Marketing Data Strategy Q&A – Why Most Marketers Get It Wrong

A strong marketing data strategy is the foundation of any digital media plan, but most marketers miss the mark when developing an effective one for their brand. In this episode of The Loop Marketing Podcast, you’ll hear from Coegi’s Executive Vice President of Operations and Analytics, Katie Kluba, and Director of Digital Operations, Julia Wold, as they discuss data strategy, and why most marketers get it wrong. 

You’ll learn:

  • How to apply upfront data-driven work to inform marketing strategies
  • The importance of data restraint and why “more” is not better and 
  • The keys to giving the client the data story they need to hear

The following is an edited transcript of the podcast. Click here to listen to the full episode on your favorite streaming platform.

Q: What are some things that marketers should be doing in the upfront work to make their marketing strategy have stronger buy-in and have a better opportunity to truly be successful?

Katie: Yes, for sure. This is such a good topic to be discussing. I want to start at the beginning. So, first and foremost, it is really important to find a true digital partner. A true digital partner is a partner who’s going to tell you what you need to hear, not what you want to hear, and then you need to find a digital partner that is also comprehensive.

What I mean by that is they need to have a proven track record of success, but they also need to possess the technical prowess, if you will, to support data collection methodologies and the media tracking requirements that will be needed to create data decisioning. So once you call Coegi – we do all of that – so once you find your digital partner, what marketers should do to set themselves up for success is first and foremost, be crystal clear on what they are trying to accomplish with the campaign initiatives.

From there, you can start creating a measurement plan or framework, if you will, and the objective here is to tie the digital media performance back to what the marketer is trying to accomplish. I’m going to give you just a few tips about this measurement plan. Keep it simple and straightforward. Over complicating measurement is not a recipe for success. 

And then I would say, remember that the power of data decisioning is based on the reliability of the data. So junk in, junk out, meaning make it meaningful. 

And I would say lastly, and this is very important, you should separate the media optimizations or performance from any larger picture media effectiveness that you’re trying to measure. Measurement is not a one size fits all approach and campaign performance should be separated from the measuring the impact of the media to the brand objectives, business objectives that may be offline.

Q: Can you talk about what it takes to define campaign performance and where the waters sometimes get muddied for marketers in evaluating success?

Katie: Absolutely. You will see that a brand will be running an awareness campaign to drive awareness of potentially a new product or service that they have brought to market. And what ends up happening is the brand marketer and the digital partner will often come up with some media performance metric. So something that’s attainable in the platform, a click-through rate, an impression, a reach, a cost per click, and they will use that as a proxy, if you will, to the brand loyalty, the brand effectiveness. 

Was the media that was put in the market, did it hit the mark? Did the audience resonate with that brand? 

Do they remember or recall what the brand message was all about? 

Those two things are very different. So your media effectiveness should be a separate study. You can do branding surveys, there are many, many things that you can do, market research and other things. But you should not confuse performance based metrics to business initiatives. All can be measured, but that doesn’t necessarily mean it has to happen in one study.

Q: What are some ways that you have gone about figuring out the right methodology to evaluate campaign performance and business objectives for our brands, especially when there are so many measurement partners to consider?

Julia: Yeah, so going off of what Katie said, it’s first imperative to identify what you’re trying to achieve, identify the best methodology that’s going to get you there. So if you are trying to have an impact on perception, thinking through that, likely a survey based perception. A panel-based study is going to be your best bet in terms of proving out performance beyond media metrics with reach, completion rate, all that. Once you can identify the methodology that best fits your client’s end goals, then you can align to a provider that’s going to meet those goals. Like we said, there are so many providers out there that tout similar capabilities. So it’s really important to compare vendors and get a holistic view of the entire ecosystem because some vendors could be piggybacking off of other vendors’ data to improve and fuel their own methodology.

So getting a holistic view of the ecosystem is important. And then when you are pitching that solution to your client and you’ve come up with your provider that you’re looking to leverage, you have more of a talk track to show the work behind your recommendation. Also, forging meaningful relationships with these different partners, it really helps to solidify your approach and show the client that your recommendation carries some weight. 

And then lastly, like Katie said, too, success of a campaign is a multifactor approach. You’re looking at success from a media perspective, looking at its success from a business perspective, and then also making sure that you and the client really align all the way down from initial planning to a final readout that this was the plan that we chose all along the way. There are instances where sometimes clients can say, well, I wish we were measuring that. Unfortunately, switching measurement mid campaign is very difficult. And then you’re never going to be able to prove your success because you’re constantly chasing another lead and not focusing on what your core set was. So having a solidified approach early on is really going to help you prove out your value.

Q: What are some ways that marketers can avoid confirmation bias and use truly data-driven processes to ensure they’re collectively moving toward the brand’s goals?

Julia: So, there’s a couple things that you can do. It really starts with the strategy upfront. I know once media is up and running, you are going to get more of those media-based metrics in terms of performance. But you can also leverage tools along the way. So, perhaps prior to setting up your campaign, you launched a study to understand, “okay, this is who we think we’re going to target. What are they saying from a respondent perspective? And is the focus of how we approach targeting going to measure up?” There’s also ways that you could think to tag your media to get a readout from an audience perspective. So we have tools like The Trade Desk where we can put in an audience profile and then see other audiences that index highly with that. And so that can confirm or deny what you’re looking at. But like you said, bias exists everywhere and it can be really challenging. So as a marketer, you really need to put yourself in the shoes of all of these different individuals and of who you’re trying to reach and think holistically, but then also you as a test and learn approach to say, “okay, I thought it was going to go this way, maybe it didn’t.” And you know, being wrong isn’t bad either. it’s just now you understand what you’re not gonna do in the future and how to pivot.

Katie: Julia, you’re spot on. Test and learn is the way to go. Test and learn is the way to go, whether you’re pivoting mid-flight or you’re creating a test design for measurement pre-campaign launch. But what you want to be careful of is when you set up any test that you are not putting your thumb on the read or the test design to support what you think the test is going to read out. So I would say bias is really easy to avoid, but it is often not avoided. I have found that the most successful marketers are the ones that listen to the experts, which is their digital partner and put some faith that the digital partner has the expertise in the measurement of the ecosystem, that a well thought out test design will tease out without any bias, without any type one or type two errors, whether or not your hypothesis proves true or false.

