Every shopper wants to feel a connection to their favorite brands. Retailers have long realized that business stability and growth is all about personalization, and making the shopper feel like they are a valued guest. AI technologies are a big part of this effort: analyzing retail data, making product recommendations, and automating processes that streamline the customer journey to deliver a great experience.

Today, agentic AI is stepping in to make it even easier — or harder, depending on the condition of your customer data. IBM defines agentic commerce as ‘an approach to buying and selling in which AI agents act on behalf of consumers or businesses to research, negotiate and complete purchases, often without direct human intervention.’ That’s a wow, adding significant depth to the customer experience a retailer can offer a shopper. Let’s call it hyper-personalization, and let’s acknowledge that it only works if it’s powered by clean, enriched data.

Is agentic commerce taking off?

Yes. Agentic commerce is not only transforming the retail industry by automating operations, personalizing shopping journeys, and enabling real-time decisions, but consumer adoption and engagement are soaring. The proof is in the numbers, as consulting firm BCG reports a 4,700% YoY traffic increase to US retail sites from GenAI browsers and chat services. These same buyers are deeply engaged, spending 32% more time on site, browsing 10% more pages, and resulting in a 27% lower bounce rate from retailer emails.

These numbers also demonstrate that retailers need to participate or risk loss of customer engagement and diminished brand loyalty as third-party AI platforms take up more space in their buyers’ universe. For AI agents to autonomously find, compare and purchase products for consumers, they need to be able to pull from a foundation of high-quality, API (or machine-readable) data. To enable this, retailers must anchor their AI initiatives with a clean data foundation.

Make sure your customer data is AI-ready 

Is your organization on track with its customer data quality policies and processes? It’s a tough question to answer without a formal understanding of your weak spots such as under-utilized CRM applications and integrated data quality tools. Melissa’s data quality assessment provides an example, creating a snapshot of data issues and identifying ways to begin correcting them. It’s an ongoing effort, and a clean data foundation requires constant vigilance.

Four key data quality operations will put your customer data on track for the wave of agentic AI that will soon be distinguishing retail leaders.

Cleanse and update your customer records routinely, so you’re not behind the curve with out of date and generally incorrect information. This means verifying identity, name, address, email, and phone in real time, and batch updating inaccurate or outdated information on a regular basis. Data changes fast, so parse and structure data into a usable format that keeps it ready for all types of marketing initiatives and business operations.

Making your data more informed makes it more powerful. Enrich customer records with demographics, firmographics, geographics, social media, property attributes, and missing email and phone information. This is the only way to correctly and confidently support analytics, personalization, and omnichannel marketing efforts.

Match and merge duplicate records to create a single, accurate customer profile you can trust. Duplicate profiles mean wasted marketing costs and prove you don’t really know your customer. Data snafus like incomplete profiles, mismatched addresses, or duplicate loyalty numbers are great examples of how bad data can ensure mistakes in customer engagement.

Monitor your customer data across the entire data lifecycle. Use data tools designed to streamline and correct data entry to prevent bad data from even entering your database and keep it clean over time.

Extend best data practices to your AI strategies

Now that your customer data is in shape, use it well to train, deploy, scale, and determine the ROI of your AI initiatives.

Good data enables retailers to prevent biased results by training and fixing metadata at the source. This can be amplified by remaining focused on a narrow, agentic AI platform aligned with qualified data and business rules — a foundation reflected in data quality operations that are disciplined and prioritize correct, current, and standardized customer data. These same processes support expert supervision, fueling real-time training and semantic flagging to correct data errors before they perpetuate into the customer experience. Well-labeled data strengthens accuracy across mission-critical AI applications, ensuring the automation and scalability necessary for real competitive value.

AI performs only as well as its data foundation, so great customer data is required. Accuracy is more than important; it’s everything.