For retailers to unlock the potential of brand-to-consumer interactions today, they must go significantly beyond siloed personalization applications and widgets.

They must merge advanced visual, vocal, written and predictive capabilities, across all channels of interaction such as site, app, email, chat, push messages, etc., to create a comprehensive digital conversation.

A recent survey found most consumers want to engage in conversation with virtual assistants to quickly find information instead of searching through web pages or apps on their own, as well as having personalized conversations that also include recommendations.

Enter Conversational Commerce. Here are the building blocks of conversational commerce that retailers must develop, to power consistent multi-channel digital conversations with their customers.

Online users are increasingly testing the limits of e-commerce search, and are most often disappointed by the results. Search strings are becoming more complex and users implicitly expect personalization of results.

Every e-commerce search instance today, is in effect, a Turing Test. Users expect machines to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Take a simple example of a user searching for "jeans." Most e-commerce fashion stores will return a confounding motley of jeans of various colors, fits, styles, prices, and brands for both men and women. For a logged-in regular shopper of the store, this is a broken search experience. The user will then either wade through hundreds of jeans and experiment with facet filters, or try different searches iteratively, perhaps something like ‘straight fit jeans with torn knee in indigo under 100’.

In this case, the search engine is expected to intelligently understand explicit and implicit cues from the user. Explicit cues such as the product, style and fit. Also, that indigo is a certain shade of blue, not a brand name here, and that the user has set a price limit. Implicit cues such as the gender of the user, preferred brands, size, and shopping style must also be considered by the machine search agent, just as they would by a human salesperson.

Fashion is a complex industry. On the one hand, individual preferences contribute heavily towards products a user purchases, one the other, external factors such as seasonal trends, campaigns, and social circles contribute significantly toward purchasing decisions. A capable A.I. agent that truly makes personalized recommendations on behalf of a brand must be designed from the ground-up to appreciate that not everything the user is going to like or buy in the future can be simply extrapolated from the user’s historical purchasing patterns.

Today, A.I. technologies make it possible to take everyday images from social media or phone cameras, and identify objects people may be wearing such as clothes, accessories, eyewear and footwear, in such detail that the closest match to each product can be found in a retailer’s catalog. So a user can simply snap an image of something she likes and find similar products seamlessly from her favorite retailers, creating a new use case for conversational commerce, one of intuitive visual search.

Machine learning and A.I. have created opportunities for retail analytics that were previously not effective. A new generation of predictive analytics based on A.I. technologies trains on historical data to calibrate models that determine, with a high degree of accuracy, users’ intent for an action and how that experience can be improved, while also aggregating that information to the entire set of customers for a retailer.

We often find product discovery is a challenge for customers on e-commerce sites that have large catalogs, particularly marketplaces because most search and recommendations are simply indexed on the metadata which may not be accurate or consistent.

Getting specific and actionable insights into challenges customers face on ecommerce sites helps retailers address aspects of their business that will have a meaningful and positive impact on their customer experience, conversions or retention.