Julia: That’s a really good point, Katie. I just want to interject too, when you do work with a smaller independent agency like Coegi, we are a little bit more agile and more flexible in the relationship standing and you know, we’re not held to the silos of some of the holding companies that they put in place just for their job function. So what we’re really looking to do is to give you the best approach rather than what’s the easiest approach or what’s the approach that fits into the job function that I’m currently doing.

Q: A large number of marketers have that tendency to overcomplicate data, especially when we’re translating that into reporting for brands. What steps should we be taking as marketers to get the most out of our data and truly and intelligently inform marketing strategies in business goals at large?

Julia:  I feel like maybe a broken record at this point because I’m always like, “think about it up front, think about it upfront,” but it definitely needs to be thought of upfront. So the way that we approach setting up campaigns is really thinking through what is the end goal that you have in mind and then working backwards. So, establishing that groundwork. If you know, for instance, your client is really interested in understanding how different geographies within their media buy are performing, then let’s focus on that and really drive that in. And focus less on – who cares about device type, who cares about you know, time of day? Those are all important and those all happen in the background, but you don’t need to pull out every single insight in the hopes that something sticks.

So, really focus on the one element that you know your client bought into and that you know you can set up a campaign where it flows from the DSP or the platform directly into analytics and then ties to maybe your advanced measurement goals. Something that I was just working on for a client recently and we were really focused as an agency on the audience. It was for a client that sold a product and we thought that the audience was so important to say this audience engaged more than this audience, but at the end of the day, all the end client cared about was like, what product of my product lines should I promote more of? Where do I need the people in store to start pushing more of based on what the consumer demand is? So if we had maybe just listened a little bit more to our client we could have helped to really craft our reporting insights to be based on what product is really driving and all of that audience stuff that we find so important is still important. But we’re just trying to complement what they’re looking for versus trying to recreate the wheel.

Katie: We, as marketers, have a lot of information about advanced measurement that includes bridging the gap between the online and offline point of sale world. There are measurement providers out there that are very powerful. The point Julia was making about listening to our clients is critical because you might have someone saying “well, what about that clickthrough rate? What about that time on site? Or what about the cost per acquisition in this particular case?” It may have been some sort of online event, right? So all of that is interesting when you’re trying to maximize and squeeze every penny out of your online marketing dollar to get your brand in front of the relevant audience, consumer base that you’re trying to reach. Very important. But at the end of the day, what the client wanted to understand is, of all of the products that I have, what are the ones that the offline world should be pushing, right? The display should be set up such that, that’s the product that is eye level. So that is a perfect example of how bias comes into a measurement framework. What you’re saying versus what you really need are two different things. So it is important to continue to have conversations, to tease all these things out. Open mind, come to it with an open mind, blank slate.

Julia: Yeah, and I don’t think many clients are going to be super black and white on what they’re looking for. So it is an evolution of like really listening intently to off the cuff comments that they might make when you’re doing reporting. And then that’s where you have the opportunity to pivot. And maybe it’s not even a pivot of your strategy, it’s just a pivot of the way that you’re going to read out your strategy and speak to the client.

Q: What are some steps that we should be taking to ensure that the data that we are translating back to the client is truly speaking the same language and helping the brand be empowered, not just causing greater confusion or going down a rabbit hole leading to nowhere?

Katie: You recall I said at the top of this podcast to keep it simple, be clear and keep it simple. That resonates with measurement, period. If you do nothing else but do that, you’ll be miles ahead of what is too often just checking the box scenario. In our industry, if you were good at Excel, you became an analyst. But that doesn’t mean that you can tie business objectives back to media optimizations and media buying. So I would say first and foremost, do not overcomplicate your performance story.

Remember, more is not better necessarily, it’s just more. 

Secondly, observations are observations. You should be able to read, interpret, the data in front of you. Then, based off of what you’re looking at, your recommendation should be what you’re going to do or what you have done, so the result of what you have done or what you’re going to do based on what you’re looking at to get the media buy – the performance – which is different than the media impact, but to get the performance to where it needs to be. So we’re effective and efficient driving every penny, nickel, squeezing that out, driving the KPI for the client. It sounds redundant, it’s literally foundational to any measurement strategies. Do not overcomplicate it, keep it simple. That’s what I would say.

Q: What predictions do you all have in terms of what changes are going to be needed to keep measurement as informative as it is today, going into two or three or even 10 years down the line?

Katie: So first of all, everyone’s in the same boat. No one should panic. We’re all in the same boat, so we will not really know the true impact until the deprecation of the cookie occurs. We can project what we think is going to happen, and what we think is going to happen is that there’s going to be less focused targeting. Right now the industry is such, we collect so much information about users who engage with the media and also engage on brands’ websites that we can create segments of how they consume that media and actions they take thereof. So there will be less ability to create those segments. However, as I said, we’re all in the same boat, so there will not be really one advantage for one marketer versus another. Outside of a few things that are going on that I think marketers should be doing immediately – and if they’re not, they need to start – there are a few identity grids that have been developed when the news came out that the cookie was going to be deleted. These identity grids are going to allow the marketers to continue to target and create segments of audiences just as they do today. So, they need to speak with their digital partner and make sure they’re opting into these identity grids.

Julia: Yeah, and I’ll just kind of piggyback off of that. Something that we’ve done as an operations department here at Coegi to help prepare these marketers for this is just to get a better understanding of the fact there are a ton of data providers out there in the ecosystem, and while we’re not talking about the marketer’s first party data they do likely want to tap into somebody else’s third party data because as we know, first-party data and zero-party data is finite. In order to scale you’re going to need other opportunities, so it’s really important for us to know who our data providers are, what their collection methodology is, how they are planning for a cookie list future, how they’re already overcoming the privacy laws of GDPR, CCPA the Pepitas of Canada.

