The web was to be personalized – that was the prediction decades back. Focused and relevant content, Internet gurus had predicted would rule content delivery. Therefore, it is no surprise that when you log on to Amazon or Walmart today, you are surprised at how much they know what you would likely be looking for. They can predict your wants, and not only yours, it appears your neighbor also has the same to say and the same goes for your neighbor’s neighbor. Prediction algorithms, machine learning capabilities and the use of robotic process automation has changed the way systems are interacting with people like you and me. Quick to latch on to these advantages have been the retail world, as the example of the likes of Amazon and Walmart show. Content delivery is now optimized for your choices, your wants, your desires, and wishes.
Technology companies have leveraged the power of Artificial Intelligence (AI) and machine learning to develop predictive analytic solutions. This help delivers personalized experiences at a scale that was never possible in the past. Targeted content at multiple touch points has become so pervasive that customers have taken it for granted. It is no longer just limited to online shopping too. Even when customers are heading to a physical store, they expect to find a significant level of personalized delivery. For example, if the weather office predicts the likelihood of a hurricane in the coming days, customers would expect to find sufficient stocks of staples, cereals, and other food items that they could stock up on.
Optimization techniques rely on a large set of data points and capture of real-time customer actions. The data sets are what forms the source for machine learning and ones that help recreate the and understand the customer journey. Predictive analytics is employed on this data set to look beyond what customers immediate wants are and start connecting the dots to look ahead on what they might likely want in the future. For example, identifying cross-selling opportunities, identifying the right customer interaction points, create more customer-centric strategies, and generally nudge customers towards more conversions and engagements.
The strategy for retailers
While for companies like Walmart or Amazon, more than 30% of sales is said to come from predictive content delivery, most retailers, however, are still lagging behind. Some estimates say that more than 50% of retailers are yet to take advantage of these capabilities. Even a little of AI can work wonders for retail. Taking that next big step is imperative to stay relevant and derive the massive benefits of technology. A phased approach could easily help retailers, both small and big achieve the benefits that bigger companies have already. It is about investing in the right technology stack, one that helps deliver relevance at their scale. The primary requirement is to consolidate data that retailers already have and repurpose it to obtain that view of your customers that’s critical to map out your customer journey.
Data and technologies like predictive analytics are very doable. Multiple e-commerce firms, as well as small and medium retailers, have employed these technology stacks to uncover customer issues and then redefine the way customers are approached. From things as simple as helping customers find the right size or the right brand that indirectly leads to lesser returns, to creating great customer experiences, retailers who have done the journey report reduced costs and improved customer feedback. This is the right time to latch on to the trend and leverage AI and machine learning to deliver optimized experiences to your customer, rather than risk losing out to others who do.