Remember the early days of digital marketing where 1 out 2 emails was opened, 1 out of 4 Ads was clicked and mass marketers controlled the flow of communication?

All of us know that it’s a different world today. Consumers are setting the terms under which conversations and interactions take place. We are expressing the right to ignore messages we find irrelevant: be it skipping DVRs, blocking Ads or opting out of emails. We are more likely to pay attention to a message that speaks directly to our own situation, needs, context, priorities and desires than to a message that speaks to a group of people that we happen to be a part of. We control what gets our attention, and a marketing message that’s not personally relevant to us gets ignored.

So how do we define relevance? Well, It would be great to say there’s a simple secret that lets us quickly and easily figure out whether a marketing message is relevant to a consumer. But because relevance means different things to different people, it’s an abstraction, which makes it difficult to define. I believe the following definition applies well to many marketing situations:

Think about the last time you read an offer that was emailed to you, or a YouTube Ad that you gave your attention to, or a product recommendation you saw on a shopping site. How relevant to you was this information? Do you think those brands understood you well?

Brands exhibit a poor understanding of the individual target consumer

• Consumers say that only 20% of online content from brands is relevant to them.
• Consumers say that only 5% of subscribed emails from brands are relevant to them.
• 91% of consumers opt-out from previously subscribed emails. Out of them, 46% are driven to brand defection because the messages are simply not relevant^.
• 41% of consumers say they would consider ending a brand relationship owing to irrelevant promotions, and an additional 22% say they would definitely defect from the brand due to irrelevance^.
• Of the advertising that is seen, only 0.06% of people actually click or tap on it and only 0.03% of ads that consumers see from brands are relevant to them^.

This relevance failure, in the digital marketing world, is being caused by 4 main factors:

Sparse and stale data: Causing a large void in consumer intelligence

A brand’s understanding of a consumer is based on the data they have about that consumer. Using this data, brands develop an audience profile about that consumer. And based on audience profiles, marketers slot them into groups, or segments of people within martech platforms such as DMPs, marketing automation, CMS etc. Today, this concept of segmentation forms the basis for martech strategies under the umbrella of targeting & personalization. The idea here is that targeting a consumer based on their segment would make for an interaction that’s more relevant to the consumer than a spray-and-pray strategy.

Today, a savvy brand usually collects the following types of data about the consumer:

• Data from direct interactions with the consumer, which could include online browsing behavior on the brand’s sites, purchase transactions, social data that the consumer granted the brand access to, etc.
• Data from its partners who have acquired data about the consumer, which its partners decided to share with the brand through some mutually beneficial agreements.
• Generic data points such as demographic or psychographic data of people in their target market.

While all of this is more data than what brands were collecting a few years ago, it’s insignificant compared to the amount of data the average consumer generates with their digital exhaust.

Today a consumer spends a considerable chunk of time online everyday. Online activities are done across many platforms, publishers and brands that the amount of time a consumer spends on the digital properties of a single brand is so little that most brands are only able to get a little sliver of a consumer’s digital exhaust. Additionally, much of the behavioral data that brands have about a consumer is stale because a consumer’s situation, needs, context, priorities, desires, change all the time.

Could this gap be closed to give brands access to the consumer intelligence they need? More about this later, but in my view, this is one of the primary reasons for poor relevance and needs to be addressed.

Consumer Privacy: What’s more important, relevance or trust?

If there is one thing in direct conflict with data collection, it’s data privacy. Data privacy issues range from the unpleasant “creepy” experiences to those bordering on unethical or harmful to a person’s identity. The lines of data privacy are fuzzy and personal to an individual. Is social listening the same as overhearing two friends having a conversation in a public place? Is location tracking the same as a salesperson being aware of you in a store? Is retargeting the same as a friend telling they found the same item you like on sale? And what about relevance predictions that could be perceived as completely wrong and presumptive?

For a brand, the battle between data collection and privacy is a battle between relevance and trust. Can this be addressed in a way that meets both these demands, where brands obtain the necessary consumer intelligence while consumers get complete control of their privacy? I believe there is an approach to solving this and will come to explain how, but there is no solution to consumer relevance without a solution to this problem.

Poor Measurement: There is no metric to measure relevance

How do brand’s measure relevance? The unfortunate answer is, they usually don’t. Even the oldest form of digital marketing: email marketing has no metric that indicates how relevant an email is to its target consumer. Some of the email metrics commonly used are open rate (OR), click through rate (CTR) and a click through open rate (CTOR). One might argue that CTOR could be a measure of relevance, but what made the consumer open that email in the first place? Was it something in the subject line that was of relevance to the consumer? How do we factor that into consideration? On the other hand, CTR is also not an accurate measure of relevance because the message itself could be relevant to the consumer’s needs but the content of the email wasn’t compelling enough for the consumer to click-through.

Due the absence of a metric, problems with conversions are often wrongly attributed to lack of consumer attention instead of a lack of relevance. And while diminishing consumer attention is a real problem, having a primary focus on solving the attention problem instead of the relevance problem is wasteful for brands.

Could relevance be measured? And if it could, then how would digital marketing be different if brands knew how truly relevant their messages were to their target audience? More about this later, but in my view the lack of metrics and measurements on marketing message relevance is one of the primary reasons why we see brands continue to bombard us with irrelevant messages that we ignored in the past.

Segmentation Technology: The inadequate approach to getting personal

Since the beginning of this decade we have been hearing buzzwords in martech such as 1-1 digital experiences, hyper-personalization, precision marketing, 1-1 ad targeting, contextual marketing, and so on. Almost every DMP, campaign management, email marketing, and experience management platform today talks about enabling some type of 1-1 targeting or personalization. All these platforms use audience segmentation technologies. But there is an inherent problem with this approach:

Peoples decisions are not just affected by their likes. They are also affected by their social environment, their culture, their economic conditions, their habits, their geography, their current needs and their current context, and even their current state of mind. All of these are heterogeneous data points, while segmentation is about finding homogeneity across consumers based on certain measures.

Segmentation technologies use algorithms such as K-means or Latent Class Analysis (LCA), which are geared towards finding similarities across data. And while these techniques work for broad correlations across homogeneous data, they are poor predictors of deriving meaning from the heterogeneous pieces of a consumer’s digital exhaust.

Can technology help make sense of a consumer’s deep digital exhaust and make relevance predictions from it? I will delve deeper into this, but in my view martech has a gap to bridge between the outcomes of segmentation and the expectations of relevance.

Conclusion

These reasons show that today, brands have a poor understanding of their target consumer at an individual level. And without this understanding it is unrealistic for marketing technology vendors today to claim that marketers will be able to effectively do 1-1 personalization and targeting using tools such as DMPs, Campaign management, A/B testing etc. Marketers should be skeptical about hype around 1-1 personalization and targeting capabilities from marketing technology vendors.

Let’s take a look at the consequences to a brand of misunderstanding their target consumers.