Nielsen Annual Marketing Report: Learn how marketers’ trust in digital drives spend, despite challenges

Digital has made the customer journey more complex. With new metrics, platforms and tools, measuring that journey is even more difficult.

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Marketing is undergoing a transformation. Digital now captures more than half of all advertising spending in the U.S.

But how do marketers perceive the effectiveness of all the new channels at their disposal? Are those insights driven by measurement data they can trust? How does perception versus reality ultimately influence budget decisions?

Nielsen surveyed over 350 marketers around the globe to get answers and found that enthusiasm for digital was tempered by severe data quality issues and measurement challenges. Digital has made the customer journey more complex. With new metrics, platforms and tools, measuring that journey is even more difficult.

Visit Digital Marketing Depot to download the “Nielsen Annual Marketing Report: The Age of Dissonance – Marketer’s Trust in Digital Drives Spend Despite Challenges.”



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How to adapt your marketing in the consumer privacy era

The rules of marketing continue to change – especially in light of data measures like the CCPA and Apple ITP.

The post How to adapt your marketing in the consumer privacy era appeared first on Marketing Land.

Consumers now expect privacy-oriented marketing from all brands. This new normal of marketing means all businesses need a new customer engagement model – one that abides by consumer concerns regarding data collection and usage, but also enables them to deliver relevant, real-time, personalized messaging to opted-in prospects and buyers.

In this complimentary eBook from BlueConic, you will discover what your organization must do today to thrive in the increasingly data-conscious, constantly evolving marketing landscape. You’ll learn:

  • The downsides of legacy marketing technologies when it comes to tracking and updating customer consent
  • How achieving a unified single customer view with modern martech like a CDP enables efficient data liberation
  • Why identity is the core of the new customer engagement model and essential for improving your CX efforts
  • How to incorporate authentication, consent, and value exchange into your customer experience

Visit Digital Marketing Depot to download “The New Customer Engagement Model: How to Adapt Your Marketing in the Consumer Privacy Era.”



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Don’t misinterpret the data: Evidence-based advertising needs experience-based context

Data will always be the foundation of our evidence, but we need to consider our experience to structure and interpret this raw information.

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“That sounds like a great idea, but what does the data tell us?” In recent years, the principle of evidence-based advertising has taken hold of the industry, bringing the tension between advertising as a science and an art to the foreground. For some, like Professor of Marketing Science Byron Sharp, the answer is clear: in the world of Big Data, evidence must take precedence over conventional wisdom. But what exactly is evidence, and how is it best used?

Broadly speaking, evidence is information that provides a foundation for some belief. As such, raw data can certainly be used as evidence, but so can intuition. After all, I don’t need to consult a spreadsheet to be confident that the sun will rise each morning; my belief is founded on years of experience stored in a mental “database.” Advertising is no different.

This is not to say that hard numbers are gratuitous, or that industry expertise holds all the answers. We must recognize, however, that there are different kinds of evidence, and each serves a distinct function in the analytic and decision-making process. To tease out these distinctions, we can turn to the academic world, where evidence is precisely classified.

In academic work, evidence is divided into three categories: primary, secondary, and tertiary. These are distinguished by 1) where they come from, and 2) how they are stored. Primary sources are typically original accounts or physical evidence, while secondary and tertiary evidence layers interpretation and analysis over primary information.

Primary evidence

Raw consumer data is primary evidence: it expresses documented consumer behavior or literal consumer responses to some prompt. The results of ad effectiveness research, like breakthrough, branding, and brand-impact are all primary evidence; assuming participants responded honestly, they reflect the ad’s impact on consumer perspectives.

Unlike most primary evidence, ad data is typically modeled to make interpretation clear: likeability goes up when more respondents like the ad, brand, or product. But knowing how an ad affects research metrics isn’t in itself an insight, much less a strategy. Consequently, primary evidence must always be subjected to analysis, whose product is secondary evidence.

