to be implemented and used by real customers, which requires development capacity. Not only that, but we learned that it is hard for other teams to implement our solutions and free up their (already complete) backlogs if we just have a pitch deck and the results of some prototype tests to convince them. So, we wanted to take our developments one step further and do everything we could to ensure a smooth implementation. Now, we have in-house development capacity, allowing us to build our solutions. This progress provides the resources to create working demos, MVP’s, and experiments on the platform, which validate our solutions from the business perspective instead of just the desirability and to set up effective collaborations with leaders and teams across the group.
One of our most successful examples of a large-scale experiment is the Fake Door experiment. We set up a banner on the most significant car selling platforms, offering a free report download. Although it was a fake gate, we ended up having half a million people clicking on it and getting a survey pop-up instead!