The Hidden Dangers of Over-Personalization in Marketing

The essentials

• Avoid over-personalization. Over-personalization in marketing can harm brand identity, limit customer discovery, and reduce overall marketing effectiveness.
• Understanding the limits of AI. While AI can improve personalization, it also has limitations and risks, such as data quality issues and overtargeting.
• Prioritize strategic balance. The 1:1 marketing paradigm should be viewed as a guiding principle, not an absolute goal. Marketers must ensure that AI-driven personalization efforts do not overshadow brand messaging or core business objectives.

Until recently, very few brands really needed to think about how much personalization was too much. Indeed, personalization has been a major marketing trend for nearly two decades.

However, even as brands have acquired new and increasingly sophisticated ways to personalize content, their aspirations have been limited by many realities, including the capabilities of their marketing platform, data quality and availability, time and resources, and performance visibility limitations.

Today, many people (and startups) are convinced that all the boundaries have been removed. Now, brands can finally achieve their long-held goal of the 1:1 marketing paradigm: sending the right message to the right person at the right time. And they can do this by having generative AI craft 1:1 messages for each individual who has opted in to receive marketing messages via email, SMS, and push, and then using machine learning to optimize the audiences and timing of each message.

There are some major problems with this way of thinking. Here’s how over-personalization in marketing can hurt your overall strategy.

The 1:1 marketing paradigm is Aspirational

This isn’t a literal or absolute goal. The idea was never that brands should uniquely personalize every message they send, nor that 100% of a message should be personalized. Personalizing some messages is a good thing. And personalizing part of a message is plenty.

Until now, it didn’t need to be said. But generative AI has led to a lot of thinking about whether this can be done and a lot less about whether that should be done. And today, when brands think about how to send the right message to the right person at the right time, they think “right” means hyper-personalized.

Let’s look at some of the limitations of the 1:1 marketing paradigm and highlight why over-personalizing marketing can actually hurt your performance.

Send it RIGHT Message

Excluding general, universal messages in favor of only personalized messages can potentially lead to a number of problems.

Personalization can compromise business operational objectives

Marketing is about building relationships. And relationships involve two parties meeting somewhere in the middle. Yes, marketers want to serve their customers, but they also need to serve their business. Doing the former doesn’t automatically accomplish the latter.

For example, if you have excess inventory of a particular product, you’ll likely want to promote it to more customers than just those who have already purchased it or purchased another product in the same category. Similarly, excess inventory in certain stores should prompt you to promote special offers or in-store discounts to all of your customers near those stores, not just those customers who have demonstrated in-store purchasing behaviors.

For new product launches, you’ll likely promote it more broadly to customers who purchased that product’s category, but it would be a missed opportunity if you didn’t promote it more broadly than that.

Personalization can limit discovery and inspiration

If you only show your customers products similar to those they have already expressed interest in, you are limiting their growth as a customer. In effect, you are putting them in a box.

For example, I recently heard about a nonprofit that sent an email campaign that highlighted programs the recipient might consider supporting, but only highlighted groups that were in the same category that each subscriber had donated to in the past. So if you donated to an educational charity, you were only considering educational charities as options, which took away the ability for them to explore charities with other missions and reduced overall donations.

Personalization should not overshadow the brand message

What are your brand values? What image do you want to convey? Consumers, especially younger ones, want to know what your company stands for. This type of positioning should not be personalized, nor should it be neglected in favor of more personalized content.

We recently had a client who significantly scaled back their AI-based personalization strategy for this very reason. It was overshadowing the brand message, and they know that brand building is important.

Brands want to create a shared experience

Most brands don’t want every customer to have completely personalized experiences because it can be detrimental to the brand’s identity. We work with many enterprise clients, and they all have rigorous and detailed brand guidelines that cover brand voice, acceptable imagery, design parameters, and more. Even a generative AI system trained on these guidelines wouldn’t be able to create a consistent enough experience to satisfy most businesses.

And with highly regulated industries — like pharmaceuticals, government, and insurance — letting generative AI create text that can be published without human review isn’t an option.

Returns on content personalization are diminishing

There are many elements you can personalize in a message. In fact, we’ve identified over 170 criteria in our Segmentation and Personalization Ideas Checklist (free to download and form-free). But the truth is, only a few of them will really make a big difference to a particular message. The rest will have little to no effect, or will even hurt performance by being a distraction.

See all

It’s also worth noting that as brands further personalize their content to the nth degree using generative AI, measuring the impact of all these variations becomes infinitely more complex to track and gain insights across all audience segments.

Related article: Generative AI for email personalization: A hallucination wrapped in confusion

At RIGHT Person

Overtargeting using AI is also a significant risk.

Audiences can become too targeted

Segmentation and suppression rules are essential tools to maintain audience engagement and minimize churn, as irrelevant and overly frequent messages are still the main drivers of unsubscribes. However, brands should monitor the algorithms their machine learning models use when it comes to audience selection so as not to become too demanding.

For example, I recently heard about a brand that was using machine learning to reduce churn. To achieve its goal, the algorithm aggressively suppressed emails to new subscribers because new subscribers churn far more than existing subscribers. This had a significant negative impact on sales. This experiment led the brand to realize that it needed different rules for new subscribers than existing subscribers because the behaviors of these two groups are very different.

I’ve also heard of cases where brands have relied too much on segmented campaigns, where they’ve only sent messages to subscribers who have already shown interest in the campaign topic. As a result, many of their new, less-engaged subscribers have received too few campaigns, depriving them of engagement opportunities, leading to an increase in the percentage of inactive subscribers.

Related Article: 7 Factors That Determine Email Deliverability

Engagement-based targeting isn’t perfect

That’s because engagement data isn’t perfect. Privacy restrictions like Apple’s Email Privacy Shield and Link Tracking Protection make some engagement signals less reliable or fewer than they were in the past. And most brands haven’t centralized their customer data into a customer data platform or similar system. Instead, customer data is scattered across multiple channels and systems, giving them an imperfect picture of a subscriber’s engagement across multiple channels.

Given the realities of data quality and availability for most brands, data-driven targeting needs to be more forgiving. Having demanding algorithms only makes sense if you have accurate data.

Related article: 4 Mistakes Brands Make When Measuring Digital Marketing

At RIGHT Time

In some circumstances, brands may not have a reason to respect the most opportune time for a marketing message to reach an individual subscriber.

Optimizing send time doesn’t always make operational sense

Historical subscriber engagement time patterns are meaningless when they conflict with operational realities, such as:

• You want to generate visits to the store or restaurant during opening hours
• I wish to respond to call center requests during call center opening hours
• The need to distribute products or tickets at fixed times determined by suppliers
• Latest news, sporting event results (e.g. Super Bowl), etc.
• Promote a flash sale
• Promote sale products on Black Friday or other occasions

Again, the “right” time to launch a campaign is not just decided by the subscriber. The “right” time for the brand matters too, sometimes a lot.

Multiple campaigns per day make it useless.

If you’re sending multiple times a day, optimizing your send time typically doesn’t have a major impact on performance. For our customers who are sending two or three campaigns a day, they’re usually happy to space out their sends around business hours.

Related article: How to Prioritize the Ever-Shifting Target of Email Personalization

Know Your Limits: Avoid Over-Personalization in Marketing

All of this isn’t to say that personalization and AI aren’t good. They are. But like most things, they can be overdone.

Remember, the 1:1 marketing paradigm is meant as a North Star to guide you in the right direction. It was never meant to inspire you to launch into space. So, stay grounded in your use of AI.

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