This article was originally published at the Forbes Communication Council
Think about your last online shopping experience. Chances are, you were delivered product recommendations based on your past purchases, but how helpful were they? Did they truly predict your next purchase and aid in your buying decision?
Amazon has generally been considered the standard-bearer for product recommendations, and for good reason. The retail giant utilizes user data on past purchases, browsed-for items and even what users have recommended to others to generate recommendations.
Still, these advancements fall short of creating a truly personal experience that can predict and assist buying behavior by having a full view of who the person is -- not just their recent search and purchase history. Even common segmentation methods fall short by making assumptions based on age and gender that fail to account for many outlying factors that can be easily discovered.
At Fusion Alliance, we find our place at the intersection of advanced analytics, experience design, and technology, and we leverage machine learning to gain customer insights that inform our strategies.
The future of retail will be defined by immersive, conversational experiences that lead to better customer interactions and increased buyer loyalty from brands that stay ahead of the curve.
Blending conversational marketing with online retail
While conversational marketing has become the new trend in business-to-business (B2B) demand generation strategy, there exists a huge business-to-consumer (B2C) opportunity, as well.
Conversational marketing practices utilize website chat features and chatbots to initiate in-the-moment interactions with customers and build context to quickly qualify them for the appropriate next step.
According to David Cancel’s aptly titled book, Conversational Marketing, both baby boomers and millennials are likely to adopt the use of chatbots, with a majority in both groups finding instantaneous responses and quick answers to simple questions being potential benefits.
Aside from the obvious advantage of getting answers to product questions, automated chatbots offer a number of opportunities to enhance shopping experiences when coupled with data.
Machine learning and chatbots
While many B2C companies are already leveraging chatbots to streamline the customer experience, there lies even greater opportunity with machine learning to truly learn from and predict consumer behavior. Today’s practical machine learning models enable rapid iteration of data and deliver quick, reliable data sets.
Data collected from customer conversations about the products they research, buy, and use can tell a deeper story about the customer themselves over time. Instead of a static list of recommended products based on their last purchase, machine learning can help us understand the customer’s lifestyle and habits in such a way as to help the customer make the best purchase in the moment.
As an example, imagine an on-the-go, seasoned business professional with a love of podcasts and streaming music. Our traveling audiophile is a regular adopter of new headphone technology, and while segmented data and previous purchase history might be able to get us in the ballpark when it comes to their next tech purchase, it doesn’t tell the whole story.
In fact, the reason for this particular purchase has nothing to do with a search for the latest technology; it is instead because past purchases have missed the mark in terms of multitasking ability and have presented connectivity issues during business calls. Therefore, relying on past purchase history or even peer purchasing data won’t help.
However, their experience with a chatbot powered by machine learning can give us helpful predictive data that informs us of their need of a balance between audio quality and the ability to quickly and clearly connect to meetings during travel. A few quick questions allow the chatbot to suggest the right new pair of earbuds to fit their lifestyle, along with helpful content and reviews that match our customer’s pre-purchase research habits.
Marrying predictive data to emerging technologies
As advances in artificial intelligence continue to blur the line between human and bot, and retail brands continue to experiment with augmented reality (AR) to replicate brick-and-mortar shopping experiences, it’s vital that data plays a role in the next phase of online shopping.
Not only should brands be placing an emphasis on the aesthetic experience that can be delivered through apps infused with AR, but they should also make room for predictive machine learning data to make the buying process even easier for the consumer, making them more likely to return in the future.
In fact, for any brand wishing to be at the forefront of the next wave of retail evolution, I believe it’s vital that a data governance framework be in place and actively funnel information to teams developing emerging technology.
The days of keeping customer data siloed away from our product teams need to come to an end in order to fully realize the marketplace potential.
The future of retail is filled with possibilities that can completely reshape the way we understand consumer behavior and connect with the consumer to meet their needs in real time.
Taking tangible steps to listen to our customers, learn from them, and act to predict their needs, while delivering a stellar shopping experience along the way, is more than a possibility -- it’s quickly becoming a reality.