Our Latest Blog

Can AI Help Retail Store Salespeople Sell More?

The best retail sales people excel because they attract customers, serve them well, and keep them coming back. They understand individual tastes and show complementary styles and accessories, boosting sales volume and customer satisfaction at the same time. Underlying this ability to perform on the floor are memorization of inventory, diligent work on outreach, and constant networking to expand clientele. You are probably thinking of your favorite professional right now—the one who can sell an entire ensemble to a long-time customer while helping two walk-ins miraculously “find” items they love.

AI is not going to replace people like these. It will augment their abilities to sell even more, and it will help salespeople with lesser skill to be more like them. Here’s how.

Get Them In
AI in the back office will enable ever-more-personalized messages to be sent to prospects and customers with compelling reasons to make a store visit. Machine learning allows buying patterns and trends to be predictions, as opposed to reactions. Without programming, AI algorithms can be tapped to show affinities and cross-sell opportunities with startling accuracy. A personal note from a with exciting content sent (automatically) from a sales professional is much more likely to prompt a visit than a generalized message. Salespeople get the advantage of outreach without consuming selling or personal time.

Treat Them Right
In-store selling tools can take advantage of AI in real time to offer suggestions on colors, patterns, compatible accessories, and sell-along items with knowledge of available inventory. Attractive assortments are simple to present to the buyer. This increases sales of available SKUs and reduces the time spent per visit—with better customer satisfaction. That is a recipe for better commissions.

Bring Them Back
Re-engagement cements the value of past, pleasant shopping experiences. AI makes it simpler not only to remind customers of items they’ve seen, but also suggest new merchandise choices. Here is where the predictive abilities of machine learning can amplify the seeming clairvoyance of top sellers and add some of that talent for new salespeople. By focusing correctly on sales by a person, to a person, AI for in-store retail can be effective while avoiding the “creepy” factor of behavioral online ads.

Make It Exciting
AI continues to help differentiate in-store retail experiences from online shopping. Real time, internet-of-things, augmented reality—driven by machine learning—is already being incorporated on the sales floor. But salespeople and inventory are the core reasons that stores draw traffic. Ironically, the primary benefit of Artificial Intelligence for retail stores will be helping real people sell, in person, to their customers.

Consider the Future
AI can create a new world for in-store retail that brings together machine intelligence with human intelligence to make the AI-augmented salesperson a reality. Additionally, grand orchestrations of AI-powered and data-informed store operations and shopper experiences are possible. Winners will be the retailers who conjoin the best of AI with the irreplaceable human elements shoppers will always seek.

Why Marketers Need Machine Learning

We can imagine that marketing before computers was primarily based on intuition. Sure, managers had ledgers that allowed them to see sales lift from advertising campaigns, but without insights about which customers were responding to which programs. (We’ll spare you another recitation of the John Wanamaker quote.) High-value customers were known through personal relationships, but marketing and selling costs could not be tracked very well by individual or account. Manual accounting tabulation meant slow reporting. Successful marketers were those who could “feel” what worked based on this restricted set of information.

How far we’ve come.
Here we are, with widespread use of CRM and marketing automation systems tracking business with phenomenal granularity. We know more about consumer behaviors and customer lifetime value that was imaginable only a few years ago. Advertising is measured and viewed instantly with explicit response tracking and social media profiling. Segmentation is fast approaching single-member entities for targeting and personalization. We can see statistics in reports in near real-time. Filters, consolidations, charts, and dashboards provide an exquisite view using the rear-view mirror of history.

Yet successful marketers are still those who can “feel” what works. Contemporary marketing systems are heavy on the past and light on prediction.

Faced with hundreds or thousands of tags and attributes, conventional methods are ill-suited to deciphering trends and motivations and applying them to prospect and customer databases to produce real predictions. It is just too difficult to code Boolean-logic programs to produce predictive analytics in a timely manner. The promise of accurate marketing attribution to guide the future remains largely unfulfilled.

Human intuition is still the primary way marketers transform data insights into actions.

Until now, that is…
Machine learning (ML) is a revolution in predictive accuracy and speed that delivers what marketers need. ML is a technique that enables systems to provide answers without explicit programming. Using artificial intelligence (AI) algorithms, ML learns from training data sets such as won-lost or lead conversion reports. This “supervised learning” is doing exactly what smart people would do if they had months to work at it with a team of analysts and programmers. It just happens in minutes with no development project. When models are applied to full prospect and customer databases, startlingly precise predictions are generated.

It isn’t magic. ML is just automation of AI computational breakthroughs that can find the patterns and associations in your data that have always been there. ML gives marketers their first, real tool for augmenting intuition with data science. Finally, we have ways to apply imagination with confidence. ML frees marketers to do what they do best; what they really want to do in place of system wrangling. Marketer are free to market.

  • Predict which customers are likely to churn so you can target them with retention programs.
  • Rank offers by predicted likelihood to convert for each prospect and send the best.
  • Forecast which leads will produce deals the highest customer lifetime value.
  • Identify cross-sell/up-sell opportunities that will boost revenue.
  • Offer account based marketing content that actually accelerates customer journeys.

For the first time, predictions that you’ve always sought are accessible without coding. Your world just changed.  Find a way to jump into the revolution.