The Importance of Data Quality in Marketing for the GenAI Era
Joyce is the head of generative AI at Amperity, leading product development and strategy. Previously, Joyce led product development for many of Amperity’s ML and ML Ops investments, including launching Amperity’s predictive models and infrastructure used by many of the world’s top brands. Joyce joined the company in 2019 following Amperity’s acquisition of Custora where she was a founding member of the product team. She earned a B.A. in Biological Mathematics from the University of Pennsylvania and is an inventor on several pending ML patents.
A recent survey reveals that CMOs around the world are optimistic and confident about GenAI’s future ability to enhance productivity and create competitive advantage. Seventy per cent are already using GenAI and 19 per cent are testing it. And the main areas they’re exploring are personalisation (67%), content creation (49%) and market segmentation (41%).
However, for many consumer brands, the divide between expectations and reality looms large. Marketers envisioning a seamless, magical customer experience must recognise that AI’s effectiveness depends on high-quality underlying data. Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality.
Let’s take a closer look at what AI-powered marketing with poor data quality could look like. Say I’m a customer of a general sports apparel and outdoor store, and I’m planning for my upcoming annual winter ski trip. I’m excited to use the personal shopper AI to give me an experience that’s easy and customised to me.
When seeking assistance from a personal shopper AI to supplement my ski gear, I quickly discover a hiccup. The AI’s guidance falls short due to its reliance on fragmented data about me distributed across various platforms of the brand. Thus, it prompts me to provide some rudimentary information which, by rights, it should already have in its repository. This is a moderate inconvenience. Although filling out my details online is par for the course, I had expected the introduction of AI into the process would have smoothened out these minor wrinkles.
My dispersed data limits the AI’s capabilities to produce effective recommendations. It can only dig up a previous purchase from a couple of years ago, oblivious to the fact that it wasn’t for my personal use, but a gift. Handicapped by its inadequate knowledge of my preferences, it suggests ill-fitting options.
As a result of this lackluster experience, my enthusiasm for completing a purchase from this brand is dampened. I elect to visit a different platform. The lack of data quality is what sabotages the effectiveness of the generative AI, crystallizing the correlation between subpar data quality and a deficient buyer experience.
Imagine a moment where you’re engaging with a sports retail store’s AI personal shopping service that’s powered by precise and unified data. This data includes a comprehensive record of all your interactions with the store, from your initial purchase to your most recent returns.
When you input your first query, the response you receive is both tailored to you and friendly, making you feel as though you’re in a one-on-one conversation with a cooperative sales representative. This AI service references your shopping history and aptly matches your past buys with your current shopping requirements.
As this interaction continues, the AI shopping assistant offers customised product suggestions to help you complete your ski outfit, and includes straightforward purchasing links. Additionally, this advanced AI can deduce profound insights about your shopping habits and even predict the items you might be interested in based on your previous purchases. This strategic anticipation increases the probability of me buying and may even persuade me to add other items to my shopping cart.
One major advantage of using this AI concierge service to place your orders is that you won’t need to go elsewhere on the website to complete the transaction. Moreover, every return or subsequent purchase you make will automatically become part of your customer profile.
Generative AI’s knowledge of my past behavior and preferences enabled it to curate an extremely tailored and convenient shopping experience for me. This is why it’s going to be my go-to brand for future shopping.
In other words, the effectiveness of AI marketing is proportional to the quality of the data.
But, how do you tackle the challenge of data quality and potential applications in the AI-oriented future?
The primary key to a successful AI strategy is a unified base of customer data. However, accurately merging this data can be tough due to its sheer volume and complexity. For instance, most customers have two or more email addresses, have moved residences at least eleven times, and use about five different channels of communication. For millennials and Generation Z, this number goes up to twelve.
Many common methods to unify customer data are rules-oriented and apply deterministic or fuzzy matching. Yet, these techniques are inflexible and can fail when data does not match flawlessly, resulting in an unfaithful customer profile. This failure may overlook a significant part of the customer’s complete history with the brand, neglecting up to date purchases or alterations in contact information.
An improved method to create a unified data base actually includes harnessing AI models. These models differ from generative AI for marketing in that they infer connections between data points to determine if they belong to the same individual, offering the same nuance and flexibility as a human yet on a colossal scale.
When your customer data tools utilize AI to tie together every touchpoint in the customer journey from initial interaction to the latest purchase and beyond (loyalty, email, website data, etc.), the output is an exhaustive customer profile that reveals who your customers are and their engagement with your brand.
For the most part, marketers have the availability to use the same suite of generative AI tools. Consequently, the input becomes your source of distinction.
Three areas can benefit from data quality to fuel AI:
The evolution of generative AI tools for marketing brings the exciting potential of achieving a level of individual personalisation that customers have come to expect in their stores of choice, but at an immense scale. However, this doesn’t happen automatically — it’s crucial for brands to feed AI tools with precise customer data to truly unleash AI’s power.
AI serves as a beneficial assistant across many industries, particularly in marketing, provided it’s use is optimized. Here’s a succinct ‘cheat-sheet’ to guide marketers on their path to generative AI:
Do:
Don’t:
(Editor’s note: This article is sponsored by Amperity)
Tags: ai, data, genai, generative ai, marketing
You must be logged in to post a comment.
Discover the pinnacle of WordPress auto blogging technology with AutomationTools.AI. Harnessing the power of cutting-edge AI algorithms, AutomationTools.AI emerges as the foremost solution for effortlessly curating content from RSS feeds directly to your WordPress platform. Say goodbye to manual content curation and hello to seamless automation, as this innovative tool streamlines the process, saving you time and effort. Stay ahead of the curve in content management and elevate your WordPress website with AutomationTools.AI—the ultimate choice for efficient, dynamic, and hassle-free auto blogging. Learn More