Secondary evidence

Analysis synthesizes research results into insights – actionable data-driven learnings – through deductive or inductive logic. The resulting secondary evidence is an interpretation of primary data: it uses facts to support conclusions about why consumers responded as they did, and what that suggests about the ad’s performance.

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Secondary sources represent what analysts think primary evidence (consumer responses) meant; they can guide our interpretation thereof, but their validity is contingent on whether the analysis was correct. A quarterly report, for example, may draw on primary evidence to more accurately represent consumer sentiment, but only by taking the risk of misinterpreting the data.

Tertiary evidence

This article is a primary source, as it expresses my perspective on evidence-based ads, but my article on the science of storytelling is a tertiary source: it brings together different analyses to make a broader point about the intersection of neuroscience and advertising. A year-end report that synthesizes quarterly analysis into an overarching narrative is also tertiary evidence.

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Tertiary sources are an aggregation of primary and secondary data, forming a curated body of knowledge; they allow us to compare analyses, and to develop a more authoritative interpretation. Our experience in the ad industry can be thought of as tertiary evidence: all the ads and research we have been exposed to nuance our interpretation of new data.

Using primary evidence

Despite its name, primary evidence is almost never the right place to start an investigation. Secondary and tertiary evidence can help us define our questions and develop hypotheses before we begin collecting primary data. When we finally do sit down with a dataset, we should again look to secondary and tertiary sources for context.

In fact, this is a process that most of us undertake without thinking. We design research with the aid of past successes and failures, and when we analyze results, we look for patterns and flags that we’ve seen before. Unfortunately, in doing so implicitly we may draw on personal assumptions or misguided conventional wisdom instead of evidence-based learnings.

It is here that the balancing act between evidence and experience begins. Disregarding experience squanders years of information we have collected in our mental database; taking its veracity and logical coherence for granted, however, can lead us to baseless conclusions. Secondary documents draw on primary evidence to help substantiate and vet our intuition.

Using secondary evidence

Secondary evidence is both the product of analysis, and a vital tool in the analytical process itself. When we interpret primary data, we create secondary evidence. As primary evidence is rarely contextualized, and often does not lend itself immediately to interpretation, it is usually necessary to draw on existing secondary sources to guide or corroborate our analysis. 

When looking at consistent consumer behavior, for example, we understand it likely represents a trend. We’ve seen this type of pattern before: regular observations of some phenomenon have led us to draw a reliable conclusion. In this case, we can rely on evidence-based intuition to analyze the data, but more complex conclusions may require additional evidence.

As noted, our intuition itself is often implicitly secondary evidence. It is important to remember, though, that all secondary evidence must point back to primary sources. It may seem obvious, but this distinction is what separates analysis from assumption. Tertiary evidence is a good way to evaluate secondary sources and the analysis they rely on.

Using tertiary evidence

Tertiary evidence is often a review of secondary sources that evaluates their merit and posits a higher-order conclusion. A collection of case studies in some research methodology or advertising practice is a prime example. Consistency in their results may suggest the existence of an advertising principle, while discrepancies could indicate methodological errors.

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The movement from primary to tertiary evidence is one of synthesis and abstraction. This can be extremely useful, but it also distances us from raw data: primary evidence. Tertiary sources are typically arranged to evidence a specific argument. Necessarily, this is to the exclusion of other insights that may be gleaned from the initial evidence.

In other words, tertiary sources result from the analysis of analysis. They can be useful both in evaluating completed research and identifying new research questions. Above all, they unite historical learnings to substantiate industry principles. By evidencing or challenging conventional wisdom, they advance our understanding of consumer behavior and advertising techniques.

Putting it all together

When we commission a new piece of research, we should always begin with extant secondary and tertiary evidence. Whether it is stored in slide decks or our memory, this historical research can inform what questions we ask, and how. That way, we won’t collect data that isn’t useful, and the data we do collect will be optimized to support the answers we need.

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Through analysis, we then convert these primary sources into secondary evidence. Again, our interpretation of the raw data will be nuanced by our experiences with similar ads or research. The insights we develop will thus yield usable and reliable conclusions about the ad, brand, or product in question. To present them, we should group them into a piece of tertiary evidence.

