has disrupted the retail industry, and that’s a good thing. Consumer behavior is increasingly managed by , leading to an unparalleled online shopping experience. -powered tools have made it easier for even non-techie CMO’s to deliver a highly personalized experience by anticipating consumer needs and making real-time predictions.
-powered marketing is helping marketers to satisfy increasing customer expectations. It’s with the help of artificial intelligence that even small- and mid-scale retailers are able to provide an exceptional one-to-one customer experience like Amazon, Walmart and Nordstrom.
To engage your customers in the right way at any contact point along their journey, consider these techniques.
Look for out-of-the-box possibilities
Give them something they haven’t even thought of. Targeted communications that are relevant and useful can create lasting customer loyalty, as well as an upswing in conversions.
The customer lifecycle is non-linear. One of the most effective ways to win them over is by touching them at the right stage in their customer journey. For example, a shopper who has browsed but didn’t purchase anything will appreciate getting an email with personalized recommendations based on his behavior.
Now, product recommendations are nothing new, but recommendation engines were not as intelligent earlier as they are now. These tailored product recommendations are powered by and advanced machine frameworks that take into account the product-data along with unique user behavior and preferences.
Companies like Amazon and Netflix have become pioneers at using powerful machine algorithms to create an out-of-the-box experience to delight their shoppers, thus converting them into customers and loyal shoppers thereafter.
Map your customer journey across touch points
Customers now have endless online and off-line touch points. Marketers need a single customer view to acknowledge all touch points rather than a series of snapshots.
Marketers who deny the importance of a centralized customer repository are fundamentally wrong in their approach. In the absence of a unified customer view, it is tough to send personalized messaging. To provide a seamless customer experience across channels and deliver real-time personalization, has to be built-in at the core of the system.
helps in providing a wholesome customer experience throughout the customer journey, from the first touch to the final sale. For instance, the traditional approach to predicting customer lifetime value is based solely on customer’s historical data. But the CLTV models powered by machine intelligence take a lot of use cases into consideration to make better predictions, including the purchasing behavior. For example, how recently and frequently they have purchased, monetary value of the purchase and soon, to infer their future actions. […]