The Gist

  • Enhancing experience. AI can optimize in-store experiences. How so?
  • Digital depth. Beyond ads, AI-driven algorithms power content curation and a host of other outcomes.
  • Omnichannel future. The intersection of AI-driven digital personalization and traditional retail is the key. How does it improve CX?

Editor's note: This is the second in a three-part series exploring the main components of personalization in retail, digital and the intersection of dissonance and action. Part 1: AI's Role in Digital and Retail Personalization, Part 1: The Big Picture.

Retail's been around for a while. Hundreds, even thousands of years. And sure, a lot has changed since customers were, say, cowboys looking for boots with gold or silver spurs, but what hasn’t changed are the core principles that create personalized experiences for consumers.

AI and the Art of Personalized Retail Experiences

One of the core principles of an effective retail strategy is being able to cater to the needs of the customer who walks into a retail storefront. What are they shopping for? What are their motivations? What do they like? Who are they shopping for? Are they price conscious? Have they purchased with this brand before?

In the past, to answer these types of questions, retail store associates typically knew your interest touchpoints based on what section of the store you were standing in. In the formalwear section of a clothing store? The associate would surmise you likely have a formal event coming up and need attire and maybe a fitting. Looking at tents in a sporting goods store? The associate would guess you're likely going camping and need some equipment for your trip.

Before the advent of digitally enabled personalization, these questions had to be answered manually by retail associates. Getting to know the customers who came into a store was critical. Being able to make a connection with this customer and being able to suggest products or services that aligned to their journey needs was how traditional retail associates garnered sales. 

This built the foundation for what we see in retail today. But what exactly does retail look like today? How can AI and personalization play a role in physical store locations? Does it mean the end of in-store retail associates?

Let’s answer some of these questions.

Related Article: AI’s Role in Digital and Retail Personalization, Part 1: The Big Picture

Embracing Omnichannel and AI-driven Personalization

Today, retail is a blend of digitally fed experiences that aid retail store selection (we will discuss some of these digitally focused initiatives later). Retailers that are "doing retail well" are fully embracing an omnichannel approach to customer personalization and targeting. For instance, in-store locations should know what email campaigns are opened the most, what customers are searching for the most and truly understand the inspiration behind a customer’s visit to their store. 

These motivations should lead the in-store displays, product placements and associate recommendations. By leveraging AI and machine learning, retail stores can track where consumers walk within stores and determine key performance indicators behind purchase intent like store associate interaction, lighting, in-store scent marketing, time of day, price of products, discounts and other key factors in determining if someone decides to make a purchase.

All this behavior is and can be learned by AI engines to help provide merchandisers with the information and tactics needed to better target consumers with the products and services they desire.

Related Article: Can Conversational AI Improve the Online Retail Experience?

AI Can Enhance the Role of Sales Associates and Elevate CX

But, if AI is running in-store purchase behavior, won't the need for retail associates dissipate? Wrong.

In fact, the need for retail associates has never been higher. As AI steps in to handle some of the in-store elements like customer traffic tracking, time of day preference for ordering and purchase intent — this information can and should be shared in real time with store associates to assist in the buying process.

Picture this scenario. A potential customer enters a fashionable clothing retailer. He first visits the men’s cologne section of the store, spends three minutes smelling different scents before moving over to the trendy clothing section where he looks at various button-down dress shirts. While in this section, he pulls out his phone and appears to be going back and forth between the phone and the shirt. He then goes back to the cologne section and does the same — looking at the phone and the cologne. After a period of time, he leaves the store without purchase. 

In the scenario described, it seems that no purchase was made. While it's possible that the customer might later visit the retail store's website and make an online purchase, it's unlikely since he initially chose to visit the physical store instead of placing an order online.

Now, picture the same scenario happening — but this time, AI is involved. As he enters, tracking systems and beacons watch his behavior. When he pulls up his phone, he pulls open the mobile app for the store and starts reading reviews on the cologne and the shirts. These systems coordinate together and notify a store associate that specializes in men’s clothing that a potential customer seems to be having difficulty choosing what he wants to purchase.

The store associate makes his way to the customer and asks if he can be of any help. The customer says, “You know, I have really big date tonight, and I want to smell and look good, but I’m having trouble deciding what's right for me." At this inflection point, the store associate can use their expertise in fashion and beauty to recommend a shirt that fits his body shape and a cologne that is right for the time of year and the venue of the date. 

This is the future of AI-assisted retail shopping. In this example, intervention by a sales associate at just the right time saves the sale and provides exceptional value and experience to the customer who no doubt, will rely on the retailers' experience again for future purchase decisions.