Much like the Pyramid Principle, different kinds of evidence build on one another to add meaning and mitigate misinterpretation. Hard data will always be the foundation of our evidence, but without drawing on the secondary and tertiary evidence of experience we are left without a reliable way to structure and interpret this raw information.

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Live Virtual Panel: Get answers to your burning B2B marketing questions

Join our expert panel for the answers to your demand gen, revOps, and marketing automation strategy questions.

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Are you ready to crush your 2020 demand gen goals? Join our expert B2B marketing panel and get the answers you need to take your organization to the next level.

In this “ask me anything” format, you’ll have demand gen and marketing technology experts addressing a curated selection of your questions about revOps, demand gen and marketing automation strategy. Join our moderator and commentator– LeanData OpsStar of the Year, Sara McNamara– and our expert panel to get all the expertise and none of the consultancy fees in this not-to-be missed, rapid-fire virtual event.

Don’t miss it! Register today and submit your questions for “Burning Questions Live Virtual Panel: Demand Gen & RevTech,” presented by Metadata.



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The data behind incrementality on Amazon

The key to driving incremental sales combines segmented bidding strategies, contextualizing ACoS metrics and proper campaign structure.

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Every marketer worth their salt is concerned about incrementality. Companies have, and should be, reevaluating their budgets, channels and service providers based on the ability to drive sales from advertising that they wouldn’t have captured otherwise.

When it comes to Amazon, this issue is particularly important, because current SERPs can naturally cannibalize an otherwise organic sale with an ad for the same product that shows up prior to the organic result. For marketers, the key to managing this issue and driving incremental sales is through a combination of segmented bidding strategies for brand, category, and competitor key terms, contextualizing Advertising Cost of Sale metrics, and proper campaign structure.

The non-incremental trap of branded keywords

Each of the larger term segments – branded, generic, and competitor – needs to be thought of in terms of the consumer’s place in the purchase funnel:

  • Brand keywords capture shoppers deepest in your purchase funnel
  • Competitor keywords capture shoppers that are deep in someone else’s purchase funnel
  • Category keywords capture shoppers at the top of your purchase funnel 

On Amazon, when a user searches for some variation on a brand name, the A9 algorithm generally does whatever it can to surface as many of those brand’s products as possible on that search page. That includes both top sellers, which will get the top organic placements, along with the long tail, which will occupy spots further down the page. Amazon takes the intent of the user – “I want to see products from this brand” – very seriously. This is borne out in the underlying data – it is much, much harder to rank organically on a category term, as compared to a branded term, as seen in the examples below.

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This shows why it’s a real challenge for your brand’s keywords to be incremental. It’s almost guaranteed that your relevant products will show up organically on the SERP – with your top sellers showing up high in the results. Additionally, consumers are more likely to click on the top few results of a branded search, as compared to generic searches.

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The biggest takeaway here is that advertising your top products on your own branded terms is a particularly bad practice. You’re capturing sales via paid placements you were likely to capture organically anyway. If brand defense is imperative, consider advertising new or longer-tail products on your branded terms instead. That way you’re defending your brand term, but you’re doing so by helping to sell products that aren’t yet ranking well organically, while still not cannibalizing sales of your top products.

Maximizing sales across category and competitor terms

In terms of incrementality, nothing is better than capturing a sale from your competitor. However, you’re likely to find that the ACoS of competitor keywords is significantly worse than that of generic or category keywords, as shown in the example below.

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The best strategy here depends a lot on your competitive landscape. Conquesting your competitor’s terms means having a deep understanding of the terms which you can reasonably bid against successfully. Terms relating to stronger competitors with deeper brand loyalty/recognition may necessitate a less aggressive strategy to control costs, while it may be well worth your while to bid forcefully against terms related to relatively weak competitors where it’s easier to pick off customers with your top products.

As opposed to branded terms, the universe of category keywords is understandably the largest on Amazon, with new relevant terms developing over time. In this larger and more dynamic environment, it’s important that you set bids based on the expected conversion rate of a given term.

