How to use data and AI in the retail customer experience

These days, brand differentiation is built on stellar experiences — not just prices and features. The perceived quality (or lack of quality) at each touchpoint in the customer journey has the power to make retail brands stand out (for better or for worse) in an increasingly saturated marketplace.

At TTEC Digital, we think about the role of brand experiences through the mindset of a customer’s “Last Best Experience.” Last Best Experiences (LBEs) are the last “wow” experience a customer had with any brand. Not only do these experiences generate long-lasting loyalty and customer retention for the brand that created the experience, but they also reflect on every other brand that will need to live up to that stellar experience in the future.

While customers will have different ideas of what their last best experience should look like, personalization is a common refrain. In fact, 71% of customers now expect personalization in their CX, according to McKinsey. Data and analytics offer the strategies and solutions to unlock greater personalization at every step in the customer journey.

How are Retailers Using Data and AI to Serve the Customer?

There is no shortage of emerging data and AI practices that promise to introduce the personalization that customers crave. However, brands are gravitating to a few overarching use cases.

Here are some leading examples based on one recent study by Emarsys and Forrester:

• 54% are using it to personalize the customer experience across channels.
• 52% are using it to manage real-time interactions with customers.
• 41% are using it to target appropriate customers for customer acquisition.

Each of these objectives can help to create the deeper brand relationships customers crave by infusing higher value engagements into the complexity of the modern omnichannel retail environment.

So, how do you get from terabytes of unstructured customer data to each of the three goals cited in this study? It comes down to turning your dirty data into data-driven insights that are more intelligent and more prescriptive.

Three Ways Data and AI Can Augment the Retail Customer Experience Through Personalization

Best Practice #1: Connect Data Sources to Create a 360-Degree View of the Customer

Before you do anything else, you need to start by developing a deeper understanding of your customers. If your company is siloed across multiple marketing platforms, contact center platforms, CRMs, and third-party tools, you’re only able to capture an incomplete snapshot of your customers and their intentions.

In many cases, brands aren’t even aware of the full extent of the barriers standing between them and a comprehensive view of their customers. Even worse, brands that attempt to deploy prescriptive AI-driven marketing strategies and support tools with an incomplete understanding of their customers run the risk of damaging those relationships with irrelevant and annoying communications.

As a starting place, you need to track down all the locations where relevant customer data currently resides. This might include app data, email data, geo signals, behavioral data, the clickstream, customer feedback, and demographics. From there, you can begin to connect these sources together and learn from them. Tools like Amazon Connect Customer Profiles can help make data unification easier.

Creating and targeting high-value persona groups can be a powerful way to begin to realize the potential of a more complete customer data profile. For example, clothing brand Lululemon recently started connecting transactional data and behavioral yoga-related activities (such as in-store classes) to basic sources of demographic data (from a 3^rd-party service like Experian). As a result, the brand is learning how to more accurately measure and track customer intent — including which activities and transactions are indicators of future behaviors. These insights are helping the brand craft stronger recommendations for both products and services.

Best Practice #2: Reimagine the Customer Journey

Customers shop in a variety of different channels. In many cases, they may start a product search in one channel, research the product in another, and finally purchase the product in a third. This can make it hard for a brand to orchestrate a cohesive customer journey.

To support the omnichannel expectations of the modern customer, brands need to better understand how their customers are currently interacting with them. This behavioral data can help inform more accurate future state strategic planning for what the optimal customer journey should look like. At the individual customer level, artificial intelligence can also drive the next-best actions that will help facilitate long-term customer satisfaction and retention.

When it comes to standing up these types of seamless experiences, brands have a few different tools and strategies at their disposal. From a broader persona perspective, customer journey mapping exercises are a great place to start framing up the right channels and the right hand-offs between these channels. At the center of this process, brands start by tracing a current state journey map, where they identify the roadblocks standing in the way of customer satisfaction and conversion. Then, in a future state journey mapping exercise, they can begin to map out the solutions to these problems.

At the real-time interaction level, customer journey orchestration (CJO) tools can help brands navigate their engagements with customers as they happen live — pushing them to the next-best actions and accounting for every touchpoint a customer has had with the brand so far.

Together, these approaches help to track and optimize the customer journey — even as that journey becomes more complicated than ever.

Best Practice #3: Activate Personalization Across ALL Channels

Digital channels tend to get the most love when it comes to activating personalization. After all, that’s where the data and automation tools exist to deliver next-best actions and 1-to-1 messaging to known email addresses and other digital identifiers. Many brands have been doing this with some degree of success for years.

But in a truly omnichannel brand environment, these digital channels also need to coordinate with brick-and-mortar experiences to ensure the customer expectation for personalization is never missed.

One of the most powerful ways brands can do this is by creating apps that bring the two brand environments together. For example, customer-facing apps that provide in-store directions, inventory information, gamification, extended aisle, and easy access to customer service can support a seamless conversion experience that removes common sources of user friction.

Similarly, associate-facing apps, powered by your Customer Data Platform (CDP), can empower in-store employees to better connect with the customers who are walking into the store. One brand that is using this strategy to great effect is Nike. Beginning a few years ago, the iconic brand introduced an associate-facing app that allows associates to see customer purchase history and view a custom list of the types of items a shopper might be interested in. Additionally, the app allows in-store staff to text with shoppers — enabling them to answer questions, and even place items on hold for a customer to try on and purchase in store.

TTEC Digital Supports Data and AI Transformations in Retail

To get the most out of each one of these emerging data and AI best practices, you need experts every step along the way. From strategic planning to solution implementation and program analysis, TTEC Digital offers the experience and technological expertise to help your brand take its data and AI practice to new heights.

Explore The State of Data and AI in Retail Report to see some of the powerful data and AI strategies we can leverage to introduce greater personalization to your customer interactions. With each strategy we’ll touch on the overarching plan, some of the tools we can use, and a snapshot of what the experience could look like.

Strategies include:

• Marketing automation
• In-store clienteling and customer support
• Journey AI
• Conversational AI
• VoC programs