
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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Study reveals that brands with more mature identity programs were significantly more likely to be successful in achieving their key objectives Tapad, a part of Experian, a global leader in cross-device digital identity resolution and a part of Experian, has commissioned Forrester Consulting, part of a leading research and advisory firm, to conduct a new study that evaluates the current state of customer data-driven marketing and explores how marketers can use identity solutions to deliver privacy safe and engaging experiences, in an evolving data landscape. The study highlights the changing ground rules for digital marketing and the threat that poses to marketers’ ability to deliver against long standing KPIs and campaign goals. Nearly two-thirds (62%) of respondents said that the forces of data deprecation will have a significant (40%) or critical (21%) impact on their marketing strategies over the next two years. Among those surveyed, identity resolution strategies have surfaced as an opportunity to create more powerful customer experiences, with 66% aiming to have it help improve customer trust and implement more ethical data collection and use practices, while nearly 60% believe it will point the way to more effective personalization and data management practices. Although organizations are eager to implement identity resolution strategies, a complex web of solutions and partners makes execution a challenge. For example, respondents report using at least eight identity solutions on average, across nearly six vendor partners, and they expect that fragmentation to persist in the ‘cookieless’ future. Additionally, brands’ identity resolution technologies typically represent a patchwork of homegrown and commercial solutions. Eighty-one percent of respondents use both in-house and commercial identity resolution tools today, and 47% use a near-equal blend of the two. Despite the challenges, many brands have the foundation for a strong identity resolution strategy in place, and they are thriving as a result. Specifically, more mature brands were 79% more successful at improving privacy safeguards to reduce regulatory and compliance risk, 247% more successful at improving marketing ROI, and over four times more effective at improving customer trust compared to their low-maturity peers. Additional insights include: Marketers Are Increasingly Playing a Key Strategic Role Within the Organization, But There is a Mandate to Demonstrate Value. Nearly three-quarters of respondents in our study agree the marketing function is more strategically important to their organization than it used to be, while almost two-thirds agree there’s more pressure than ever to prove the ROI or business performance of their activities. Consumers Expect Brands to Deliver Engaging Experiences Across Highly Fragmented Journeys: Tapad, a part of Experian found that 72% of respondents agree that customers demand more relevant, personalized experiences at the time and place of their choosing. At the same time, 67% of respondents recognize that customer purchase journeys take place over more touchpoints and channels than ever, and 59% of respondents agree that those journeys are less predictable and linear than they once were. Marketing Runs on Data, But the Rules Governing Customer Data Usage are Ever-Evolving: According to the study, 70% of decision-makers agree that consumer data is the lifeblood of their marketing strategies – fueling the personalized, omnichannel experiences customers demand. At the same time, 69% of respondents recognize that customers are increasingly aware of how their data is being used. At least two-thirds agree that data deprecation, including tighter restrictions on data use (66%), as well as operating system and browser changes impacting third-party cookies (68%) means that legacy marketing strategies are unlikely to remain viable in the long-term.“ Our latest survey findings give us a better understanding of how our customers and other companies around the world are trying to master the relationship between people, their data and their devices,” said Mark Connon, General Manager at Tapad, a part of Experian. “This research shows why it's fundamental for the industry to continuously work to develop solutions that are agnostic. Tapad, a part of Experian has worked tirelessly to deliver on this with our Tapad Graph, and by introducing solutions like Switchboard to help the evolving ecosystem and in turn helping customers reap the benefits of better identity in both short and long-term.” The study is founded on an online survey of over 300 decision-makers at global brands and agencies, which was fielded from March to April, 2021. Data deprecation and identity are fast-developing, moving targets, so this study delivers targeted insights and recommendations for how to prepare for coming shifts in customer data strategies – whether they manifest tomorrow or a year from now. Get started with The Tapad Graph For personalized consultation on the value and benefits of The Tapad Graph for your business, email Sales@tapad.com today!

