VR/AR enabled virtual trial rooms to see fit could be great. Clothing, apparel, and shoes are obvious. Less obvious uses are around makeup, hair color, and cosmetics. Being able to try out a complete look and multiple variations could be compelling.

Upsell, cross-sell and personalized interactions are alright, but they don’t move the needle enough to actually improve the customer experience.

This is all about a better customer experience. When you combine the power of AI with human interaction, you have the best of both worlds. AI can make recommendations based on past purchases and buying behaviors, but a good salesperson can infuse those recommendations with personality and excitement.

We have all heard the narrative that data, analytical insights, artificial intelligence, machine learning, and other advanced digital-first technologies are key to driving personalization and enhanced customer engagement. However, before taking the leap and digitally transforming the company, retailers should focus on ensuring that the foundational elements and brand purposes are in place. Every single retail strategy in 2020 and beyond should focus on enhancing and optimizing the customer experience.

It is assumed that we are digitally connected 24/7, with customers engaging with brands across multiple channels on their terms. Unless the foundational elements, including having the right product, at the right price, meeting the customers’ expectations, engaging with the customers on their terms, and having the right customer-facing associates are in place, the fancy digital innovations will ultimately fail and fast.

To transform customer and associate experience, you truly need to become a data-driven organization. You must tap into existing data to discover critical insights that can be shared across your entire organization… including at store level. I think it is best to use an AI-fueled business intelligence platform that supports the entire analytics cycle, from discovery to operationalization. That way you can visualize, analyze and share actionable insights about your data with everyone in your stores. If you leverage AI tools that interpret the data for you, and it also presents actionable insights in plain language, you can far more easily craft tactical store improvements that affect shoppers directly. You have to get actionable insights from your tools to make them worth their investments. This can translate to more effective merchandising and promo displays, more productive staffing and better customer traffic management.

The best AI implementations in-store will be directed to improving the overall shopping experience. Combining the power of data with knowledgeable, well-trained store associates is the holy grail of retail, and it is in reach.

Test, measure, analyze (with AI) optimize, deploy or reject, repeat — just guessing will get you nowhere!

I’m currently working on a project where “mystery shopper” survey results and transactional sales data are being analyzed using AI to predictively determine which locations will perform above average and which locations need attention. AI is being used as an additional tool not to create more “actionable data” (a meaningless buzz phrase in my opinion) but to provide quantitative evidence as to what specific actions need to be taken to improve not only commercial performance but the overall customer experience. Not surprisingly both are inextricably and proportionately linked to success.

Demand forecasting has shown the most promise. Other tech like facial recognition, fraud detection and in-store stock out predictions have been used as well in terms of AI-enabled tech. The only commonality is that these are back office technologies with noticeable ROI that are making inroads. Customer-facing efforts still have some way to go to make them useful and economical. Most larger retailers have already developed innovation labs, pilots and testing centers and stores. These are the most effective ways to trial the technology before full-scale rollouts of the tech, to make sure the tech really does bring customer value. Look for more AI-powered applications in the future.

Inventory, inventory, inventory – Still the bane of retailers’ existence. Leveraging AI to get a better handle on inventory in the store so that associates can replenish or know where to access additional inventory not only enhances the customer experience, it generates revenue! Look at the stuff leaders like Lowe’s and Walmart are doing and that should be a good impetus for others to initiate programs to help get a better handle on inventory.

Flexibility is the key word regarding how AI-enabled technology can help the in-store experience. This can mean flexibility in how the customer views pricing, product placement and inventory levels. It can also be how the associate can change their selling technique by utilizing technology to understand the needs of the customers they are working with directly. Either way, putting the customer first must be the number one priority when analyzing the use-case for all in-store tech purchases.

There’s a bit of the Wild West to all the new capabilities that are emerging. What retailers should not do is go the traditional route, of study, specify, RFP and take years from concept to go-live.