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The issue here, which I’ve written about in a previous column, is that over 90% of category keywords do not get more than one click per day, given a constant bid. Additionally, with roughly 80 clicks being necessary to get a confident estimate of the true conversion rate of a given keyword, running this test could take nearly two and half months. Meanwhile, conversion rates change roughly every month – as one extreme example, think of the expected conversion rate for “easter candy” in April versus May or June.

To succeed with category keywords you must have an exploration strategy that values the rate of data acquisition. At my current workplace, we use a probalistic binary search model, that adjusts bids from very high to low in order to more quickly determine the expected conversion rate.

Outside of this more refined statistical method, what marketers can do to better find and exploit meaningful keywords on Amazon is deploy a more granular campaign structure. Because keywords define audience segments, each audience segment needs a different set of considerations in terms of aggressiveness and expectations.

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To spell this out, brand keyword campaigns should have high ROAS expectations, focus on emerging products, and get tested for incrementality. Competitor keyword campaigns should have the lowest ROAS expectations and focus on launching and dominant products. Finally, Category keyword campaigns should be expected to give you a break-even ROAS and must be handled with a strong exploration strategy. These overarching themes are important to keep in mind as you scale your marketing efforts on Amazon because they are critical to driving incremental sales growth.

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Align your marketing plan with your analytics measurement plan

Audit your analytics on a regular basis to ensure the data you are collecting is as accurate as possible and that all data that should be collected is reported.

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All great marketing departments and teams should have a marketing plan and know it intimately. What is surprising is how often when conducting an analytics audit when the first thing I ask for is a copy of their marketing plan, how often I’m presented with a “deer in headlights” look or at best given the response “Oh we have one, but haven’t updated it in years. We just know it!”

Why is having a current marketing plan that is disseminated and known by your entire marketing team and other teams within your organization critical and how does this have anything to do with your corporate analytics? For the simple reason: Without one, how does the marketing team actually know what they should be doing. More importantly, how can success be measured?

The key to marketing success is to merge a marketing plan with a measurement plan into a unified plan.

Key measurement elements of a marketing plan

The first step in developing or validating a marketing plan is ensuring the marketing department’s mission statement aligns with the corporate mission statement. This is where many marketing teams make their first mistake. If the two don’t align properly, then how can the marketing department effectively obtain corporate buy-in and ensure their marketing efforts are effective in helping the organization meet its overall goals?

Once the marketing department has validated its mission statement, it needs to define specific objectives. Then, it is time to merge these elements with a measurement plan that defines specific marketing tactics that can be planned, budgeted for, approved, executed and measured.

 Clearly define these tactics. Examples can be:

  • More posts (paid and non-paid) on specific social apps (i.e. Facebook, Twitter, Reddit, etc.)
  • More engagement with the public on social media sites
  • Creation of branded ads 

Perhaps the most difficult task in this process is that of defining the appropriate Key Performance Indicators to measure how these tactics measure up. Some KPI in support of the above examples might be:

  • Increase in branded organic search traffic
  • Overall increase in organic search traffic
  • Increased activity/engagement on corporate social media accounts including click-throughs on posts
  • Increase click-through rates on branded ads
  • Increase in online sales by specific channels (organic search, branded campaigns, social accounts, etc.)

When defining your KPI keep in mind the following four factors that make up a useful KPI:

  1. Must utilize obtainable data
  2. Must relate directly to the marketing objective
  3. Should not be an absolute value but a ratio or comparison. For example, a KPI for improved customer engagement might be to increase average session duration. Or, measure average time on site comparing period one to period two. Or, a KPI for measuring effective campaigns on the “average number of orders per 100 sessions” by campaign
  4. Must be easily reportable and understandable by the target audience.

With the KPIs in place, ensure your analytics account is configured correctly. Ensure that you can accurately – without too much effort – report on the identified KPIs. Have all the required channels been defined? Don’t just rely on default channels from your analytics tool. Make sure the marketing activities to support the marketing department’s mission statement are realistic and approved.

