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. Just think, recommendations likely popped up in the sidebar during your most recent Amazon binge. “People who viewed this product also viewed...” often appears as you scroll. A chatbot may even appear with ideas related to your shopping history. This is conversational marketing at work.
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.
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. While conversational marketing has taken many companies this far, using conventional conversational marketing techniques, in conjunction with machine learning, can be the answer retailers are looking to create the experience of the future.
Online retail: Blending conversational marketing with AI technologies
While conversational marketing has become the 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 is on the hunt for a new pair. 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 purchase has nothing to do with a search for the latest technology, but rather because past purchases have missed the mark for this customer’s need for multitasking and call connectivity. In this case, relying on past purchase history or even peer purchasing information won’t help.
However, their experience with a chatbot powered by machine learning can give us helpful predictive data that informs the retailer of their need for 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 a new pair of headphones 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 (AI) 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 a reality.