The Short Shelf Life of Insight
Ecommerce brands have a limited amount of time to convince consumers to buy before they move on. They have an even shorter window of time to generate actionable insights based on the preferences and intent of each customer. Any misstep in the insights gleaned or customer experience created can result in costly missed opportunities for retailers. Real-time data and insights have become one of the most sought after marketing assets and have fundamentally reshaped the way marketers and brands communicate with customers.
Creating insights from real-time data is an important, and costly, issue for many brands. They spend time and effort collecting data, cleaning it, and using resources to find something meaningful from it. But what about after the insights have been generated? Do insights have a shelf life? In a word, yes. But it varies based on how a brand plans to use them.
While insights have greater longevity when leveraged for historical reporting and analytics, their shelf life for influencing a customer’s in-session behavior is fleeting.
Customer preferences and intent change wildly from moment-to-moment. Retailers must be tuned in to these changes and adapt their experience accordingly. For example, within a single shopping session, I may begin by looking for a new blazer for myself, but then switch to a new dress for my wife for Valentine’s Day. A brand relying on overly-broad segmentation models rather than real-time data and insights won’t be able to adapt to my change in intent and will likely continue to serve products and content targeting a male shopper shopping for himself.
As opposed to segmentation models, data science algorithms understand the various attributes and correlations among products in a brand’s catalogue and learn the tastes of customers to predict their needs. Data science algorithms help in individualizing each customer’s experience by changing the products recommended for a specific customer or changing the order of products in a search result, for example.
With changing shopping habits, diminishing customer loyalty and increasingly high expectations gathering customer insights has become extremely important for ecommerce businesses in order to survive. So, why are so many ecommerce brands and retailers failing to keep up? Because to do so requires they optimize and synchronize a dizzying array of complex business processes and data sources. These include customer data, product content and catalogue data, marketing and promotional data, and inventory data, just to name a few… and the amount of available data sets shows no signs of slowing.
As more data sources become available to marketers — and the need to act on that data on a more real-time basis grows — understanding the short shelf-life of marketing insights will become a bigger issue for marketers. In turn, it will become increasingly critical these brands have a unified engagement platform capable of understanding and influencing the preferences and intent of each customer in real-time, that is also open and extensible so as to seamlessly integrate these new data sources as they become available.