And just getting that foundational knowledge – I feel like foundation is another key word for this podcast – but getting that knowledge of how things are being collected there. It’ll give you a lot more peace of mind in understanding that you’re reaching people with ethical third-party data sources. Additionally from our approach too is to think about using your first-party data as it is now, you have the opportunity still with cookies to layer on and see where your audience indexes with other third-party sources so you can start to create a persona of who your current customer is and make sure that you just start to collect that stuff now so that it’s useful for the future. So for instance, say you don’t have a first-party list, but now you know, I can create a persona based on people and maybe they like to engage on this type of content, including a contextual element to your next media buy is going to be a great way to test out how it performs. That way you can still have what a cookie is able to give you today and you have that comparative tool.

Additionally, a lot of what I’m seeing in the marketplace and where I’m seeing a lot of people are leaning, are toward more of those PMP and even programmatic guaranteed buys. And I almost feel like we as marketers are slightly going back to what things looked like 10 years ago from a buying standpoint because we know that we can trust that certain publisher and it’s all about kind of that relationship building again, whereas we got a little loosey goosey taking whatever was available to us. Now we’re just becoming more intentional with the way that we’re building our campaigns and really focusing on quality.

I will say that ever since I’ve been here at Coegi for six years, quality has always been something that we’ve focused on and it’s just being more and more elevated as we continue to evolve. So I feel like our company has been really set up well for navigating this new landscape.

Katie: Yeah, that’s a really good point, Julia. Extending your first-party and zero-party audiences leveraging third-party data providers is going to be key. The direct deals are going to be key. Contextually relevant buys are going to be key. I do want to add that we’re really talking more about the programmatic ecosystem and the impact is going to be felt primarily there. We still have a lot of user profile information within the social sphere, so your addressability there will still be pretty focused.

Q: Lastly, what do you feel is the most common mistake that marketers are making today in their use of data and how it is informing marketing strategies?

Katie: Well, I’m going to be honest, number one. The most common mistake I see is marketers just don’t use data. So there’s a couple reasons why they don’t, even if they think they’re using data, they’re probably not using it correctly. I’m not trying to upset my brand friends, but interpreting the data correctly is important to make data decisions, right? Data informed decisions. And I would say the other area or reason why most marketers do not use data, is they don’t know where to get the data. And so that’s where I would say the two areas are probably the biggest components of why we have bias. Can we go back to that question? Why do we have bias in our measurement and which leaks into our strategy? The digital ecosystem is so measurable and if there is some trust given to the integrity of the data, which has to be pur purposeful and the measurement plan that is tied to the strategy, if there’s some trust given there in connectivity, what you’re going to get out of that is a really solid circular feedback loop of data decisioning. And I think your campaigns are going to do very, very well for your brand.

– – – –

To improve your measurement and data strategies, check out Coegi’s 5 Step Guide to Successful Marketing Measurement.

Google Analytics 4: The Transition is Here

Get ready for the imminent sunset of Universal Analytics. Starting July 1st, Universal Analytics will no longer process data. To ensure a smooth transition, it’s crucial to deploy and test Google Analytics 4 tags in place of your existing website analytics infrastructure. We understand that this task can be overwhelming, especially if you’re unsure where to begin. That’s why we’ve created a comprehensive checklist to guide you through the entire process:

Learn About GA4

Start by familiarizing yourself with Google Analytics 4. While it shares some similarities with Universal Analytics, there are significant differences in the user interface, event tracking, and conversions, as well as the modification or removal of certain metrics.

Google Resources: 

Introducing GA4

UA vs GA4

Review Your Tag Strategy

While it is not necessary to make fundamental changes to what you are tracking on your website, it is a good time to consider if what you are tracking in the Universal Analytics setup allows you to make informed business decisions. If not, update your strategy to track important events on your website, with a focus on collecting lead data for future re-messaging opportunities.

Migrate Existing Tags

Once you’ve updated your tag strategy, it’s time to delve into Google Tag Manager (GTM). Google Analytics 4 requires a configuration tag that should fire across all your pages. If Google hasn’t already guided you through creating a new configuration tag, simply create a new tag that fires on all pages. This configuration tag will typically replace your Universal Analytics Page View tag.

Migrating your existing Universal Analytics events to Google Analytics 4 is relatively straightforward. Inside Google Tag Manager, you can reuse existing triggers, but you’ll need to update your tag type to a GA4 Event tag.

The key difference between events in Universal Analytics and Google Analytics 4 is the structure. In Universal Analytics all events had a Category, Action, Label, and Value. In Google Analytics 4, simply name your events and pass additional event parameters as key-value pairs. Your ‘Event Name’ should be descriptive enough to identify an event – leave the nuances to event parameters. For instance, if you had a tag in Universal Analytics that tracked blog post views, it would have likely looked like the following: 

  • Event Action: Blog Event
  • Event Category: Blog Post View
  • Event Label: dynamically populated the title of the blog post 

In Google Analytics 4 this could be simplified to the event name “blog_post_view” with an event parameter of name with a value equal to the title of the blog post.

Compare Data

After your new tags are deployed, you should compare the Google Analytics 4 data to Universal Analytics data or another reference data source. There can be issues during your new tag deployment or overlooked settings that may need to be re-examined. For example, if you were using session de-duplication of goals in Universal Analytics, you may need to instead adjust your Google Analytics 4 firing rules in GTM to prevent duplicates as this feature of Universal Analytics has sunset. Being aware of the nuances in your data is vital.

Educate Teams

Now that your new Google Analytics 4 property is up and running, it’s essential to educate key stakeholders and teams within your organization about the data available in GA4. By providing them with the necessary knowledge and insights, you empower them to make informed decisions and maximize the benefits of the new analytics platform. Here are some steps you can take to educate your teams:

Organize Training Sessions:

Conduct training sessions or workshops to introduce your teams to Google Analytics 4. Cover the basics of the platform, including navigation, key features, and reporting capabilities. Provide hands-on demonstrations to help them understand how to access and interpret the data.

Highlight Differences:

Emphasize the key differences between Universal Analytics and Google Analytics 4. Explain the changes in terminology, event tracking, and reporting structure. Help your teams grasp the nuances and limitations of GA4 compared to the previous version, ensuring they are aware of any adjustments needed in their analysis processes.