Marketing measurement plans are typically in a layout grid format. Do a quick search and you’ll discover many suggested layouts. My favorite is simple:

With a merged marketing measurement plan in place, the next task is to align your corporate analytics to capture appropriate data and making it reportable becomes an easier task, as well as getting buy-in from other departments.

Imagine if your marketing plan didn’t include a KPI on sales by channel? Could you get your IT group to make the necessary coding changes to push transaction values to your analytics tool? Frequently they simply default to saying: “Sales information is available from our e-commerce tool.” While true, you can extract sales data from a backend tool, in virtually all cases, you can’t attribute those sales to specific marketing efforts. It is only with an approved marketing plan in place can you apply leverage and get this data integrated with your analytics.

What about custom channels? Do you need to segregate paid social, from non-paid social (your team’s participating on social sites on your own posts), from public sharing of your content? Yes, these are three unique social channels that should be tracked and reported on, if your company is utilizing these channels as part of their marketing plan.

You can create custom analytic reports that demonstrate how effective various marketing efforts are in support of not only the marketing department’s mission statement but also the corporate mission statement. This allows you to easily evaluate and adjust with objectively to demonstrate just how successful these efforts are to the c-suite.

Remember that marketing mission statements are a living and breathing thing. The world of online marketing is constantly changing as are the tools that help execute marketing plans and those that measure results. Plan on reviewing the mission statement at a minimum annually and possibly quarterly or semi-annually if appropriate for the organization. Don’t forget to get analytics audited by an independent auditor on a regular basis to ensure the data being collected is as accurate as possible, and that all data that should be collected is reported.

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What are analytics experts looking to in 2020 with data and privacy?

Logan Gordon, Simo Ahava, Astrid Illum, Abby Matchett and Sayf Sharif share insights to help you gain executive buy-in about privacy policy issues this year.

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While researching the state of tracking and data privacy, I talked to many smart industry experts and asked several to share their advice for 2020. It’s one thing for me to offer their executive summary, it’s another to hear it directly from them.

Plus, these folks will be helpful as you look for executive buy-in. “But Simo Ahava and Abby Matchett said…”

What do the experts think?

This must begin with a huge thanks to the following smart folks who shared their time and talent with us as we, collectively, prepare for the upcoming year. One of the best things about web analytics and digital marketing communities is the perspective that we are all in it together. I would encourage you to follow these fearless leaders, contribute to the conversation with them, and don’t be afraid to reach out for guidance.

Logan Gordan

The changes aren’t over yet, and I would expect continual developments geared toward greater privacy and greater transparency for the foreseeable future.

My advice is to color inside the lines. Attempts to work around or even toe the line will find themselves having to reinvent their approach on a regular basis as new privacy protections take effect. Instead, privacy-first approaches will find themselves having to spend less effort to comply with the changing data landscape.

Simo Ahava

This is the time to build a solid and robust benchmark. Go through your data from the past two years and try to identify the rate of cookie loss. The longer the period of time you’re investigating the higher the cookie loss.

Similarly, if you’re not already doing so, implement an ad block detection system. The best way to do this is to run some client-side JavaScript that uses a namespace of a known tracker — name it e.g. “ads.js” — and then send hits to some custom data store you own (so not Google Analytics) if that file is blocked by the browser.

Then, segment your data by browser. Check especially the usage statistics for Firefox and Safari, as they are the most prominent tracking prevention browsers out there. Note that this isn’t an exact science. Especially Chromium-based browsers (Chrome, Edge, Brave) might make it difficult to distinguish one browser from the other.

Once you have a benchmark, you know the scope of the problem. You can apply these numbers to your analyses by introducing margins of error based on the cookie loss statistics and the amount of ad blocking in use. For example, if your data shows that 20% of all visitors to your site block Google Analytics, you can be less worried about the 10% of the discrepancy between transactions collected by GA vs. your backend.

