At BENlabs, our approach to AI- and data-driven marketing is often described as the “Learn, Create, Model, Scale” approach. But what does that mean? And how does that help build AI models that allow campaigns to scale?
Our EVP of Tech and Head of Tech Evangelism breaks it down for us in this brief description of how we optimize campaign outcomes.
Learn, create, model, scale:
- Learn – a deep look at the brand’s social ecosystem. How do they factor into the landscape? What engagement do they have? Which audiences are they resonating with?
- Create – we then create content that integrates what we have learned. We start testing some of the ideas and some of the hypotheses that originated in the “learn” phase. With individual pieces of content out into the wild to see how they perform with audiences. Some do well, some don’t.
- Model – testing content produces a feedback loop that allows for modeling. Overall understanding of the “learn” phase + output from the “create” phase = more accurate models. To allow us to really see which areas are working, which aren’t. Which areas can we then expand out into.
- Scale – equipped with these models, we can then apply them more broadly. This allows us to amplify campaigns and expand audiences for maximum campaign reach and success.
Learn, create, model, scale: What does that mean? | Ask BENlabs: Video Transcript
Yeah, so “learn, create, model, scale” is very core to the way that we operate here at BEN. And it specifically allows us to build AI models and utilize our AI technology to really optimize campaign outcomes. So, in the first phase, we are learning. We are looking at a brand. We are looking at the overall social ecosystem. We’re looking about their position in it and trying to learn more about that brand.
Where do they sit? What engagement do they have? Which audiences are they resonating with? At a kind of very high level, we then use the output of that “learn” phase to go into the “create” phase. And create as the name suggests is where you start creating content. You start testing some of the ideas and some of the hypothesis that you came up with from the output from the “learn” phase. With individual pieces of content you put them out into the wild, you see how the audiences and how well they’re received.
One of them may do really well. One of them may not. That is then a feedback loop, which then feeds back into the process, which allows us to start doing the modeling. That’s where we take our overall understanding that we generated during “learn.” We take the output that we’re getting from the “create” phase and we use that to build more accurate models. To allow us to really see which areas are working, which aren’t. Which areas can we then expand out into.
And that then finally leads us to “scale,” which is where you’re taking that now proven model and applying it much more broadly, which allows us to really amplify the work that’s going on, and the audiences that you can potentially reach. And so that four-step process takes us from not knowing who this brand is to really understanding how it all works.
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