Showcase New Features:

Highlight the enhanced features and functionalities of Google Analytics 4 that can benefit different teams within your organization. For example, show marketers how to leverage the advanced audience segmentation and user journey analysis capabilities. Demonstrate to product teams how GA4 can provide insights into user behavior and help optimize the user experience. Tailor the training to the specific needs and interests of each team.

The Drum – Your Data Strategy Can be a Community-Building Strategy

How do the world’s most beloved brands like Lego and Trader Joe’s earn lasting spots in the hearts of consumers? They use consumer data the right way, creating meaningful experiences that build relationships between individuals and the brand. Not to simply create transactions.

You can do the same (even without the theme parks or Hawaiian shirts).

Read more on The Drum:

Advanced Marketing Measurement and Modeling 101

A strong marketing measurement strategy is the cornerstone of media planning, answering the complex question: how is advertising supporting business success? 

A unified measurement framework guides brands toward achieving full-funnel goals. Sometimes, this is as simple as defining media KPIs and optimization points – think conversions, cost per action, reach and frequency, cost per unique reach, and so on.

But, oftentimes, media metrics alone cannot answer brands’ most critical questions. In these instances, advanced measurement studies and modeling strategies are critical tools to inform smart decision-making. 

Upgrading Marketing Data Insights With Advanced Measurement

Advanced measurement strategies don’t just track business success—they explain it. They answer the why before the what or how, providing a source of truth across multiple business disciplines and streamlining communication between stakeholders. 

What is advanced marketing measurement?

Advanced measurement refers to methods used to answer advertising questions that are difficult to address by standard media metrics alone. They’re important for understanding campaign performance in a more meaningful way than cost and reach. 

Examples of such questions include:

  • Did my brand have an increase in unaided brand awareness?
  • Did my retail locations gain incremental visits as a result of my marketing campaign?
  • Has my brand’s market share increased as a result of the media running?

In these situations, reporting back on simple media metrics won’t offer the depth of business intel you need. As Coegi’s Vice President of Marketing and Innovation, Ryan Green, quotes in Marketing Profs:

“Advanced measurement strategies mute the irrelevant metrics and form connective tissue between the rest so that marketers have a deeper understanding of how various campaign factors can help (or hurt) sales.” 

Some metrics simply matter more than others. When you shift toward performance metrics directly relating to your business goals, you’ll gain a clearer line of sight into what is and is not working.

5 Advanced Measurement and Modeling Tactics You Need to Know

Once you identify a need for advanced measurement, it’s time to determine which approach(es) will help fill that knowledge gap. Here are five of the most common advanced measurement methods we use at Coegi: 

#1 Brand Lift Study

What are brand lift studies?

Brand lift studies provide mid- or post-campaign consumer readouts to measure brand impact. Set up prior to campaign launch, these studies are ideal for awareness or consideration campaigns looking to track incremental improvements in more elusive KPIs such as brand awareness, ad recall, brand favorability and purchase intent. 

Brand lift studies are typically conducted through control vs. exposed consumer surveys that ask questions such as: 

  • Have you seen an advertisement for {{insert brand here}} in the last 30 days? 
  • What’s your perception of {{insert brand here}}? 
  • Would you consider purchasing {{insert brand here}} next time you visit the supermarket?

Depending on the media mix, you can deploy single-channel measurement studies. You’ve likely been served a one question survey before a YouTube video or in your Facebook feed – that is an example of a single-channel brand lift study. Or, you can run cross-channel measurement studies in a demand-side platform environment using display, video, audio, native, and connected TV methods.

These insights are able to be segmented by parameters such as audience, geography, creative, and channel to isolate the top performing elements.

Why use brand lift studies?

Brand lift studies help bridge communication gaps and showcase how various advertising channels work together to meet the primary goal. They can be useful for brands in any industry, especially those lacking broad awareness in cluttered categories.

#2 Foot Traffic Lift Study

What are foot traffic lift studies?

Foot traffic lift studies measure brick and mortar visitation. They connect the dots between awareness and conversion by measuring the lift of in-store foot traffic due to ad exposure. These studies are typically conducted using mobile location data from in-app user opt-in as well as one-to-one impression pixels. Industries that most commonly benefit from foot traffic studies are retail, auto, travel, QSR and CPG.

Why use foot traffic lift studies?

They serve as a valuable sales proxy for brands with brick and mortar locations or whose products are most commonly purchased at physical retail stores. Understanding visitation lift also helps understand consumer consideration, especially for large-scale items like automobiles that often have a longer purchase cycle. 

For industries and businesses without branded physical store fronts, creative assets should include retailer logos to direct consumers to distributors that are most convenient to consumers’ locations. 

#3 Sales Lift Study

What are sales lift studies?

Sales lift studies are used to measure SKU-level data and tie it back to advertising. They match in-store transactions to digital campaigns including digital, video, native, audio, social, and CTV ads. Oftentimes, these studies use first-party shopper data from retail loyalty programs to tie advertising exposure to in-store purchase behavior. Common sources for this information are retail media networks, IRi, and Catalina.

Why use sales lift studies?

These study results show the increase of in-store purchases due to omnichannel advertising efforts. Sales lift is ideal for CPG brands when incremental product sales and understanding of bottomline company growth is the most critical indicator of success. Attribution of sales is increasingly complicated as products are available in multiple online and offline marketplaces, and advertising is similarly fragmented. 

Sales lift helps zoom in on the most important metric, sales volume, without getting lost in the weeds. To see how Coegi used sales lift to prove ROI for a cookie brand, view our case study here

#4 – Media Mix Modeling

What is media mix modeling?

Media mix modeling (MMM) is an analysis method that helps define optimal media channel budget allocation using historical performance data. Through multi-linear regression models, this method assigns value to each marketing touchpoint, so marketers can determine how each variable impacted key outcomes. It requires at least two years of sales data and media metrics to make accurate predictions and performance optimizations.

Marketers like specifics, as they help with targeting and attribution, but MMM’s purpose is to help marketers understand how various marketing activities drive the business metrics of a product or service.” – Hugo Loriat 

Why use media mix modeling?