Astrid Illum

I believe that the current quickening pace towards restrictions on storing and using data will continue – involving both tech providers and the judiciary. But local rulings will provide interpretations on application to specific cases pointing in different directions since there is a lack of understanding of the basic issues at stake in the technical underpinnings of modern websites. Rulings in some countries will point in one direction, and in another direction in another country. This will make the situation a difficult one to operate in for most companies.

While we are waiting for the ramifications of existing laws to unfold and while a deeper understanding of the basic issues at stake is not yet widely held by the people applying said laws – marketers have to adopt a dual strategy: First off keep to the strictest interpretation of the laws to mitigate risk and secondly work to create a language around use of data that showcases the major part of why sharing data is important: To improve our digital products. Current language lumps together all kinds of data collection in one big suspect pot – in large part due to specific types of tools, practices and methods that are unduly invasive or boundless. Marketers and their technical colleagues in analytics should work together to rescue all the valiant uses of data our modern world is built on.

Abby Matchett

I think 2020 is going to be the year of evaluation. Marketing strategies, data collection strategies, and platforming strategies are all going to be called into question as regulations tighten and browsers participate more actively in privacy regulation.

For marketers dealing with data loss and other privacy concerns, this change is an opportunity to re-evaluate their initiatives. This is a time to take stock of their programs, and identify their key objectives – ensuring that their marketing initiatives are aligning with the overall business objectives. Marketers will need to adapt to the changing environment, which really will be the new norm!

Sayf Sharif

You are not an attorney so do not feel like you need to tell your bosses or clients what to do. Give them the breadth of options and the strengths and weaknesses of the approaches to how they deal with privacy, GDPR, web tracking implications, etc. Stay on top of what options there are, and how those options impact negatively or positively your ability to provide an ROI on analytics work. Offer to speak with their attorneys and provide them technical advice/guidance on what you can do, and how you can do it, but ultimately let the attorneys make the decisions on how they want to proceed.

As an aside, I see many consultants making recommendations of what to do, what not to do at conferences for instance, and at the end of the day a consultant should not be making a specific recommendation here, only providing options and advice on impact for their clients, rather than legal advice as in “this is what you need to do” because that liability for the decision lies at the feet of the consultant. It’s not our responsibility to determine what moral/ethical/legal direction their company can go, we should focus on what we can technically do, what the new limitations of browsers are, and then provide those options to our clients to make the decisions themselves, while also being aware of what the laws are, and ultimately doing our best to not break any laws knowingly ourselves even at the direction of our clients.

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See how agencies are putting data-driven marketing to work

Companies looking to remain competitive must now find ways to address consumers as unique individuals with highly specific, personal preferences.

The post See how agencies are putting data-driven marketing to work appeared first on Marketing Land.

By gathering rich, relevant data on consumer behavior and demographics, businesses can target their leads and customers on a far more personal level, optimizing their engagement rates while ensuring a positive brand experience.

But delivering on this data-driven expectation can present a number of challenges – particularly for digital agencies, whose clients are throwing unprecedented amounts of data in their direction.

In an effort to find out how agencies are overcoming some of these obstacles, SharpSpring partnered with Ascend2 to field the Data-Driven Marketing Trends Survey. This paper draws on those results to offer an in-depth view of the challenges involved in successful data-driven marketing as well as the many ways in which agencies are helping their clients stay ahead of the curve.

Visit Digital Marketing Depot to download Data-Driven Marketing: Let Your Data Take the Wheel.”



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Beyond the cookie: What’s next for attribution?

Identity resolution and more holistic approaches to measurement are the way forward, according to 11 experts.

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Now that third party cookies are on death watch, there are many questions arising about post-cookiepocalpyse marketing. Among them, what happens to attribution and what current or future methodologies will take their place?

To better understand the challenge of attribution going forward, we asked a range of marketing and martech executives to comment on replacement solutions and alternatives. Their reactions and responses cluster around three big themes: the importance of first-party data and customer engagement, identity resolution as a successor to cookies and developing a more sophisticated, holistic approach to measurement.