Harnessing Technology to Enhance Customer Engagement

As time has passed and technology advances have overtaken the world as we know it, the level and depth of personalization we see today is truly exceptional. Nearly all consumer behavior can be tracked, analyzed and reformulated to cook up a truly exceptional cocktail of personalized content and product suggestion that not only appeals to the consumer need, but also appeals to the channel in which the consumer is active as well as the time and place they are active.

It’s also worth noting that personalization goes way beyond just suggesting the right products. Brands are also able to suggest the right content and supportive material that helps consumers within their journey and supports the brand holistically. 

We've all probably experienced visiting an ecommerce web store, browsing products and then afterward those same products seem to follow us wherever we go online. Whether we navigate to a new site or check our email, the products we viewed stalk us like a bad '80s slasher movie killer. 

Learning Opportunities

Revolutionizing Digital Experiences With AI for Better Personalization in Ecommerce

But how does AI fit in here? Sure, AI must be part of these products following us around, right? Well, of course — but it goes much, much deeper than just suggesting products. 

AI-driven experiences in digital aren’t just segmented to ads that follow you around, it flows into everything customers interact with through digital platforms. You’re likely familiar with this tactic thanks to social media. Social media platforms like Facebook, Instagram and TikTok leverage your on-app behavior to drive content it thinks you’ll enjoy.

Like to watch videos about exotic supercars? The app will start to notice this behavior and share more content that fits within this channel. Typically visit social media sites during the after-hours timeframe? The app will recognize this and serve up relevant ads and content during this time to keep you engaged within the platform as long as possible.

The rules of engagement may seem straightforward, but behind the scenes, AI and machine learning algorithms power these processes. These same algorithms and concepts are applied to personalize products and services for consumers.

AI in Action: Personalized Recommendations and Seamless Experiences

Here’s a great use case to explain further how AI interacts within the digital commerce space. Let’s say you’re in the market for camping gear and you navigate to a sporting goods ecommerce web store. Once you arrive you navigate to the search bar and type in "cold-weather camping tent." You’re then presented with various tents that are rated for cold weather. As you browse, you start to compare a few tents and find one you like and add it to the shopping cart.

Upon checkout, you’re given a few product selections such as hand warmers, cold-weather blankets and cold-weather jackets and pants. You decide the blanket is a good idea and add that to the cart and check out. Upon checkout you notice you’ll receive an additional 10% off by creating an account, which you decide is a good idea, and then, ultimately, place the order. 

All seems normal, right? Well, what you don’t see is what AI is doing behind the scenes. In this example, we assume this commerce experience is leveraging AI in all aspects of the digital commerce experience. When you land on the site and search for products, what you don’t see is the AI reading your long form text and vigorously matching that text with previous customers who have searched the same thing.

AI is looking at previous purchase behavior, top viewed products, top margin generated for the ecommerce site as well as many other factors to determine which products you’ll see. The goal: to show you items you'll be more likely to purchase. As you navigate to one of these cold weather tents, the ecommerce AI is determining how long you’re spending on the page, where you scroll to and if you add the item to the cart.

Once the item is added to the cart the site then cooks up suggested items that other customers have purchased when also purchasing this tent — and serves them up in an attempt to grow the basket size of the order.

The final step in the process is the most important; by offering a discount to register for an account with the order, AI is able to understand what product categories the customer has purchased and segment content and experiences based upon that.

The next time this customer navigates to the site, the home page banner, product descriptions, look and feel of the site, promotions, suggested products and other key elements will be personalized to every activity the customer has interacted with since the last purchase. 

AI-Driven Personalization Paves the Way for Next-Generation Omnichannel Experiences

Within the digital space, the above example is the first step in creating persona specific journeys and campaigns. Maybe the same time next year, that customer looking for the cold-weather tent comes back looking for cold-weather pants — AI will be able to determine that this customer shops for cold-weather products within the same period each year, and start to leverage omnichannel marketing through SMS, email and other channels to pre-target the customer before the need arrives. 

This digital AI intervention is critical to personalizing experiences and journeys for customers across all channels, but especially retail. As brands push the envelope of what’s possible within personalization through AI and machine learning — the intersection of retail and digital is impossible to ignore and quite frankly, this intersection is the future of omnichannel AI-driven personalization.

Editor's note: In the final and third part of this series publishing at a later date, we will discuss how retail and digital can coexist and provide massive dividends for customer satisfaction and revenue. AI and machine learning can be leveraged to provide synchronized experiences that strengthen connections between customers and retail associates. AI isn't here to replace retail associates but to complement and extend their value to customers.

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