Consumer expectations have changed drastically in the last few years as innovations like Amazon Prime, free returns and personalisation have emerged. Retailers have to understand the ways their customers are shopping – what they’re searching for, which platforms they’re using, where they find inspiration and what they’ll spend more for. They need to know what the consumer expects from a shopping experience and how to consistently benchmark themselves against that expectation. They also need to be able to know what their customer actually does when it comes to shopping - not just what they say they want.
Reporting isn't working
Retailers and brands typically then turn to reporting tools to try to understand their customers’ expectations and rely on various metrics to estimate their performance. From this, they can try to make decisions about where to focus their time and resources, reading into the metrics and coming up with an idea of what needs to change. This sounds simple, but in practice, with data siloed in different tools for different channels and already weeks old, plus the reliance on gut feeling or personal expertise, it’s a pretty unscientific approach.
Part of the problem is that ecommerce teams working in fashion brands tend to have little time to seek out these insights into their customers’ expectations by delving into reporting tools. That means they’re less likely to find out about changing shopping habits and derive plans from them. That’s because they’re typically fully absorbed in the work of manipulating product data to get it ready for publishing on sales channels.
AI can see a bigger picture of the customer
Ideally, AI should be measuring and learning from a massive variety of data sources:
cost per click/mille/acquisition
search term volumes
All of these are valuable pieces of information that give an indication as to what the brand’s customers expect from their shopping experience, and which would be impossible to effectively monitor manually. The task is then to relate this all back to crucial elements of product data. For example, AI can be trained to pick up correlations between the presence of additional information (for a fashion brand it might be something like including information on the fabric used in a product) and increased conversion.
By doing this, it can learn which pieces of product data are most important for a given brand, and recommend the right value to use for them, in order to match it to the way the brand’s customer is shopping.
The principle here is that the insights generated by the AI are not just reports or metrics – they’re tied to actions which can be taken, e.g. altering copy, including more images, adding new information. Best of all, these recommendations are supported by data and prioritised, so the team can work on the most important changes first.