Numbers don’t lie, but they don’t tell the whole story either. It is crucial to fully understand the context of the data you’re analyzing. What factors may have contributed to performance fluctuations? Creative? Messaging? Audience strategy? Seasonality? 

Media mix models help incorporate all of these variables to determine what story the data is telling. By blending multiple factors, rather than just a singular KPI, you can see a bird’s-eye view of how all the pieces are working together to impact long-term strategy and performance. 

Learn more on how to use MMM to boost your bottom line in this video: 

#5 – Performance Scoring Model

What is a performance scoring model?

A performance scoring model is a unified marketing measurement model that uses multiple, weighted data sources based on level of significance to define your media’s impact on business goals. It incorporates both media and non-media data to enable smart business decisions and more accurate predictions. 

In the end, you come out with a performance score that summarizes how your brand is doing in relation to business goals. Here’s a simplified graphic example of what a performance scoring model can look like: 

performance scoring model
Performance Scoring Model

Why use a performance scoring model?

No single marketing metric or strategy can equate to business success. Brands need a custom, yet flexible, solution to accurately track and measure marketing results on an ongoing basis. The performance scoring model is a great option for those looking for that flexibility and customization. It is an all-encompassing business dashboard you can use to unify data analytics, clearly qualify marketing’s impact and inform smart decision-making. 

Potential Barriers to Entry with Advanced Marketing Measurement

It’s important to weigh the pros and cons before implementing any of these tactics. Consider and discuss these three primary challenges before selecting your advanced measurement plan: 

#1 – Cost

  • For lift studies, each measurement partner has a unique pricing structure. At times, these can be cost prohibitive for brands just getting started. Consider the available budget and expected outcomes beforehand. 
  • For advanced modeling, you will likely need to outsource a digital agency, such as Coegi, or a data technology partner to implement these analyses – unless you have an in-house expert with statistics training. 

#2 – Data Availability

  • For lift studies, some providers require impression volume or retail location minimums to ensure feasibility and statistical significance. It’s also important to identify which channels you want to analyze. Walled gardens (ie. Amazon, Meta) will require different solutions than other programmatic platforms that allow for cross-channel measurement.
  • For MMM, you need to already have two or more years of quality marketing and sales data to input. Similarly, the performance scoring model is more flexible, but will be most effective if you have strong consumer data to input from the start. 

#3 – Time

  • Lift studies tend to take several weeks to launch and gather statistically significant data. It’s important to plan early and set expectations. 

Launching Your Brand’s Advanced Marketing Measurement Plan

Once you’ve identified a need for advanced measurement or modeling, it is important to ensure the tactics you chose align with the desired business outcomes. 

To help you get started, we took our entire approach to marketing measurement and boiled it down to five simple steps. View our 5 Step Guide to Successful Marketing Measurement here

Partner With Coegi for Expert Marketing Measurement Strategies

Advanced measurement and modeling will become increasingly important for quantifying marketing success, especially in the cookieless future. But this can be a daunting task for any marketer.

If you are unsure what measurement strategy is best for your brand goals, contact Coegi for a discovery call to get started

Driving 4X ROAS for CPG Wine Client on Instacart


Bread & Butter Wines uses the online grocery delivery app, Instacart, as a central tactic in their e-commerce strategy. Because of this, our account team was eager to try a new optimized bidding tool offered by the platform. Our goals were threefold: to keep the client’s strategy in stride with a rapidly evolving platform, test AI’s ability to directly impact ROI, and reduce operational lift.



YoY Sales

Cost per Conversion


Coegi runs an evergreen campaign on Instacart for Bread & Butter, which has consistently delivered at or above a 2x ROAS benchmark. However, achieving these results required time-consuming manual optimizations based on cost-per-click metrics. While this approach was effective in driving results, we sought a more efficient and profitable bidding process.


Instacart’s new optimized bidding tool uses AI to automate bidding and maximize ad spend. Staying up-to-date with innovative platform updates is a priority for Coegi, and we knew this tool had the potential to significantly increase campaign performance and efficiency. Within a month of the Instacart release, our team implemented the new capability.

After a brief learning period, the AI algorithm began pushing ROAS into the 3x range. As the campaign progressed, this figure steadily increased to an average of over 4x, with peaks for individual products hitting up to 10x ROAS. As a result, the campaign generated a 32% YoY increase in Instacart revenue and decreased the average cost-per-conversion by 53%. 

In the age of Web3 and AI advances, a seemingly small platform update can have a significant impact on your results. Our team’s enthusiasm for testing and learning allowed this campaign to double its impact on Bread & Butter Wines’ ROI. 

Increasing Brand Lift and Growing Market Share for BODYARMOR


BODYARMOR was a new entrant into a well-defined CPG category: sports energy drinks.  

With a product containing less than half the sugar in Gatorade, but only 2% of overall category market share, BODYARMOR was looking to disrupt the paradigm. 



Lift in Brand Awareness

Video Views in 2 Months

Lift in Purchase Intent


The media challenge was to break through the clutter in a crowded space and ensure BODYARMOR’s message of superior hydration was reaching the most relevant audience – ultimately increasing awareness and market share.

Logistically, they secured significant investment with grocery store and gas station distribution networks. The brand also had endorsements across all major sports leagues, plus significant involvement from investor, Kobe Bryant. But, to increase market share and sales, they needed to establish brand awareness with the right customers.


We used industry research to select the optimal digital media channels, as well as our own planning and channel mix software, to develop the optimal “go-to-market” plan. Using BODYARMOR’s first-party data collected from web engagements and promotional eblast sign-ups, we created targeted media plans for key niche audiences and engaged media partners to further invest in BODYARMOR’s success and growth.

Our team performed look-alike modeling and statistical analysis to create microtargeted audiences, including: Blue Collar Workers, Grocery Gatekeepers, Veterans, Teenage Athletes, and Health-Focused Adults.

Each audience had its own media plan and messaging strategy, for example:

  • The Blue Collar Worker audience focused on Midwestern and Southern states, Facebook and YouTube channels – using creative highlighting their partnership with NASCAR’s Ryan Blaney and UFC promotions. 
  • The Teenage Athlete audience focused on Instagram and Snapchat and leveraged endorsements from the NBA’s James Harden and NFL’s Richard Sherman.