Nancy Smith, President and CEO, Analytic Partners

Considering the impending changes from Google, we believe it’s crucial for all brands to choose an approach to measurement that will allow them to gain the most accurate results when dealing with data loss. It’s vital to continuously experiment with, test and validate measurement strategies while incorporating an adaptive methodology. This is at the core of Mix Modeling and, when combined with continuous assessment of data quality, is key to ensuring robust results.

First-party data is going to continue to grow in importance and what is currently known as multi-touch attribution will morph into blended or more siloed solutions, used in a more limited way to better understand touchpoints. Analytic Partners has already been adapting and leveraging touchpoint analytics to glean tactical user-level insights.

Kristina Podnar, Digital Policy Consultant & Author

In the short term, we will see marketers grasping at the basic and mostly ineffective practice of last-click attribution, and an uptick in federated login systems (already in play in the EU). Longer term, marketers will have to look to mapping audience segments on the open marketplace (an industry standard and buy-in will be prerequisites), contextual targeting and federated learning. In the absence of conversion tracking, marketers can and should look to a unified ID solution, which opens up new opportunities beyond digital and addresses user touchpoints across all channels.

Jane Ostler, Global Head of Media, Insights Division, Kantar

Although cookies have started to crumble, they will not disappear completely for some time. In this new “mixed economy,” marketers will need to find new and creative ways to assess the impact of digital campaigns in a privacy-compliant way. As 2020 progresses, we may see some publishers using alternative measurement solutions based around deterministic IDs and panels, and we predict more direct integrations between publishers and measurement partners to enable the transfer of anonymized data. Other advertisers, publishers and agencies will turn to lab-based approaches to understand the effectiveness of digital media.

What is certain is that campaign measurement will become ever more complex. Marketers will need to future-proof their measurement frameworks and reduce their reliance on cookies for tracking. And many will turn to third-party data and analytics, which is the most trusted in the industry, to maintain accurate campaign measurement in the evolving media landscape.

Scott McDonald, CEO, Advertising Research Foundation

Even before cookies were slated for extinction, attribution always had its limitations. For the most part, it was mostly about digital – so it left out many important parts of the marketing mix. Over time, this encouraged marketers to over-value (easy to measure) short-term activation at the expense of (harder to measure) long-term brand building. A lot of evidence shows this was short-sighted and led many brands to lose market share, differentiation and pricing power. And even within the realm of activation, it proved hard to assign credit properly in complex environments without at least some experimental design component.

The loss of cookies is likely to make it harder still to sustain credible systems for linking ad exposures to ad outcomes across the media landscape – at least outside of the walled gardens. In the immediate future, I would expect marketers to pursue attribution analytics increasingly within walled gardens rather than across them. I would expect increasing numbers of media companies to attempt to build their own walled gardens by encouraging or requiring unified sign-in (policies that are very congenial to subscription services and to dual revenue-stream business models). And though I also expect that a number of players will attempt to resurrect cookies through other types of IDs linking websites, devices, and platforms, these will continue to run against the headwinds of public and policy pressures for data privacy.

Ian Trider, Director RTB Platform Operations, Centro

Despite removing third-party cookies, none of the major web browsers are trying to take away a website’s ability to track its own users. Marketers can expect continued click-through conversion to some extent indefinitely. However, they may have to rely more on their own website analytics for data instead of third parties.

Beyond that, marketers can apply the same measurement techniques used in the offline world to the measurement of their online campaigns. Marketers can use geo-based or time-based testing to determine the broader impacts of their campaigns beyond what can be measured directly. These approaches to measurement can also help estimate causal impact, as opposed to measuring only correlation, which is typical for online advertising measurement today.

Mike Herrick, SVP of Technology, Airship

The impact of data privacy regulations have consolidated power into the hands of platforms, who, to attain compliance, have nixed third-party data and measurement, instituting end-to-end reliance for marketers. Now cookies are crumbling, but the milk has already been spilled. To move forward, advertisers and marketers must shed a campaign-centric mentality and find ways to invite consumers into direct relationships, where resulting data is their own. Expect to see more ads prompting consumers to install and share mobile wallet coupons, opt in to SMS shortcodes, or engage in meaningful ways on brand-owned properties. Necessity may herald a renaissance, where brand marketers shift focus from interruptive tactics ported to the mobile era, to more authentic, contextual interactions that allow them to be there in consumers’ moments in helpful and handy ways.

