How data, ID graphs, and AI can be used to deliver highly relevant experiences to customers–without overstepping privacy boundaries

You’ve likely seen the hubbub around machine learning and LLMs being put to work to create scaled, automated, hyper-personalized, one-to-one campaigns. With the global revenue of the customer experience personalization software industry projected to increase to $11.6 billion by 2026, a massive tech overhaul is right around the corner. 

Yet when it comes to hyperpersonalization, the risks and rewards are amplified. Make a misstep, and your consumer trust could tank. Yet, do everything right, and you can achieve higher engagement and ROI. 

As marketers go from pondering about LLMs as hypothetical scalability automators to engaging in actual use cases implementing the tech for one-to-one campaigns, it’s time we reviewed what role a good data backbone plays in at-scale personalization and how not to miss the mark when it comes to automated customer interactions.

What is personalization at scale?

Before we launch into hyperpersonalization, let’s review what “personalization at scale” means today.

Personalization at scale involves using customer data (typically first-party), verified and refined through an identity graph platform to create profiles for each customer. These profiles are then used to deliver individualized experiences to a large number of customers across a number of different channels. Personalized content can include content, recommendations, coupons, emails, and more.

The “scaling” process is automated to a significant degree–and specific tools can automate it even further. A/B testing, generative AI, aforementioned identity graphs, and more can all assist with achieving personalization at scale.

What hyperpersonalization looks like

There are countless modern-day examples of one-to-one personalization at scale, both within and outside traditional marketing campaigns. The best rely on a backbone of first-party data.

Loyalty programs like those showcased by Sephora and Starbucks personalize rewards and promotions based on location and purchase history.

CTV platforms like Netflix and Max, along with streaming platforms like Spotify, use their analysis of subscribers’ viewing and listening history to tailor unique content recommendations for each user. 

In ecommerce, platforms like Amazon use one-to-one personalization to create a homepage full of individualized purchase recommendations.

Social media platforms, too, rely on the strength of the “algorithm” to serve up content users will like and resonate with–and increasingly, that also includes products users are likely to buy.

In lifecycle marketing, emails targeting cart abandonment, product suggestions based on browsing history, and purchase follow-ups also count as one-to-one personalization. 

How do you achieve hyperpersonalization at scale? 

Today’s at-scale personalization relies extensively on first-party data. So how does it work, from beginning to end? 

Start with data-driven insights

As with most things marketing, you should start with the data. Typically, first-party data serves as the base for your campaigns and insights. You can also use zero-party data gleaned from your customers (through surveys, for example).

You should then refine this data with an identity graph partner to create unique profiles for each customer you’re reaching. This allows you to target each of your customers across channels and devices seamlessly.

Automation & AI 

Machine learning and AI help process vast amounts of data, predicting user behavior and serving personalized content in real time. While most identity graphs use the help of algorithms to process data and create identities, new tech–agentic AI and, of course, LLMs–are also finding use cases in hyperpersonalization efforts. 

While the industry used to rely on broad segments and personalization (especially in copy) done manually, the process limited scalability. Any increase in segmentation meant more money, time, and effort spent on generating creative assets–to the point where increased segmentation just didn’t make financial sense. Generative AI’s involvement, however, can change that.

Omnichannel Personalization 

While early personalization was about segmenting users (and segmentation is still crucial), now, when you combine two or more segments, you can get even more granular with your approach. With the help of an identity graph partner, you can target users across channels and devices. 

Combine that with automated recommendations and LLM assistance, and you get closer to the dream of one-to-one personalization. 

What if customers think it’s creepy?

The consequences of coming off as “creepy” to prospects or customers should be on every marketer’s mind, especially given all the hype around one-to-one personalization. 

Just because you can, doesn’t always mean you should. Getting the messaging right matters. 

SmarterHQ says if when customers have a “creepy” interaction with a brand, over 60% will cut ties–and also tell their friends and family (68%). 

That said, according to a study from McKinsey, 71% of customers expect personalized interactions. So how do you get the balance right?

One thing. Respect privacy.

That means following privacy regulations, securing user data, and respecting (and giving options for) users’ privacy choices. 

While stats show that many users respond to personalized offers, that doesn’t mean everyone wants a highly personalized experience. Give users the choice to opt out, or adjust their personalization settings. And be transparent about the data you’re using. 

What’s next? 

Marketers have talked plenty about how generative AI is going to revolutionize the industry (More personalization! More scalability! More granularity! More speed!). However, many marketers are still wary on genAI. In fact, nearly a third of CMOs avoid using genAI in marketing campaigns. 

In avoiding the tech completely, these CMOs don’t run the risk of making the mistakes of freewheeling marketers forgetting the importance of a good data and hygiene backbone. Yet, rather than instead risk getting left behind, perhaps it’s time for these CMOs to enhance and verify their data–and then take a leap into trying out an LLM or two. 

For the 70% of CMOs that do use genAI: with the tech moving along at a breakneck pace and one-to-one use cases coming sooner than soon, it’s vital to keep in mind the basics of data hygiene, privacy standards, and customer needs and wants.