To maximize campaign efficacy, we enabled multiple layers of targeting. This included layering our media with the national distribution footprint, to ensure the campaign was reaching the right people at the right time in the right place. We customized sequential messaging based on customer engagement level and continually made real-time adjustments to optimize performance.

We also engaged our Google reps to ensure alignment and efficiency across the board. These partnerships allowed access to Google Beta products, as well as brand lift and purchase intent studies to evaluate campaign success.

By all measures, this campaign delivered superior performance. Aggressive optimization throughout the campaign resulted in 55% over-delivery and engagement rates much higher than initially outlined. Through Google Site Link extensions, we were able to drive and measure a significant amount of in-store sales volume. 

In year one, the audience-first strategy produced strong brand recall lift amongst four of the five target groups. In year two, the strategy shifted to only include the top four performing audience groups.

  • The Blue Collar audience saw 22% brand lift and $840k in attributable sales.
  • The Grocery Gatekeeper audience saw 14% brand lift and $376k in attributable sales.
  • The Veteran audience saw 12% brand lift and $411k in attributable sales.
  • The Teenage Athlete audience saw 14% brand lift, $154k in attributable sales.
  • The Health Conscious Adult audience saw 2% brand lift, $214k in attributable sales.

More importantly, sales increased nearly 300% YoY, with 6% category market share; leading to the brand being acquired by Coca-Cola.

The Countdown to Zero-Party Data

The Countdown to Zero-Party Data

If you’ve been paying attention to marketing news lately, you have no doubt seen the terms first-party, second-party, third-party, and zero-party data. These terms are critical in almost every targeting strategy conversation. 

With Google’s impending deprecation of third-party cookies, it is vital that you understand the differences between these data types. In this blog, you’ll learn how they can help or hurt your advertising strategies. Plus, we’ll outline how to collect and leverage each data source from third to zero-party data. 

Third-Party Data

What is Third-Party Data?

Third-party data is any information collected on consumers from an entity with no relationship to that consumer. In marketing, data aggregators commonly gather data from web browsers that are bundled and sold to advertisers. 

How to Collect Third-Party Data:

  • To collect third-party data, marketers purchase curated data packages from aggregators. This data is the primary target of data and privacy protection laws because it is usually collected and shared without the explicit consent of consumers. 

How to Use Third-Party Data:

  • How do you use this data? The short answer: due to changes in privacy laws/policies and the cookieless future, you should use third-party data sparingly. Additionally, this data collection can be inaccurate and lead to budget waste by serving ads to the wrong audiences.
  • Start shifting toward collecting more effective forms of consumer data, like first-, second-, and zero-party data, for your targeting needs.  

Second-Party Data

What is Second-Party Data?

Second-party data is consumer information collected directly by another organization that your brand has purchased or gained access to through partnerships. Unlike third-party data, the collecting organization has a direct relationship with the consumer. This leads to more accurate and actionable information. 

How to Collect Second-Party Data:

  • One of the more common forms of second-party data collection is through walled gardens, such as social media and retail media platforms. For example, social media account users on each platform are required to login prior to the use of the app. You may also gain access to this type of data through quality publisher partnerships. 
  • Like third-party data, you have to purchase or negotiate access to this data from the collection source. Coegi partners with providers like OwnerIQ, US Farm Data, and other reputable sources to ensure our clients have access to quality second-party data. 

How to Use Second-Party Data:

  • As mentioned above, you will likely use this kind of data while running ads on walled garden platforms or when activating direct partnerships with publishers. If you partner with a company to access this data, they will typically send anonymized email lists or require you to serve ads through them to gain access to their audiences. 
  • It is important to thoroughly vet any partnership in this space. Be sure you are in accordance with any privacy laws or policies put in place. 

First-Party Data

What is First-Party Data?

First-party data is information your brand collects directly from your audience. If you analyze it effectively, this will be one of the most important elements for digital advertising strategies in a cookieless future. 

How to Collect First-Party Data:

  • Place gated content on your website to collect emails and other information.
  • Generate email newsletter sign-ups in exchange for discount codes or special offers.
  • Store relevant information from customer purchases in your CRM platform for future segmentation and activation.

How to Use First-Party Data:

  • After collecting first-party data, you can use it to reach individuals who have already engaged in your brand through features like email-match targeting.
  • Develop modeled audiences to target people who have similar data points or behaviors to your existing customer base. Personalize advertising messages and other communications based on the most valuable and influential data points.

Zero-Party Data

What is Zero-Party Data? 

Coined by Forrester, zero-party data is collected when “a customer intentionally and proactively shares with a brand. It can include preference center data, purchase intentions, personal context, and how the individual wants the brand to recognize [them].” This data is technically a subcategory of first-party data. It is, however, worth giving this information its own terminology because it has the potential to go beyond first-party data snapshots and provide advanced profiles of your customer base. 

How to Collect Zero-Party Data:

  • Design and distribute short strategic surveys, quizzes, and polls for your audiences. 
  • Include interactive tools on your website that allow users to self-identify for a more personalized website experience.
  • Require free or subscription-based website account set-ups and logins to view the most valuable content to create a value exchange. 
  • Build product or service ratings into your website listings.

Tip: Motivate the customer by offering them something of value from your brand. For example, you could offer a discount or special access to an event in exchange for providing the data. 

How to Use Zero-Party Data:

  • Add zero-party data to your CRM and use it to curate customized communications and offers that build brand loyalty.
  • Act on user feedback to align your marketing strategy and customer touchpoints with the desires of your target audience.
  • Deliver custom suggestions to your users’ account home page based on information collected in their account set-up. 

Tip: Be conscientious about how often you are asking for this information and be sure to include variety between each ask. You don’t want to fatigue the customer and create a bad user experience. 

Bringing it All Together

The key distinction to make between each data type is the source.  As you move from third to zero-party data, you move closer to a more accurate understanding of your audience. These direct-from-the-source insights will help you make smarter strategy decisions and more effectively motivate your audience to convert. 

To learn more about how to use this data, read our Cookieless Targeting and Identity Solutions blog by Coegi’s Director of Innovation, Savannah Westbrock. 