Jump in and try something and encourage a culture of experimentation. Retail is about reinvention right now. Now is the time for companies to step out of their traditional comfort zones and try some new things. And remember it’s called “test and learn” not sit and wait.

Gartner defines AI as having potential in the following four areas:

• Descriptive – what happened?
• Diagnostic – why did it happen?
• Predictive – what will happen?
• -Prescriptive – what should we do about it?

Prescriptive to me is the most valuable. To utilize AI for prescriptivity most effectively requires a real-time cloud based environment that supports all channels simultaneously. Just as Amazon understands who I am and what I buy and browse, a predominantly store-based retailer needs to know the same to compete. Retailers need to understand the unique customer journeys that makes them different than other retailers in their sector, prioritize those journeys that provide the best customer service, integrate the channel silos and then create those journeys.

At Oracle four years ago we developed and came to market with the “retail store of the future” that purported just this — replicate to the extent it adds value the elements of a high quality online/e-commerce experience to win with consumers. Beacons interacting with smartphones to deliver personalized offers, interactive mirrors to show alternate colors or sizes, sensors on shelves monitoring stock and position, dynamic signs and pricing, clienteling so associates can personally address a customer, cameras capturing and interpreting shopper movements, and more. These and more are all good ideas, require data, analytics and sometimes AI/Machine Learning. The question retailers must ask themselves first is what really matters to their customers, and they must tailor experiences to this. Leading with interesting technology use cases is not a recipe for success. To land on what makes sense you need the ability to test ideas, incubate ones that show potential and be able to scale the rollout. The investment this requires can be prohibitive for most retailers (save for those like Walmart and Kroger) so… Read more »

I visited Perch in NYC and explored all of their AI-enabled retail technology that is built into custom displays. It detects the products that shoppers pick up, offers dynamic digital content and measures shopper interactions in real-time. At that time, Perch was working with Sephora and I demoed AR lipstick on a digital display. Perch has grow to work with Macy’s and other leading retailers. Today, retailers can partner to introduce new technologies in-store or work with in-house technology teams to create a “store of the future” concept to test in limited locations. Ultimately, gaining feedback from shopper interactions is vital to developing in-store retail technologies that stick.

Having watches and/or experienced various implementations in luxury, apparel and travel retail over the last 3-4 years, I’ve yet to see a successful AI assist for customer interactions at store level. Once in the store, the customers overwhelmingly want caring, empathetic, knowledgeable associates passionate about what they are selling and the customer experience.

AI tools look and feel manufactured and take away from the authenticity of the human interaction between associate and customer. What does seem to work are AI enabled inventory/replenishment tools that ensure the right product is in stock in the right store, and tools that help manage staffing/scheduling.

I continue to be convinced (for now) that AI investments should be in impacting data analytics and other non-human aspects of the business, and the best investments in in-store customer experience are related to attracting, hiring, inspiring and retaining the best in-store selling and leadership teams.

The obvious use case for AI-enabled technology is predicting the optimal inventory assortments and quantity for each store to help prevent too much, too little or too late inventory situations. Using AI-to recommend products based on customer preferences, interest and purchase history and be used in multiple approaches: customer app, associate app, digital displays, smart mirrors in fitting rooms, etc.

At the risk of splitting hairs, and with tongue in cheek, I’m going to say that if AI stands for ‘Artificial Intelligence’, then what I really want is “Available Information.” Real, actual information. I have a question. I want to learn something I couldn’t find sitting on my couch at home. That’s probably going to involve a human. Maybe a tech-enabled human.

The whole point of going to the store is to see, touch, feel, smell, hear the product in real life. Yes, the app that lets me point my phone at the new shoes and shows me different colors is fun. Does that help me make a decision or confuse me? Does in-store technology reduce return rates? Is all this AI making us smarter, more efficient in our final buying decisions? Or are we just trying to have more fun (experience) in the process? (Yeah, it’s that pesky ROI question.)