May 29, 2019
Creating relevant and meaningful content.
We don’t read what we can’t relate to.
Keeping it personal can lead to
8 min read
Why product attribute data like size, color, fit, style, etc, is the hidden secret to creating more relevant and personalized content.
The tale of two worlds.
In one world, there are marketers who’s aim is to find out as much as possible about their customers. Everything from their ages, to where they live, what their extracurricular activities are, and of course any available transactional data.
This is all essential to providing a personalized customer experience. In another world, there are the planners and buyers who have an extensive understanding of their products.
From inventory levels to margins to sales – they have a deep understanding of the product life-cycle and what their customers want to buy. So with all this knowledge and information floating around in the 2 worlds, how can these data points be used to create more happy customers and drive more sales?
In other words, how can we make these worlds coalesce. The missing link between these two worlds and the path to true customer personalization is right under every retailers’ nose.
“The next step for retail executives who want to buy more efficiently and market more effectively is to redistribute some of their invaluable time, energy and skill to embrace the power of AI and Machine learning with their product attribute data and watch the impact it will have on the end result”
The importance of product attribute data.
Product attribute data like size, color, fit, style, etc, is the hidden secret to creating more relevant and personalized content – which leads to more sales and happier customers.
The marketing team can pair product attribute data with knowledge about their customers and shopping history to accurately predict the next basket item a customer will purchase, the best time to email a customer about a promotion, what promotions a customer may want to see, etc. In the buying world, the planners and buyers can use product attribute data to reliably predict the demand for each product to determine what to buy in what style, color, fit etc. meeting customer demands and trends more accurately.
The advent of AI and Machine Learning has made collecting and actioning product attribute data much more feasible and accessible. While human beings can be incredibly creative, they tend to be bad with mass data, they simply don’t have the capacity to process, let alone analyze, the overwhelming amount of product attribute data. With AI and Machine Learning, analyzing and actioning product attribute data is now much more tangible, reliable and repeatable.