
Originally appeared on MarTech Series
Marketing’s understanding of identity has evolved rapidly over the past decade, much like the shifting media landscape itself. From the early days of basic direct mail targeting to today’s complex omnichannel environment, identity has become both more powerful and more fragmented. Each era has brought new tools, challenges, and opportunities, shaping how brands interact with their customers.
We’ve moved from traditional media like mail, newspapers, and linear/network TV, to cable TV, the internet, mobile devices, and apps. Now, multiple streaming platforms dominate, creating a far more complex media landscape. As a result, understanding the customer journey and reaching consumers across these various touchpoints has become increasingly difficult. Managing frequency and ensuring effective communication across channels is now more challenging than ever.
This development has led to a fragmented view of the consumer, making it harder for marketers to ensure that they are reaching the right audience at the right time while also avoiding oversaturation. Marketers must now navigate a fragmented customer journey across multiple channels, each with its own identity signals, to stitch together a cohesive view of the customer.
Let’s break down this evolution, era by era, to understand how identity has progressed—and where it’s headed.
2010-2015: The rise of digital identity – Cookies and MAIDs
Between 2010 and 2015, the digital era fundamentally changed how marketers approached identity. Mobile usage surged during this time, and programmatic advertising emerged as the dominant method for reaching consumers across the internet.
The introduction of cookies and mobile advertising IDs (MAIDs) became the foundation for tracking users across the web and mobile apps. With these identifiers, marketers gained new capabilities to deliver targeted, personalized messages and drive efficiency through programmatic advertising.
This era gave birth to powerful tools for targeting. Marketers could now follow users’ digital footprints, regardless of whether they were browsing on desktop or mobile. This leap in precision allowed brands to optimize spend and performance at scale, but it came with its limitations. Identity was still tied to specific browsers or devices, leaving gaps when users switched platforms. The fragmentation across different devices and the reliance on cookies and MAIDs meant that a seamless, unified view of the customer was still out of reach.
2015-2020: The age of walled gardens
From 2015 to 2020, the identity landscape grew more complex with the rise of walled gardens. Platforms like Facebook, Google, and Amazon created closed ecosystems of first-party data, offering rich, self-declared insights about consumers. These platforms built massive advertising businesses on the strength of their user data, giving marketers unprecedented targeting precision within their environments.
However, the rise of walled gardens also marked the start of new challenges. While these platforms provided detailed identity solutions within their walls, they didn’t communicate with one another. Marketers could target users with pinpoint accuracy inside Facebook or Google, but they couldn’t connect those identities across different ecosystems. This siloed approach to identity left marketers with an incomplete picture of the customer journey, and brands struggled to piece together a cohesive understanding of their audience across platforms.
The promise of detailed targeting was tempered by the fragmentation of the landscape. Marketers were dealing with disparate identity solutions, making it difficult to track users as they moved between these closed environments and the open web.
2020-2025: The multi-ID landscape – CTV, retail media, signal loss, and privacy
By 2020, the identity landscape had splintered further, with the rise of connected TV (CTV) and retail media adding even more complexity to the mix. Consumers now engaged with brands across an increasing number of channels—CTV, mobile, desktop, and even in-store—and each of these channels had its own identifiers and systems for tracking.
Simultaneously, privacy regulations are tightening the rules around data collection and usage. This, coupled with the planned deprecation of third-party cookies and MAIDs has thrown marketers into a state of flux. The tools they had relied on for years were disappearing, and new solutions had yet to fully emerge. The multi-ID landscape was born, where brands had to navigate multiple identity systems across different platforms, devices, and environments.
Retail media networks became another significant player in the identity game. As large retailers like Amazon and Walmart built their own advertising ecosystems, they added yet another layer of first-party data to the mix. While these platforms offer robust insights into consumer behavior, they also operate within their own walled gardens, further fragmenting the identity landscape.
With cookies and MAIDs being phased out, the industry began to experiment with alternatives like first-party data, contextual targeting, and new universal identity solutions. The challenge and opportunity for marketers lies in unifying these fragmented identity signals to create a consistent and actionable view of the customer.
2025: The omnichannel imperative
Looking ahead to 2025 and beyond, the identity landscape will continue to evolve, but the focus remains the same: activating and measuring across an increasingly fragmented and complex media environment. Consumers now expect seamless, personalized experiences across every channel—from CTV to digital to mobile—and marketers need to keep up.
The future of identity lies in interoperability, scale, and availability. Marketers need solutions that can connect the dots across different platforms and devices, allowing them to follow their customers through every stage of the journey. Identity must be actionable in real-time, allowing for personalization and relevance across every touchpoint, so that media can be measurable and attributable.
Brands that succeed in 2025 and beyond will be those that invest in scalable, omnichannel identity solutions. They’ll need to embrace privacy-friendly approaches like first-party data, while also ensuring their systems can adapt to an ever-changing landscape.
