Overcoming the Personalization Gap with Machine Learning [A Q&A with Episerver]
According to a recent survey and in-depth interviews of nearly 150 e-commerce executives, only 17 percent of online retailers have a path to develop personalized experiences for customers (despite 64 percent of them having personalization technology). On the other hand, a large majority (74 percent) of customers feel frustrated when website content is not personalized.
How can retailers bridge this gap between what they currently offer and what customers want? Website Magazine caught up with Joey More, director of product at Peerius (an Episerver company) to learn about the current challenges retailers have for executing personalization initiatives and how machine learning can help.
Why are so many retailers still sorely missing the mark when it comes to personalizing Web experiences?
Some retailers don’t know that one-to-one personalization is actually possible. There are plenty of vendors out there selling different segmentation tools under the banner of personalization whether they truly have these capabilities or not. Many retailers may have experimented with these segmentation strategies or may be currently executing them, but they are very hard to scale without the help of machine learning. Behavioral, one-to-one personalization applies machine learning algorithms to big data to make every interaction personal to each customer. And with this technology, there is no limit to the number of segments or groups that can brands can track and target. Episerver users can now distill those segments down to one, targeting each individual visitor based on their distinct customer journey.
What, if any, changes have to happen in the retail space to bring more retailers on-board with personalizing their Web experiences?
With so many different channels and touchpoints to think about, retailers are busier than ever. They must focus on the activities that create a real ROI and bring measurable improvement to customer experience, but it takes sophisticated technology to make this happen. At Episerver, we have developed tools to simplify implementation and generate incremental revenue from day one.
How does Episerver help automate steps in the personalization process – and to what degree is machine learning involved?
We believe that the machine should do the heavy lifting for you. This allows teams to focus more on the strategy and overall campaign management rather than on the nitty-gritty of managing an ever increasing number of segments and products. We have over 100 different machine-learning algorithms, which our platform deploys at optimal moments in the customer journey to increase conversion and average order value (AOV).
Does Episerver take into account behavioral fluctuations (like gift-giving or family buying) to adjust the consumer's experience? If so, how so?
We consider the behavior of the visitor from all of the channels they have interacted with, whether that be online, mobile, in-store or through a contact center. Our platform specifically focuses on gleaning the intent of the consumer from each distinct shopping session – which might involve multiple touchpoints across multiple channels – to ensure that the right products are being put in front of the user at the right time.
Anything else you’d like to add?
Machine learning technology is being used across hundreds of different sites right now, including Tommy Hilfiger, Topshop, Superdry, Ted Baker and many other brands across homeware, wine, electronics and gifts. This technology helps retailers get ahead of their competition and create the unique customer experience that today’s consumers expect.