Brian Czarny, CMO, Factual

Over time, we expect to see more brands look to mobile device IDs as a means to craft a more complete picture of their customers and measure the results of digital campaigns. Brands are already using location data-driven products to better understand their audiences, personalize the messages delivered to them based on their interests, and measure in-store visitation results, and we expect to see more marketers turn to location as part of a holistic strategy.

Kyle Henderick, Senior Director of Client Services, Yes Marketing 

Major browsers are building, or have already built, anonymized ways for digital ad attribution to be captured via APIs. Building out a robust architecture to interact with each browser’s unique requirements will be a significant undertaking for marketers. Ultimately, all of this still points to a greater need [for] investment [in] identity resolution and building a better direct relationship with the customer to take advantage of first-party data and reporting.

The best way forward for marketers is to stop relying on the easy wins in digital. Marketers must create their own future by building relationships with customers so they are more willing to share their data and by investing in identifying customers across devices. 

Todd Parsons, Chief Product Officer, OpenX

With user privacy now top of mind and the clock winding down on the third-party cookies, attribution is going to become both more complicated and more expensive for marketers to measure. To reach the same levels of accuracy in attribution that we see today, without relying on third-party cookies, marketers will need the ability to stitch identity together across addressable channels using first-party data. On top of that, any new solutions will need to comply with standards for collecting and resolving first-part data in our emerging opt-in (not opt-out) consumer marketing economy.

This problem isn’t new, however. Our ability to assign precise value to marketing channels that address the same person or household — everything from direct mail to cookie-targeted display — has always been difficult. And, it’s been harder in places where addressability is nearly nonexistent, like CPG products being sold to customers of Walgreens, for instance. Now that cookies can no longer serve as a reliable identifier for marketers, our industry is finally being forced to create new, privacy-first ways of leveraging first-party data to plan, track and measure the performance of campaigns across channels.

Michael Schoen, SVP of Marketing Solutions, Neustar

Identity resolution – and, specifically, a provider’s approach to it – will determine the relative impact marketers will face in a world beyond the cookie. Leveraging offline identity (PII), which is rooted in more stable identifiers like name, address, and phone number — as well as direct integrations with platforms and publishers, inclusive of walled gardens — gives marketers a clear path forward to doing attribution in a post-cookie world. Effective and reliable attribution measurement has always required looking beyond the cookie to capture the whole customer journey. This is the only way to accurately quantify marketing’s incremental impact to power both tactical and strategic planning, and investment decisions.

Erik Archer Smith, VP of Marketing, Scale Venture Partners  

Third-party cookies are an “easy button” for retargeting across popular networks like Facebook, but they don’t provide insight across platforms (Facebook vs. Amazon, for example) or granular data on behavior (who, what, when, where, and why). Without that important context, a third-party cookie can only really tell you that a “visitor” came back, and, even then, usually can’t tell you who came back unless that person converts by filling out a form, making a purchase, etc. So the cookie changes might affect some marketing vanity metrics (e.g., retargeting CTR) and make certain multi-touch attribution models less accurate, but I don’t see it having an impact on the most important metric: sales conversions.

At a high level, focus on creating great experiences and people will still trade their data. People will still opt in for valuable tools or resources. Which is great news for everyone since the quality of marketing goes up across the board. From a technical standpoint, consider taking control of your own data and embrace first-party cookies; there are several data platforms today that let you do this. This allows you to do your own retargeting through DSPs and provide personalized audiences into platforms like Facebook that are based on your own actual product or website activity. Even better, these technologies can let you resolve identity across different media “walled gardens” so you can better understand the “who, what, when, where” and maybe even “why: of user behavior, which is where real attribution comes in.

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