High Risk: Three Potential AI Consequences of Bad Data for the Retail Industry

Since the sudden arrival of generative AI tools in late 2022, AI has dominated conversations about how to optimize business and marketing practices. The retail industry is no exception: a recent McKinsey survey revealed that 90% of Fortune 500 retail executives have started experimenting with GenAI solutions.

Retailers depend on customer relationships, and AI has the potential to greatly optimize marketing efforts by deepening those relationships. AI can harness oceans of data collected from previous interactions to reach customers on a personal level, recognizing individual preferences and promoting brand loyalty.

However, an AI tool by itself won’t be enough to improve a retailer’s marketing campaigns. AI tools are only ever as good as the quality of data they are trained on. Without clean, organized data, even the most advanced model will fall short. Clean, well-organized customer data is essential to ensure your AI investments lead to lasting increases in customer satisfaction, engagement, and, ultimately, sales.

But just as good data can have positive benefits for AI tools, bad data can lead to a host of serious problems. Let’s take a look at some of the most significant consequences of poor data hygiene for retail AI initiatives.

Bad data: Short-term and long-term risks

GenAI can deliver impressive results — such as generating  accurate answers to complicated questions in a matter of seconds. But what we often fail to consider is the importance of the data that the answer was based on.

Much like filling up a diesel tank with unleaded gas, feeding AI flawed or disorganized data doesn’t just render it ineffective — it can cause lasting damage.

In retail, AI built on bad data can have both short and long-term consequences, affecting individual customer experiences as well as wider business operations.

1. Irrelevant recommendations

With an unreliable data foundation, AI will produce unreliable insights. What does this look like in the retail environment? Irrelevant information targeted at unengaged customers.

With poor data, your AI marketing tool could recommend women’s running shoes to a male customer who has never shown any interest in running. This inaccuracy turns off the customer, leaves them feeling spammed, and makes it less likely they think of your brand the next time they go shopping. If customers don’t feel like your company understands them or their preferences, they will turn to a competitor that does.

2. Misguided business strategy

The need for quality data isn’t just important for your AI marketing tools; your customer data ultimately informs your overall business strategy. When you understand your customers, you can use that information to decide which products to prioritize, what messages to promote in advertisements, and even where to open your next brick-and-mortar store.

AI can be hugely beneficial in helping you turn your customer data into insights that guide strategic directions — quickly combing through mountains of data to generate the most useful takeaways. But if the data is poor, you can end up embarking on a fruitless journey — wasting time, money, and resources in the process.

3. Incorrect customer identification

The consequences of a disorganized dataset ramp up further if your AI tool inadvertently exposes private customer information — especially when infringing on data privacy regulations like CCPA or GDPR.

Imagine your brand emails Sarah, wishing her a happy birthday and extending a complimentary discount. It’s a nice, easy campaign, intent on building a relationship with Sarah and increasing her customer lifetime value (CLV). Now imagine that, due to messy data, this message is accidentally sent to Tim — whose birthday isn’t for another eight months.

Not only does this turn Tim away from your brand — as he assumes you have little-to-no understanding of him or his preferences — but he also questions how secure his own data is. He may even choose to invoke his data privacy rights, requesting the removal of his information from your database. Misidentification doesn’t just risk embarrassment—it threatens compliance, customer trust, and financial penalties.

Identity: The prerequisite for AI success

Retailers can avoid these consequences by getting their customer data in order. Customer identity is fundamental to a well-organized, trustworthy, and workable dataset. You need to be confident in exactly who your customers are to effectively use AI when marketing to them; without a strong identity, you reduce your return on marketing investments and risk ineffective, potentially damaging campaigns.

Amperity’s Identity Resolution resolves customer identities from vast and varied datasets in minutes. By using the best in AI to connect fragmented customer data, we create unified Identity Graphs you can trust.

Combining AI, probabilistic, and deterministic strategies, our algorithms cut the need for complex coding that drains tech team resources. Identity Resolution sifts through customer data across various channels and sources, using a patented AI model to recognize links in the data and develop comprehensive customer identities that remain stable regardless of changes in behavior, channel, or even name.

Turning your first-party data into a trustworthy identity foundation ensures you remain on the right side of privacy regulations while making informed strategy moves — safe in the knowledge you understand who your customers are, what they want, and how they shop.

For more insights on how retailers can make the most out of their AI tools with strong customer data management and strategy, download our recent guide.