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In retail, especially e-commerce, a store's website serves as its storefront. This key access point needs constant care and maintenance. At the same time, even the best preventative measures aren't always going to work out, and a technical snag or item discrepancy causes a customer to stop a purchase and not return to it. Retailers shouldn't necessarily prevent every possible worst-case scenario from occurring, as it would be extremely costly. Instead, they should anticipate failure and learn to mitigate its effects through predictive analytics for retail. In using these types of solutions, it's possible to retain customers otherwise lost due to loss of interest.

A proactive approach to site maintenance and experience
The key to success in maintaining shoppers in the e-commerce setting often comes from improving the user experience, according to Entrepreneur magazine. Some would say technical support would be a more effective solution, but the problem is that customers don't have the patience for customer support unless it's involved with something that they've already purchased. If the problem is the site, they can just switch to another store and get a better result. Instead, the website should work for the customer by offering the best solutions to their issues.

What if the e-commerce shopping experience is customized for shoppers? What if the e-commerce experience is customized for shoppers in real time?

Applying predictive analytics onto the site itself is one way of doing this, integrating a platform with a rich web experience. Entrepreneur suggested the groundwork is already there through customer behavioral analytics. This process enables a proactive response to customer inquiries, concerns and directions. By integrating this at the site level, the experience becomes much more dynamic, anticipating the needs of shoppers and addressing problems as they appear. It also uses integrated user data to customize the site design to specifically nudge consumers in the right direction.

The impact of this proactive approach could be significant. Already, predictive analytics enables a stronger and more cost-efficient level of customer service. Conversion XL noted that by using predictive modeling, retailers at the web and storefront channels can identify how customer service inquiries get handled, and what are the optimal levels to get a response, whether on the phone or through a site chat. So it's not as far a stretch to apply such models to the website experience itself based on a combination of recommendation engines, user input and prior history. As such, the potential for a more robust shopping experience is possible, acquiring and retaining customers in the long term.