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GenAI Meets Customer AI: The Holy Grail of Personalization

Clearly, GenAI by itself is not sufficient, but can marketers complement it with other technology to achieve personalization at scale?

Generative AI has taken the world by storm, with applications like ChatGPT setting a record for being the fastest growing consumer application ever, reaching 100 million monthly active users within two months of launch. It’s no surprise that marketers and creative professions have rapidly embraced Generative AI. Advertisers and agencies have started experimenting with the technology to “spin out ideas and mop up drudge work”. 

While marketers are somewhat skeptical of the occasional missteps of the technology, it’s largely clear that Generative AI has helped achieve tremendous efficiencies in creating content. 

The Twin Challenge of Scaling Personalization

At the heart of it, personalization is about matching the right content to the right customer, taking into account not only each customer’s intents and preferences, but also accounting for each customer’s unique self-directed journey. Traditionally, marketers have struggled with both pieces of the personalization challenge: how to scale content, as well as how to match the right content with the right customer in each moment and on every channel. 

The struggle in scaling content creation has traditionally been a bottleneck in scaling personalization. Back in 2018, an Adobe study found that creative teams who work on personalized campaigns were generating 28 pieces of content every week, but it would take them up to 12 days on average to launch a single piece of content.

Gen AI Solves The Content Bottleneck 

Generative AI’s first contribution to personalization is to reduce or even eliminate the content bottleneck. With just a few prompts, marketers can generate new copy and images in multiple stylistic variations. AI has been hailed as the solution to creating content at scale

With so much content now available, the biggest remaining challenge for marketers is to match the right content in the right style to each individual customer. Without this, marketers risk creating irrelevant content that might be seen as spam by customers.

Clearly, GenAI by itself is not sufficient, but can marketers complement it with other technology to achieve personalization at scale?

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Content AI + Customer AI = Holy Grail of Personalization

In order to truly succeed at personalization, marketers need to combine GenAI (or “Content AI”) with Customer AI.

In other words, the next generation of personalization will involve building a “content interaction graph” for each customer, and using the graph to tailor the marketing message at every moment and on every channel. For instance, not only can marketers personalize content around specific topics or products that the customer may be interested in, they can now also tailor the tone and style to each customer. 

“Customer AI” or AI-decisioning, is all about determining the essentials of each personalized marketing campaign: Who to target; What to say to each individual; When to say it based on the stage in the customer’s lifecycle or journey; and Where (or which marketing channel) is best for each customer. Without such decisioning, marketing often falls flat, and fails to deliver on the promise of personalization. With the arrival of GenAI, ensuring that your marketing is backed by AI-decisioning becomes even more important. 

The nature of AI-decisioning must also evolve to take advantage of the opportunity presented by GenAI.

In the past, marketers have looked to establish “Customer AI” that is built on data about digital and offline customer interactions with the brand. A key goal for marketers has been to map these interactions to the universe of content that was available in order to recommend further personalized content to each customer.

With the advent of GenAI, content is no longer limited, and more can be created on-demand. This means that customer interactions need to be understood not only in the context of the content that exists, but also imagining content (or a variation thereof) that is yet to be “created”.

Understanding customer interactions through the lens of the Large Language Models (LLMs, or the underlying models that power the generative AI) suddenly becomes critical. Marketers must develop this understanding, and ensure that their Customer Data Platforms (CDPs) support such a view of each customer. 

The good news is that AI technology is becoming more and more accessible to non-technical marketers. The AI-enabled marketer now has all the tools to personalize at scale. Data Scientists are pivotal in creating the AI, but marketers as the “data artist” can truly unlock the power of that AI to deliver individualized storytelling in this new era of hyper-relevant marketing. 

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