Cookieless Attribution and Measurement Solutions

Cookieless Attribution and Measurement Solutions

Cookies have been the underpinning for most digital marketing performance measurement for over twenty years, which has allowed advertisers to measure post-click conversions and attribution for sales impact. As a result, channels like paid search and display retargeting typically stand out as ‘performance channels’. Simply put, cookie deprecation takes away the easy button of using off-the-self audiences and straightforward conversion tracking.  However, without third-party pixels, determining clear return on ad spend will become more challenging, especially for marketers who continue to rely on click-based attribution models.

Without cookies, it is imperative that you develop more meaningful ways of understanding how customers make decisions and how it impacts business results, a topic we recently covered on The Loop Marketing Podcast.

How to calculate marketing ROI in the cookieless future

In this new paradigm, marketers will need to rely more heavily on strategy to get the greatest and most accurate ROI

The ability to calculate marketing ROI starts with having a strong measurement strategy in place prior to campaign launch. Smart marketers know to look beyond online conversion data and search for correlations with business performance to determine true directional success. Advertising campaigns need to be set-up to achieve business goals rather than just vanity metrics. It’s important to know when to incorporate more robust analytical solutions to understand what’s impacting your bottom line. 

Cookieless measurement solutions

Some methods for measuring media campaigns in the cookieless future include: 

  • Media mix modeling (MMM): MMM works by isolating one variable at a time to see the impact of removing or adding a tactic. It allows deeper understanding of how omnichannel campaigns work together and incrementally impact key outcomes. 
  • Advanced measurement studies: Exposed vs. control consumer studies track brand lift, sales lift or foot traffic lift to provide greater insights into the real impact of advertising on difficult-to-measure business goals. 
  • Overlaying multiple data sources: Brands can match up Google Analytics conversion data, or sales data, with paid media data. While more time and knowledge intensive in terms of the analysis needed, this is effective to look beyond media data alone and instead looking holistically at the brand to understand marketing’s impact. 

Place less emphasis on media efficiency metrics and more emphasis on effectiveness. Look at correlations between business and media data to identify incremental conversions compared to your company baseline. 

To achieve this, marketers will need to identify leading indicators of success by channel and tactic and optimize towards those metrics.  

Will the cookieless future impact walled gardens?

Walled gardens, such as Facebook and Amazon, leverage their own first-party user data. As a result, cookie deprecation will affect them less in terms of targeting. 

Within platform confines, advertisers will still be able to track individual users, though the windows of attribution can vary. Due to this, walled gardens allow for brands to conduct some closed-loop measurement. That being said, there will be limitations on attribution, and less deterministic targeting as privacy laws continue to become stricter.

Walled garden pixels will have limited ability to pass back data to the platform once cookies are gone. We can expect front end marketing performance metrics to decline, even if backend business performance remains the same. Plan for shifts in attribution, using strategies like those laid out above, as we get closer to cookie deprecation.

Cookieless attribution tips

Begin testing and learning today to proactively understand what will and will not be effective in the cookieless future. 

  1. Begin benchmarking current performance ASAP: compare performance of cookie-based vs. cookieless tactics. Then, analyze backend data to determine the effect on business results and set expectations accordingly.
  2. Consolidate to fewer platforms, or find a way to ID map: Platforms are developing their own internal ID tracking frameworks. The more platforms you execute your media through, the more disparate measurement systems you have to consider. This will also minimize duplication across platforms. 

The deprecation of third-party cookies will undoubtedly impact the way marketers approach digital media. But a data-driven media plan tied into a holistic cookieless attribution and measurement solution will ensure your business continues to grow by reaching the audience in the right place at the right time.

3 Ways to Improve Marketing Campaigns Using AI

Are you using artificial intelligence for your marketing? 

If not, you’re likely spending unnecessary time and effort launching and optimizing advertising campaigns.

Which creative assets are best for this audience? How much should I bid for a particular ad placement? Who is my target audience? 

AI can help you answer all of these questions, minimizing guesswork and assumptions. 

How does AI boost marketing campaign performance?

To distill it down, AI uses algorithms to sort through data using a set of rules to complete automated tasks. To function optimally, the algorithm needs a goal to optimize towards. So it’s up to humans to tell the machine what that goal is and if the campaign is succeeding or not. These guardrails are necessary to ensure actions aren’t taken that create cost reductions, but are actually detrimental to marketing goals. 

With this strategic foundation in place, artificial intelligence can significantly improve campaign performance, save time, and increase efficiency.

Here are the top three ways you can use AI to improve marketing campaigns:

1) Campaign Optimization

The most prolific use for AI in marketing campaigns is for media buying automation and optimization. 

Automatic bidding allows media buyers to place appropriate bids for each ad placement in the open market. This ensures you are reaching your target spend levels and pacing evenly across campaign durations. 

Artificial intelligence tools can also optimize pre and post bid to improve campaign performance and learnings: 

  • Pre-Bid Optimization: Analyze site visitation patterns and social media following before a campaign launches for faster learnings and more accurate targeting.
  • Post-Bid Optimization: Pull insights from millions of data points to understand what worked and what didn’t. These learnings provide a roadmap for improving future campaigns and identify tweaks throughout the campaign. 

These automations allow you to focus on meaningful data and strategic thinking. Use them to repurpose your time to more strategic tasks while platforms manage the day to day operations. Look for anomalies and trends in the data, but let the AI do the heavy lifting. Then you can take a more inquisitive approach to determining the “why” behind the data points.

2) Audience Targeting 

The second way you can use AI to improve marketing campaigns is by expanding and refining audience targeting. Using AI, you are able to expand your first party and/or pixel data to find more people who look like your existing customers. Whether it be similar media consumption behaviors, demographics, or other behavioral attributes, this technology can find people most likely to convert. 

In a post-cookie world, AI-assisted targeting will be particularly important. Many brands will have less complete customer profiles due to less availability of consumer data. There will be an increasing need for AI to rapidly test various targeting tactics to find your best audiences in a way that’s both measurable and efficient. 

