A recent survey found that CMOs around the world are optimistic and confident about the future ability of the AI generation to improve productivity and create competitive advantage. In this opinion piece, Joyce Gordon (main image), head of generative AI at Amperityexplains how to unlock the magic of marketing using Generation AI.
Seventy percent are already using Gen AI and 19 percent are testing it. The main areas explored are personalization (67%), content creation (49%) and market segmentation (41%).
However, for many consumer brands, the gap between expectations and reality is huge. Marketers envisioning a seamless and magical customer experience must recognize that the effectiveness of AI depends on high-quality underlying data. Without it, AI collapses, leaving marketers grappling with a reality that is far from magical.
Failure of AI-based marketing
Let’s take a closer look at what AI-driven marketing could look like with low-quality data. Let’s say I’m a customer at a general sports and outdoor clothing store and I’m planning my next annual winter ski trip. I’m excited to use Personal Shopper AI to give me a simple, personalized experience.
I need to fill some gaps in my ski wardrobe, so I ask the personal shopper AI to suggest a few items to buy. But the AI creates its responses based on data about me that is scattered across the brand’s multiple systems. Without a clear idea of who I am, he asks me for basic information he should already know. A bit annoying… I’m used to entering my information when shopping online, but I was hoping the AI upgrade to experience would make it easier for me.
Because my data is so disconnected, the AI concierge only has an order associated with my name from two years ago, which was actually a gift. Without a complete picture of me, this personal shopper AI is unable to generate accurate information and ends up sharing recommendations that are not useful.
Ultimately, this poor experience makes me less enthusiastic about purchasing from this brand and I decide to go elsewhere.
Data quality is the root cause of a disconnected and impersonal generative AI experience: poor data quality = poor customer experience.
AI-powered marketing for victory
Now let’s return to this outdoor sports retailer scenario, but imagine that the personal shopper’s AI is powered by precise, unified data that contains a complete history of my interactions with the brand, from first purchase to last return.
I ask my first question and get a super personalized and friendly response, already starting to create the experience of a one-on-one connection with a helpful seller. It automatically references my purchase history and connects my past purchases to my current purchasing needs.
Based on my prompts and responses, the concierge provides a set of tailored recommendations to complement my ski wardrobe, along with direct links to purchase. The AI is then able to generate sophisticated insights about me as a customer and even make predictions about the types of products I might want to buy based on my past purchases, thereby increasing the likelihood that I will purchase and potentially even expanding my basket to buy more. items.
With this experience, I can actually use the concierge to order without having to navigate elsewhere. I also know that my returns or any future purchases will be integrated into my profile.
Because it knew my history and preferences, Generative AI created a super personalized and convenient shopping experience for me. This is a brand I will return to for future purchases.
In other words, when it comes to AI for marketing, better data = better results.
So how do we address the data quality challenge, and what might that look like in this new world of AI?
Solve the data quality problem
The first essential element to powering an effective AI strategy is a unified customer database. The tricky part is that it’s difficult to accurately unify customer data due to its scale and complexity: most consumers have at least two email addresses, have moved more than eleven times during their lives and use an average of five channels (or if they are millennials or millennials). Z is actually twelve channels).
Many familiar approaches to unifying customer data are rules-based and use deterministic/fuzzy matching, but these methods are inflexible and fail when the data does not match perfectly. This, in turn, creates an inaccurate customer profile that can actually miss much of a customer’s life history with the brand and fail to account for recent purchases or changes in information of contact. A better way to build a unified database actually involves using AI models (a different flavor of AI than generative AI for marketing) to find the connections between data points in order to know if they belong to the same person with the same nuance and flexibility of a data point. human but on a large scale.
When your customer data tools can use AI to unify every touchpoint of the customer journey, from the first interaction to the last purchase and beyond (loyalty, email, website data, etc.), the result is a comprehensive customer profile that tells you who your customers are. and how they interact with your brand.
How Data Quality in Generative AI Drives Growth
For the most part, marketers have access to the same set of generative AI tools. Therefore, the fuel you use will become your differentiator.
Data quality for AI provides benefits in three areas:
- Customer experiences that stand out: more personalized and creative offers, better customer service interactions, a smoother end-to-end experience, etc.
- Operational efficiency gains for your teams: faster time to market, less manual intervention, better ROI on campaigns, etc.
- Reduced computational costs: Better-informed AI does not need to go back and forth with the user, avoiding the accumulation of API calls that quickly become costly
As generative AI tools for marketing continue to evolve, they promise to return to the level of one-on-one personalization that customers have come to expect in their favorite stores, but now at scale. However, this won’t happen on its own: brands need to provide AI tools with accurate customer data to bring the magic of AI to life.
The Dos and Don’ts of AI in Marketing
AI is a useful ally for many industries, especially marketing, provided it is harnessed appropriately.
Here’s a quick cheat sheet to help marketers on their AI generation journey:
Do:
- Be explicit about the specific use cases in which you plan to use data and AI and specify the expected results. What results do you hope to achieve?
- Carefully evaluate whether Gen AI is the most appropriate tool for your specific use case.
- Prioritize data quality and completeness: Establishing a unified customer database is essential for an effective AI strategy.
Don’t do it:
- Hurry up to implement AI generation in all areas. Start with a manageable, human use case, like generating subject lines.
