The holidays are already upon us, promising nearly $731 billion in retail sales in November and December. It's the single most critical time for brands to take advantage of seasonal shopper behavior and drive campaigns that effectively compete, capture, and convert customers at high quantities.
But many retailers miss the mark because they fail to understand a key way to secure a successful season ahead: shifting their machine-learning strategy.
Shopper behavior is fundamentally different this time of year, which means marketers can't leverage the same data and algorithms they did back in August, or even as recently as October.
Lack of preparation and adjustment could cost brands big over the holidays unless they make three changes.
1. Update your algorithms to accommodate behavioral shifts
I say this time and time again: AI at its core is just math, sometimes complex math, and it's up to analysts using the right data to make it worthwhile—especially during the holiday season. Machine-learning algorithms don't have common sense; completely without hesitation, they use and believe the data you provide them.
Customer behavior is very different during the holidays: You'll get an influx of shoppers who don't engage any other time of year, customers will look at particular brands and categories they haven't before, they'll purchase quicker than they usually do, and the list goes on. Plus, everything about their behavior is amplified right now: the number of times they visit and abandon your site, the amount of products and categories they look at, the number of items they're wish-listing and adding to their cart, etc.
Those shifts in engagement are essential to understand and cater to, yet algorithms don't know any better. The only way they can know to read those behaviors is if you tell them to. That requires a close alignment between marketing and data science teams; they must collaborate on what the models should be processing this season, and apply the updated models to the several weeks that comprise the holiday season.