3) Creative Optimization

Finally, creative optimization is another impactful way to leverage artificial intelligence. Consumers are demanding more personalized, exciting moments from brands. To drive action, ads need to be hyper relevant to your niche audiences’ motivations, but also break through the clutter of other “personalized” ads. 

Dynamic creative optimization is an AI feature that delivers the optimal combinations of creative imagery and headlines to individuals in real-time. This takes the guesswork out of the creative process while improving ad personalization. By letting computers sit on top of the data, you can gain valuable insights into what creatives drive the most impact among core audiences. 

Here are a few tips to make the process of exploring AI go a little smoother:

Bonus AI Marketing Tips

  • Build a strategic foundation: AI saves time and resources, but there’s no point in capturing data if you don’t know your key business objectives.
  • Test and learn: The digital world changes fast. Use data in real-time to make decisions and see if things are working – and if they’re not, to make smart pivots.  
  • Remember the human element: We can’t forget feelings, relationships and human interaction. Make sure human insights from your team and your audience are still part of planning and optimization processes. 

If you’re a marketer, start getting more familiar with AI. Don’t expect to get it all right at first. Ask questions and talk with experts in this space. Start learning and move forward from there.

To work with Coegi and leverage our Data & Technology experts for your brand, contact us today

Cookieless Targeting and Identity Solutions

An audience-first approach or 1:1 marketing is something brands often strive for. As a digital marketing partner, it’s at the core of our mission. 

However, the ‘cookieless world’, the meanest curveball Google has thrown at the industry yet, is approaching – even if its arrival has been further delayed. With cookieless targeting, being ‘audience-first’ takes on a new definition. 

Targeting will no longer be as simple as building an audience persona and pressing “go” on pre-made data sets. Instead, it’s about really diving into the ethos of who your core consumer is and using that intel to guide your audience strategy.

We sat down with Coegi’s Account Strategy Director, Savannah Westbrock, to get her perspective and tips on how she’s helping clients prepare for cookieless targeting. The following article is an edited transcript of that interview.

It’s Time to Improve Your Audience Research

How should audience research change in light of the cookieless future?

There are three changes in audience research most marketers need to make to ensure the data tells an accurate story: 

  1. Understand the methodology: We rely on research every day to inform our media plans and marketing decision making. However, we often don’t peel back the curtain to understand how that data was collected and consider potential biases. In the cookieless future, it will be even more important to think critically and be selective with our data sourcing. 
  2. Exit the platform: Don’t rely solely on demand side platform information and forecasting for your planning. This data will be most affected by cookie deprecation. Instead, combine platform insights with external research that never relied on cookies. 
  3. Diversify your data sources: It’s time to get creative. Platform data and syndicated research will still hold value. But, you’ll need to layer it with non-syndicated data and first party data. Combine these tools to see a full picture. Even consider non-media data, such as macro-environmental trends, which may impact your audience’s behaviors and the industry at large. 

What types of cookieless data should brands be gathering to understand their audiences?

Pixel-based retargeting is essentially out of the picture. The best pivot brands can make is mining their own first-party data. But you don’t have to rely solely on your own data. Combine ‘hard’ data such as your website and platform analytics with ‘soft’ data such as social listening. Taking a more journalistic approach with these softer data sources can actually provide more meaningful insights and make your brand more authentic and trustworthy. 

Tip: Balance quantitative and qualitative data. Trust your instincts and use research to back up or refute as needed. 

How can marketers collect and expand their first-party data? 

First, you need to have systems in place to generate leads. Then, it’s all about what you do with that customer data to maximize results and become more strategic. 

Lead generation campaigns: Keep first-party data and zero-party data collection top of mind when planning campaigns. For example, promoting a useful downloadable with a lead form. This will help drive consideration and give you an opportunity to learn about your audience in exchange for shared value. 

Data enrichment: Once you collect and understand your first-party data, you can upload it to enrichment tools, such as consumer survey platforms. This helps you learn more about your audience’s interests, media consumption and day-to-day behaviors. 

Cookieless Audience Targeting Alternatives

Is contextual targeting an effective cookieless targeting strategy? 

If your audience research is thorough, you will know the channels your audience frequents, their preferred devices, favorite shows, and where they are most engaged. Pair this insight with contextual placements that make sense for your ads. 

Contextual strategies fell by the wayside in the late 2010s. Many brands focused on only reaching the “perfect” deterministic, addressable audience with cookie-based data. So some marketers may fear for impression waste by comparison. However, there are now many sophisticated contextual solutions that allow for hyper-customization and reach niche interest groups

For instance, Natural Language Processing (NLP) algorithms are beginning to better understand the actual context of ad placements using artificial intelligence. This allows marketers to implement positive sentiment targeting and smarter keyword targeting. Smart contextual offerings can optimize to real-time content trends, going beyond standard display. 

Are new user identity solutions direct replacements for cookies? 

Cookieless identity solutions such as Unified ID 2.0 and Liveramp’s IdentityLink will help reach high-value segments without wasting media dollars on the wrong audiences. But, there will still be gaps. Pre-made audiences and 1:1 third party targeting will not be the same. As cookie-based information is no longer shared across the web, we’ll need to tap a few different buying strategies. I also expect walled gardens will center in on themselves more, protecting their high value audience data. 

To overcome these challenges, marketers use all the data at your disposal to understand customers better, from channel-based information, survey data, CRM analysis, Google Analytics, and more. 

Cookieless Targeting Tips

What’s your best advice to brands preparing for a cookieless future?

There’s a lot to consider, but the two simple things brands should prioritize are: 

  1. Invest in first-party data collection
  2. Start testing now 

The most important thing you can do now is establish a baseline. Then you can conduct a true study comparing your performance with and without cookies. Cover these two bases and you will be ahead of many brands. From there, you can continue to refine and adjust your research, targeting and measurement strategies as the industry evolves. 

Our team at Coegi is actively testing cookieless solutions and brainstorming innovative cookieless media plans for our clients. For more strategic insights and tips on how to prepare your digital advertising for this change, listen to our full podcast episode on cookieless targeting here

Coegi Partners

/ Contact

Tell us about your project

This field is for validation purposes and should be left unchanged.

Coegi Partners
Skip to content