Adapting to the future of identity
The evolution of identity has been marked by increasing complexity, but also by growing opportunity. As marketers adapt to a world without third-party cookies and MAIDs, the need for unified identity solutions has never been more urgent. Brands that can navigate the multi-ID landscape will unlock new levels of efficiency and personalization, while those that fail to adapt risk falling behind.
The path forward is clear: invest in identity solutions that bridge the gaps between devices, platforms, and channels, providing a full view of the customer. The future of marketing belongs to those who can manage identity in a fragmented world—and those who can’t will struggle to stay relevant.
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Oh, how the time flies. We’re already into March of this young 2017, and as much as it pains me to say, it’s probably about time we begin thinking about Holiday 2017. But such is life, and if we have to do it, we might as well do it right. So how do we start to think about “doing it right” in Holiday 2017? Well, as an analytics guy, I might be biased, but I believe the data contains the answers. While there are obviously many more factors, which you can see in our 2016 Holiday Insights webinar, data points and concepts to consider, let’s dive into a few interesting 2016 holiday marketing insights that can help you begin prepping for this upcoming holiday. First, a quick note on the data in this post – all data is collected from a holistic study that examines a single inbox designed to mimic the “average” consumer…and since, on average, most email subscribers aren’t doing more than opening, we make sure that no content is clicked through and no transactions are recorded. We then coded each email on a variety of different metrics, some of which you’ll see below. Finally, in order to increase the robustness of our evaluations, we examine each brand within the study individually, first by calculating the overall average KPIs for the time period within the study. Then, we compare each mailing for each brand against that brand’s baseline, creating a +/- metric on a per campaign basis. Then, we average those metrics across each brand, creating a model for expected performance compared to the “typical” mailing. To illustrate, let’s examine the following (simplified) sample table. Suppose we want to know what the “expected” impact of X for a brand’s marketing program. In the table below, we’ve gathered the open rates for campaigns that exhibit X for brands A & B. The table also shows the long-term average open rates for each brand, and the percent change of the campaigns compared to that baseline. By averaging those results, we get a holistic “expectation model” for campaigns exhibiting X. In this sample, we can generalize to say, any campaign including X for any brand should, on average, expect an open rate that’s about 9% lower than their long-term baseline. Peak week’s heavy influence I don’t think it’s an earth shattering revelation that the data shows peak week’s performance as being significantly above average, but it often surprises me to see just how much better it does than the surrounding time periods. This is easily seen in the chart below, where I’ve plotted every single mailing’s +/- revenue per email over time. I’ve categorized the mailings as “holiday” vs “standard” to see if there were any significant patterns. As you can see, most mailings performed worse than the baseline, due to the baseline being so heavily influenced by peak week, with mailings performing 2-5x better than average. What does it mean? Peak week’s impact is large enough that brands might want to consider viewing it in a vacuum, away from the days surrounding it, in order to get a better read on the overall health of their email program. Your brand should expect much better than average results throughout peak week, of course, and if the data doesn’t show that, you might be in trouble! Holiday messages get a boost with subject line mentions Throughout holiday, creative treatments and copy call out or hint towards specific holidays. Overall, those holiday messages generally perform better than average across all KPIs, as shown below. These results improve even further when those specific holidays are mentioned within the subject line, with Black Friday mailings seeing the largest increase when combined with a subject line mention. What’s most interesting to me, however, is how negatively Christmas themed mailings were affected when the holiday was called out in the subject line (a net 25% decline in revenue per email). All of these results may arise from a case of self-selection bias, whereas we should expect a specific holiday message to do better than average simply because it’s a specific holiday theme. This concept works in a few ways: a) If a company is giving a great offer, they might want to make it feel more “special” by creating a unique theme and selling concept around it (the holiday) . b) If a company has decided to devote resources to creating a mailing designed around a specific holiday, likely requiring a change in the creative process / design or additional strategizing around copy and positioning, then they will likely want to attach stronger offers to make the increased effort worth it. c) Due to the date of deployment, companies are more likely to both add strong offers and devote creative resources to a mailing because they implicitly understand that customers are more likely to engage on those days due to larger economic or societal trends and to differentiate themselves from the noise. d) If companies are devoting good offers or creative treatments to a specific holiday (or both), then the best companies realize that they should signal this with a mention in the subject line, leading any message with a holiday mention to of course do better What does it mean? The data highlights a particularly interesting in holiday email analytics – understanding causal effects. Sure, theming a mailing around a specific holiday might be the cause for the improved metrics, but it’s more likely that we assign holiday themes to a mailing that would have done well already. This is a much larger concept to think about in marketing, and the main takeaway is to be highly critical of any causal inference you make regarding performance. There are bigger factors at play with subject lines than length I have a deep-seated skepticism of any broad subject line analysis – subject lines are a quagmire of entanglements, where no single feature can ever be considered in a vacuum against any other feature. And yet, a tiny bit of Googling reveals hundreds of posts about subject lines, ranging from improving open rates with personalization and emojis to the grand-daddy of them all – shorter subject lines improve open rates. 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