Significance of data quality in the era of GenAI

A recent survey indicates that Chief Marketing Officers (CMOs) worldwide are optimistic and confident about the future potential of Generative Artificial Intelligence (GenAI) to elevate productivity and confer a competitive edge. According to the findings, seventy percent of CMOs already leverage GenAI, with an additional 19 percent in the testing phase. Notably, their exploration predominantly revolves around personalization (67%), content creation (49%), and market segmentation (41%). Despite such enthusiasm, many consumer brands grapple with a significant disparity between expectations and reality. Marketers envisioning a seamless and enchanting customer experience must acknowledge that the efficacy of AI hinges on the quality of underlying data. Without a robust foundation, AI initiatives risk falling short, leaving marketers to navigate a realm far from the envisioned magical experience.

Let's find out more about how AI-powered marketing, coupled with poor data quality, could manifest undesirable outcomes. Consider a scenario where a customer, excitedly preparing for an annual winter ski trip, interacts with the personal shopper AI of a general sports apparel and outdoor store. Expecting a tailored and effortless experience, the customer seeks recommendations to enhance their ski wardrobe. However, due to fragmented data across multiple systems, the AI requests basic information already assumed to be known, leading to a suboptimal experience. This disconnect culminates in irrelevant recommendations, ultimately dampening the customer's enthusiasm and prompting them to seek alternatives.

Conversely, envision a scenario where the personal shopper AI operates on accurate, unified data, encompassing the customer's complete interaction history with the brand. With seamless integration and personalized insights, the AI offers tailored recommendations, leveraging past purchases to anticipate future needs. The result? A seamless and personalized buying experience, fostering customer loyalty and brand affinity. In essence, when it comes to AI-driven marketing, superior data quality yields superior results.

Now, let's address the crux of the matter: how can organizations tackle the data quality challenge and harness AI's potential effectively?

The foundation of any successful AI strategy lies in a unified customer data infrastructure. However, unifying customer data poses significant challenges due to its scale and complexity. Traditional rules-based approaches often falter in the face of imperfect data, leading to inaccurate customer profiles and missed opportunities. A more effective approach involves leveraging AI models to discern connections between data points accurately, ensuring a comprehensive customer profile that reflects their journey with the brand.

So, why does data quality matter in the realm of generative AI, and how does it drive growth?

For starters, superior data quality fuels AI-powered marketing initiatives across three key areas: delivering standout customer experiences, driving operational efficiencies, and reducing compute costs. As AI tools evolve, accurate customer data becomes the linchpin for achieving personalized interactions at scale.

In the era of AI in marketing, it's essential to adhere to certain principles:

Do:

• Clearly define the use cases and expected outcomes for AI and data initiatives.
• Evaluate the suitability of GenAI for specific use cases.
• Prioritize data quality and comprehensiveness to establish a unified customer data foundation.

Don't:

• Rush into implementing GenAI across all areas. Start with manageable use cases and scale gradually.