Marketers are always challenged to expand sales beyond “business as usual,” while being good stewards of company resources spent on marketing. Every additional dollar spent on marketing is expected to yield incremental earnings—or else that dollar is better spent elsewhere. You must be able to determine return on advertising spend (ROAS) for any campaign or platform you add to your marketing mix. A key driver of positive ROAS is incremental customer actions produced by ad exposure. Confident, accurate measurement of incremental actions is the goal of an effective testing program. Why do we test campaign performance? Because demonstrating incremental actions from a campaign is a victory. You can keep winning by doing more of the same. Not finding sufficient incremental actions is an opportunity to reallocate resources and consider new tactics. Uncertainty whether the campaign produced incremental actions is frustrating. Ending a profitable marketing program because incremental actions were not effectively measured is tragic. Test for success When you apply rigorous methods to test the performance of campaigns, you can learn to make incremental improvements in campaign performance. The design of a marketing test requires the following: Customer Action to be measured during the test. This action indicates a recognizable step on the path to purchase: awareness, evaluation, inquiry, comparison of offers or products, or a purchase. Treatment, i.e., exposure to a brand’s ad during a campaign. Prediction regarding the relationship between action and treatment (e.g., Ad exposure produces an increase in purchase likelihood). Experimental design is the structure you will create within your marketing campaign to carry out the test. Review of results and insights. Selecting a customer action to measure Make sure that the customer action you measure in your test is: Meaningful to the campaign’s goal. What is the primary goal of the campaign? Is it brand awareness? Web site visits? Inquiries? Completed sales? An engagement by the customer. Your measurement should capture meaningful, deliberate interaction of consumers with the brand. Attributable to advertising. There should be a reasonable expectation that ad exposure should increase, or perhaps influence the nature of customer actions. Abundant in the data. Customer action should be a) plentiful and b) have a high probability of being recorded during the ad campaign (in other words, a high match rate between actions and the audience members). Selecting campaign treatments It is best for treatments to be as specific as possible. Ad exposures should be comparable with respect to: brand and offer, messaging, call to action, and format. Making a prediction This is the “hypothesis.” Generally, you assume that exposure to advertising will influence customer actions. To do this, you need to reject the conclusion that exposure does NOT affect actions (the “null hypothesis”). Elements of an experimental design An attribution method that links each audience to their purchase action during the test. This consists of a unique identifier of the prospect which can be recognized both in records of the audience and records of the measured action during the measurement period. A target audience that receives ad exposure. A control audience that does not receive ad exposure. It provides a crucial baseline measurement of action against which the target audience is compared. Time boundaries for measurement, related to the treatment: Pre-campaign Campaign Post-campaign Randomly selected audiences (recommended) Some audience platforms, such as direct mail and addressable television operators, feature the ability to select distinct audience members in advance. Randomly selected audiences can generally be assumed to be similar in all respects except ad exposure. The lift of the action rate is simple to calculate: Campaign Lift = (Action Rate (target) / Action Rate (control)) -1 Non-randomly selected audiences are more difficult, but still possible, to measure effectively. There may be inherent biases between them that may or may not be obvious. To measure campaign performance, we must first account for any pre-existing differences in customer actions, and then adjust for these when measuring the effect of ad exposure. Typically, the pre-campaign period (and possibly the post-campaign period as well) are used to obtain a baseline comparison of actions between the two audiences. This is a “difference of differences” measurement: Baseline lift = (Action Rate (target) / Action Rate (control)) -1 Campaign Lift = (Action Rate (target) / Action Rate (control)) -1 Net campaign lift (advertising effect) = Campaign Lift – Baseline Lift Analyzing results and insights How large is the lift? This is generally expressed as a percentage increase in action rate for the target audience vs. the control audience. Are we confident that the lift is real, and not just random noise in the data? This question is answered with the “confidence level.”. 95% confidence means the probability of a “true positive” result is 95%; and the probability of a “false positive” due to random error is 5%. What was the campaign cost per incremental action? If you also know the expected revenue from each incremental action, you can project out incremental revenue, from which you can calculate return on ad spend. Other insights: Do the results make directional sense (we would hope that ad exposure will cause an increase in customer actions, not a decrease)? Does action rate generally increase with the number of ad exposures? Summary Well-designed testing and measurement practices allow you to learn from individual advertising campaigns to improve decision-making. The ability to draw confident conclusions from campaigns will allow experimentation with different strategies, tactics, and communication channels to maximize performance. These test-and-learn strategies also enhance your ability to adjust to marketplace trends by monitoring campaign performance. To learn how Experian’s solutions can help you measure the success of your marketing campaigns, watch our short video, or explore our measurement solutions.

