Technology
4 strategies for using data, AI to personalize customer experience
Here are winning data and AI strategies for a modern retail environment that caters to the one-to-one customer experience.
October 11, 2022 by Brian Lannan — Vice President, Retail Experience, Avtex
How do retail brands deliver a customer's Last Best Experience in this hypercompetitive experience economy? By leveraging data and AI for a more personalized CX.
Personalization has been a concept in retail for the better part of two decades, but it's only been able to reach the highly effective 1:1 level recently. As customer data platforms and automation tools evolve, so too has a brand's ability to create offers and communications that are truly tailored to each customer's individual needs.
These types of experiences are possible through strategic design and orchestration of data, analytics, AI, and automation technologies. When a brand gets this technology balance just right — and unifies these systems together with 360-degree customer data — the resulting personalized CX can help foster deeper customer loyalty is the bedrock of long-term revenue growth.
So, what are the winning data and AI strategies for a modern retail environment that caters to the 1:1 CX?
Strategy #1: Marketing optimization and journey AI
Your customers shop in a variety of channels. 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 brands to orchestrate one cohesive journey for the customer.
By merging customer feedback channels (like social media and contact center logs) and customer transactional data points (such as past purchases and shopping recency), modern customer-centric retailers have an opportunity to better predict the next actions their customers are going to take and sync up those predictions with the right marketing and customer service messages along the way.
Unified customer data is a critical requirement to provide accurate next best actions for customers throughout their unique customer journeys. Once these are combined into a single customer data platform and augmented with third-party data points, AI is used to produce recommendations for next best actions, products, services, coupons, discounts, and other offers for each customer. These offers are based on the customer's attributes, historical interactions, and purchases, as well as those of similar customers.
For example, these data indicators might be able to help predict when a customer will be in the market for a replacement product, or which additional products or services would make for logical follow-up purchases. Integrated, automated marketing can trigger actions to a personalized channel mix — from direct mail and online advertising to in-store offers and personalized landing pages. Over time, you can continue to fine-tune these efforts using A/B testing to explore new actions and new messages.
Strategy #2: "Clienteling" in the store
While digital channels experienced an uptick during the pandemic, in-store experiences haven't disappeared. But they do need to get better at mirroring the personalization customers have come to expect from all channels. 'Clienteling' refers to the process of driving in-store customer satisfaction through greater personalization at key shopping touchpoints. Most often, it is delivered through more personalized associate service or through a self-service brand app. By connecting the digital and physical experiences, brands have the ability to insert new products and integrate shopping guidance that will impress guests and drive new revenue.
To empower associates in the store, this strategy requires connecting your brand's customer data platform to an employee-facing application that allows every associate to pull up useful customer insights in just a few taps. An associate can quickly see recent purchases, favorite products, and even explore some of the AI-driven next best actions that might resonate with the customer in-store.
Similarly, customer-facing apps equipped with location-based triggers provide customers with support, product information, relevant offers, and even directions in the store, without ever having to track down an associate to ask for help.
Strategy #3: Conversational AI
By now, most customers have experienced chatbots in one form or another. Often these experiences are, at best, forgettable and, at worst, frustrating. Innovative retailers can enable a richer customer experience through conversational AI.
Conversational AI gives customers the opportunity to self-personalize their digital experiences more effectively with a brand. For the brands, it helps reduce contact center overhead costs, drive sales, and streamline access to live support. Many companies already have access to conversational AI capabilities through existing technologies in their tech stack. Often, the difference between forgettable chatbot experiences and a user's last best conversational AI experience depends on how well the experience is designed.
Strong experiences don't happen by accident; they start with customer-centric design. Designing memorable experiences should start with a robust exploration of your customers, their personas, and their user journeys.
Start your conversational AI roadmap with both current state and future state journey maps that shed new light on the ways your customers navigate your website and other digital properties.
Then you can begin to create the automation governance to help direct those experiences at different moments in the digital journey, providing triggers along the way to escalate conversations to a live representative when needed.
Strategy #4: 360-degree voice of the customer programs
Voice of the Customer programs can have value at all phases of the customer journey. The best VoC programs inform more efficient supply chains, better product quality, more intuitive experience navigation, and faster customer support. To make these outcomes possible, your VoC needs to both collect the relevant data points and synthesize them into a framework of insights that both notifies your team of CX issues and identifies opportunities to make it right.
First, you'll need access to relevant customer feedback from a wide range of sources, including surveys, product reviews, social media, contact center agent notes, call and chat transcripts, and other available sources. Next, combine those data points with the inferred feedback you can track, such as transaction history and shopping patterns. Together, these two sets of insights will give you a greater 360-degree view of your customer — beyond just what they say, but also their behaviors and preferences. Finally, set triggers that help monitor customer sentiment and respond to it at both the individual and brand level as needed.
Today, retail customers don't expect just one of these data-driven strategies to help them navigate their shopping experiences — they expect them all. And they expect them to work together seamlessly to create a comprehensive, omnichannel experience. Putting them together lets you implement data, analytics, and AI tools, while integrating them to maximize their collective performance for your customers and company.
About Brian Lannan
Brian joined Avtex in 2021 from Target, where he led the Guest Experience team and was responsible for experience strategy and insights, guest-centric culture development, voice of guest, and brand and reputation insights. He has 15 years of retail experience developing and leading strategy, insights, and CX capabilities.
Related Media
Subscribe
Get the latest news and resources from Retail Customer Experience.
