Judy is CMO at a growing e-retailer. She knows that video content must be added to her marketing arsenal, however it’s too costly and complex to hire all the skills necessary to produce and capture high quality video content, edit it for Facebook, Youtube, Twitter and now TikTok, and then analyze the results to learn about what’s working for each segment of her customer base, to enable testing and continual improvement of the videos in the same way that is possible with the words on her marketing website.
Judy uses data-driven video marketing automation to solve this problem.
Hundreds of candidate videos are automatically generated, from source materials including her logo and brand guidelines, photos of her products, her existing Youtube and Facebook video feeds, and stock video footage chosen from an extensive library of content.
The videos are automatically assembled using the dataset of what works best for her audience.
Each week, Judy is sent new videos for approval. Judy simply ticks the videos that are approved, and rejects others. The system learns from what Judy approves and rejects. And Judy learns from the system – the engagement metrics of her previous videos. In time, Judy finds she is simply approving the top recommendations, and is comfortable enough to set future videos to be auto-approved.
Her videos change with the seasons, based on shifting market preferences. The videos also change simply for the sake of change, because that’s enough to overcome the negative effects of content fatigue on her social media channels.
Judy’s video marketing engagement metrics are continually moving up and to the right based on the ever-growing amount of data that can be applied to optimize those videos. Her company sells more stuff.