Data quality matters for AI in marketing

According to a recent poll, CMOs worldwide are upbeat and confident about GenAI's potential to boost output and provide them a competitive edge. Nineteen percent are trying GenAI, and seventy percent are currently utilizing it. Furthermore, the primary topics they are investigating are market segmentation (41%), content generation (49%) and personalization (67%).

But there's a big gap between aspirations and reality for a lot of consumer businesses. Marketers that have grand visions of a flawless, magical customer experience need to understand that high-quality underlying data is essential to AI's efficacy. Without it, marketers are left to deal with a less-than-wonderful reality as the AI fails miserably.

AI-driven marketing campaigns fall short

Let's examine more closely what low-quality data can imply for AI-powered marketing. Let's say I visit a general sportswear and outdoor store to make plans for my yearly winter ski vacation that is coming up. I can't wait to get a smooth, personalized experience with the personal shopper AI.

I ask the personal shopper AI to recommend some goods for me to buy because I need to fill in some holes in my ski gear. However, the AI is using information about me that is dispersed throughout the many systems of the business to generate its replies. It asks me for some fundamental information that it ought to know already, but it can't really see me. A tad irritating...

Although I'm used to providing my information when I purchase online, I had hoped that the AI improvement would simplify the process for me.

The AI concierge only has one order associated with my name from two years ago, which was actually a gift, because my data is so disjointed. This personal shopper AI can't provide precise insights without a complete image of me, thus it ends up providing useless recommendations.

In the end, this poor experience reduces my enthusiasm for making purchases from this brand, therefore I choose to shop elsewhere.

bad data quality results in a bad user experience, which is the cause of an impersonal and disjointed generative AI experience.

AI-driven advertising to triumph

Imagine that the personal shopper AI is driven by precise, unified data that encompasses my whole history of contacts with the company, from my initial purchase to my most recent return. Let's go back to the outdoor sports retailer example.

As soon as I ask my first inquiry, I receive a very prompt, kind answer that begins to simulate a one-on-one conversation with a competent sales representative. It makes a connection between my prior purchases and my present buying wants by automatically consulting my shopping history.

The concierge gives me a customized list of suggestions to complete my ski attire along with direct links to make purchases based on my prompts and replies. The AI may then produce deep insights about me as a consumer and even forecast what kinds of things I would be interested in based on my previous transactions, increasing the possibility that I will make a purchase and possibly even filling my basket with more goods.

I can place an order directly through the concierge throughout the experience, saving me time and effort. I am also aware that my profile will be updated with any returns and future purchases.

Generative AI was able to build a really convenient and personalized shopping experience for me since it was aware of my past purchases and preferences. I'll definitely be sticking with this brand for my further purchases.

Put another way, greater data equals better outcomes when it comes to AI for marketing.

How therefore do you truly tackle the problem of poor data quality? And in this new AI world, how would that look?

Resolving the issue with data quality

A consistent foundation of consumer data is essential to fueling an AI approach that works. The difficult part is that customer data is so large and complex that it is difficult to accurately unify. For example, most consumers use an average of five channels, have moved eleven times in their lives, and have at least two email addresses (millennials and Gen Z users use twelve channels).

Numerous well-known techniques for combining consumer data are rule-based and rely on deterministic or fuzzy matching; however, these systems are inflexible and fail when there are discrepancies in the data. Consequently, this leads to the creation of an imprecise customer profile that may overlook a significant chunk of a client's whole relationship with the brand and fail to take into consideration current transactions or modifications to their contact details.

Utilizing AI models—a distinct type of AI from generative AI for marketing—to identify relationships between data pieces and determine if they are part of the same person at a large scale, with the same flexibility and subtlety as a human—is a more effective approach to creating a unified data foundation.

A comprehensive customer profile that describes your target audience and their interactions with your brand can be obtained when your customer data tools leverage artificial intelligence (AI) to integrate all customer touchpoints, including loyalty, email, website data, and more, from the initial interaction to the final purchase and beyond.

How generative AI's data quality propels development

Since other marketers have access to the same set of generative AI technologies, your unique selling point will be the fuel you choose.

Quality of data to electricity Three areas are when AI is advantageous:

Outstanding client experiences include more individualized, imaginative offers, improved customer support exchanges, a more seamless end-to-end process, etc.

Gains in operational efficiency for your teams: reduced manual intervention, a quicker time to market, increased campaign ROI, etc.

Diminished computational expenses – more knowledgeable AI eliminates the need for back-and-forth with the user, saving pricey API calls.

With continued development, generative AI tools for marketing hold the potential to return to the kind of one-to-one personalization that consumers would anticipate from their favorite retailers, but on a much larger scale. However, for the AI magic to materialize, marketers must supply AI tools with precise consumer data. This won't happen on its own.

AI in marketing: Dos and Don'ts

AI may be a useful ally in many fields, including marketing, if it is applied properly. This is a brief "cheat-sheet" to assist marketers using GenAI:

Take action:

Give precise instructions on the use cases in which you want to apply AI and data, together with the anticipated results. What outcomes are you hoping to attain?

Consider carefully whether Gen AI is the best tool for your particular use case.

Give priority to the completeness and quality of your data; a successful AI strategy depends on a consistent base of consumer data.


Implement GenAI as soon as possible in all domains. Start with a small, human-in-the-loop use case, like coming up with topic lines.

Yasmin Anderson

AI Catalog's chief editor

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