Personalization is the new retail imperative for forward-thinking brands as consumers increasingly seek out superior customer experiences. How will these brands deliver uniquely engaging, authentic, and – most critically – relevant shopping experiences?
By using up-to-the-second data – and knowing how to process that data – retailers can dynamically predict what consumers will find relevant. Consider this the evolution of relevancy – retail brands leveraging the newest machine learning and data science to go beyond segmentation. In this new world enabled by predictive intelligence, brands can personalize content for each consumer and offer truly tailored shopping experiences.
This is not segmentation as the retail world typically understands it. Of course, retail marketers have always understood the importance of relevancy, and have developed expertise in using technology to present the most relevant products and messages to the right customers at the right time. To date, using big data to improve segmentation has typically involved an exercise along the lines of: “Show me everyone who browsed, but did not purchase a sweater.” The core limitations of such oft-used strategies are clear: they fail to focus on the individual who has unique tastes and goals.
Through predictive intelligence, retailers can consider a customer’s data contextually, and in real-time, to better understand the customer as an individual – not as a part of a demographic group. By predicting individual customer behavior, marketers are now able to customize a shopper’s journey and match it to desires that the customer may not even have known she possessed.
This technology allows brands to automatically build individual predictive models for each and every customer that interacts with a retailer. How? By collecting hundreds of thousands of attributes for every single customer – including things like transactional, clickstream, and purchase history data. Predictive intelligence also updates each and every model with real-time behavioral information and dynamically adjusts individual customers’ experience to deliver the right message for him or her – thereby optimizing business results.
The key value stems from the way predictive models look at both product affinities and customers with similar preferences. This allows retailers to present an individual customer with new products or promotions they normally wouldn’t have considered or been aware of – but which data science predicts they will actually enjoy. The result: vastly enhanced new opportunities for personalized product discovery.
Predictive intelligence technology also solves a counterintuitive problem that many retailers face: the “little data” problem. While retailers may hold a tremendous amount of aggregate data about all of their customers – the vaunted big data – they often possess remarkably scant data about any individual. The newest predictive intelligence technologies allow retailers to improve the relevancy of recommendations that come out of these individual models, even for those consumers who have had relatively little interaction with that retailer.
Armed with predictive intelligence, retailers can now look at each customer as an individual and as a unique engagement opportunity. No longer are shoppers to be clumped together as a demographic or segment of the population – identified by their age, gender, income, and other impersonal characteristics. With predictive intelligence, each shopper experience is unique and personalized.
Predictive intelligence is the next step for retail, enabling brands to elevate the shopping experience with concierge-like customer service, product recommendations, and personalized promotions.
In 2016, look for smart brands to upgrade their predictive intelligence strategy with tools that not only consume data from all sources, but also allow them to embed data in multiple customer channels and create truly personalized shopping experiences for every consumer.
Rama Ramakrishnan is Chief Data Scientist for Demandware