It’s almost that time of the year again, the time to put away fourth of July merchandise and replace it with this year's favorite superhero backpacks. It’s almost back-to-school season, and parents and kids from kindergarten to college are preparing for school's "new normal." To navigate the challenge of 2021, Experian’s Marketing Analytics team is sharing Back-to-School shopping season insights with you. Download the eBook to learn more. Our outlook about this year's Back-to-School shopping season can help you better plan and improve your marketing effectiveness. The report covers who's actively shopping for school supplies, whether they're shopping in-person or online, and what they're buying this year. Here's a summary of what you'll learn in the report: Who (specifically) is shopping for back-to-school supplies this year? More than half of online searches related to Back-to-School were made by a small set of consumer segments. We’ve identified 4 Mosaic® groups as being in-market for back-to-school merchandise. To find these types of consumers, we used online behavioral data and filtered for households with school-age children between 5 and 15 years old. Each group, such as Flourishing Families, share similar shopping behaviors and needs. While each group of consumers has a need for Back-to-School merchandise, they have different circumstances that require more personalized marketing. Let's break down each Mosaic® group to better understand their size and key features so that you can build more personalized messaging. Contact us for segments and insights specific to your brand. Power Elite As you can see in our Mosaic® product brochure, Power Elite is categorized as Group A. This is the largest group analyzed in the report, accounting for 4.5 million U.S. households. Here are the Power Elite consumer types actively shopping for back-to-school merchandise this year: A01: American Royalty A03: Kids and Cabernet A04: Picture Perfect Families Key Features: Wealthy Highly Educated Politically conservative Purchase housewares and electronics in store Vacation and fitness retail influencers Luxury lease cars Flourishing Families Also called Group B in this report, Flourishing Families is comprised of 3.7 million U.S. households. Active consumer types: B07: Across the Ages B08: Babies and Bliss B09: Family Fun-tastic Key Features: Affluent Charitable contributors Athletic activities High-priced children’s clothing Home products & furnishings Sporting good Suburban Style Suburban Style, also Group D, is made up of 2.9 million U.S. households. Active consumer types: D15: Sport Utility Families D16: Settled in Suburbia Key Features: Comfortable lifestyle Ethnically diverse Politically diverse Instagrammers Children’s games Wholesale members Family Union The Family Union group, Group I, is the smallest of those analyzed in this report, but still a respectable size: 1.2 million U.S. households. Active consumer types: I31: Hard Working Values Key Features: Bilingual Married with kids Large households Hunting clothing Automotive tools Will they shop online or in stores? Prepare for a return to in-store shopping as the US moves post-pandemic. These consumers have shopped in-store for Back-to-School and have trended toward in-store shopping as the vaccine was distributed. Mobile location data shows these consumers actively shopped in-person during the 2019 Back-to-School season, and are shopping in-person again post-pandemic. Experian analyzed consumer mobile location data for big box retailers, department stores, malls and apparel-accessory stores since June 2019. The aggregated number of visits was indexed each month against 12-month average of that respective year. An index higher than 100 indicates shopping behavior that month was higher than the average of that year. An index less than 100 indicates shopping behavior that month was less than the average of that year. Planning store layouts and inventory will be more important this year for marketers as consumers return to the stores for Back-to-School shopping needs. What will they buy? Plan for Back-to-School product composition to be like pre-pandemic while you plan your inventory. Keep an eye on local outbreak risk which dictates whether school districts will pivot to remote learning. Product composition during the 2020 Back-to-School season was skewed away from apparel and towards virtual learning materials, such as home office supplies and technology, but should revert to pre-pandemic behaviors. Using ConsumerViewTM Transactional data, we compared consumer product composition during the 2019 and 2020 back-to-school shopping seasons. Children’s Apparel and Accessories: share was smaller in 2020, and was a more dramatic impact for Groups A, B, and D. Books: Groups B and D saw an increased share in 2020, but Groups A and I saw little change. Home Office: share was greater in 2020 for all groups, particularly Group A. Computers: share was greater in 2020 for all segments, particularly Group I Want to learn more? Improve your marketing ROI and grow your business during back-to-school season using Experian’s new Discovery Platform. No sign-up required: watch the demo to learn how retailers like you can use The Discovery Platform™ to track online versus in-store shopping and safely navigate evolving back-to-school consumer behaviors.