How to Strategically and Compliantly Use First Party Data for Regulated Industries

In healthcare marketing, particularly under strict regulations like HIPAA, the use of first-party data (1PD) demands a nuanced, strategic approach. Leveraging 1PD responsibly and effectively can unlock significant value while maintaining compliance. Healthcare marketing must prioritize privacy at every turn, with specific measures to protect individual rights:

  • Explicit Consent: Always secure explicit consent for using any Personally Identifiable Information (PII) in marketing communications. This is especially vital in highly regulated industries like healthcare, where patient trust is paramount.
  • Data Anonymization: Anonymizing or tokenizing PHI (Protected Health Information) before utilizing it for any marketing purpose is crucial. This ensures that even in data-driven decision-making, no individual’s privacy is compromised.

So where do you start?

Here’s how Coegi recommends maximizing your use of 1PD to strike:

1. Non-PHI Data Utilization

The foundation of responsible 1PD usage lies in focusing on non-PHI data, ensuring patient privacy is never compromised:

  • Social Followers and Demographics: Voluntary, non-sensitive data such as social media engagement and publicly available demographic insights can serve as a starting point for audience segmentation. This data can be analyzed to create preliminary target personas, ensuring that marketing is tailored to audience interests without venturing into sensitive areas.

2. Lookalike Modeling with Tokenization

To unlock deeper insights from first-party data without compromising privacy, tokenization must be applied before any modeling begins:

  • Tokenization First: Before conducting any analysis, customer data is tokenized, ensuring personal identities are anonymized. This enables the organization to securely harness insights without exposing PHI.
  • Modeling High-Value Clients: Post-tokenization, focus on identifying high-value clients based on their Lifetime Value (LTV) and other non-PII metrics. By refining marketing strategies using these models, businesses can allocate resources more effectively toward customers likely to drive higher returns.
  • Cohort Targeting: A cleanroom environment provides a secure way to apply cohort-based targeting while maintaining compliance. This enables marketing teams to group customers with similar characteristics and behavior patterns for more personalized messaging.

3. Index-Based Matching for Lookalike Audiences

Lookalike modeling is essential for audience expansion, and index-based matching offers a compliant, privacy-conscious method:

  • Interest and Propensity Data: By using index-based matching, organizations can match interests and customer propensities without direct data matching. This allows for effective targeting while protecting personal data.
  • Building Lookalike Audiences: Insights from tokenized data, combined with index-based matching, enable the creation of lookalike audiences, allowing businesses to broaden their reach to potential new customers with similar characteristics to high-value clients—all while adhering to strict privacy regulations.

4. Aggregated Campaign Optimization and Analytics

Optimizing marketing campaigns through data analytics is key, but it’s critical to ensure compliance throughout the process:

  • Automated Targeting: Machine learning can help automate and continually refine targeting strategies by analyzing past campaign performance. By doing so, organizations can keep their messaging relevant and impactful, based on real-time insights from previous campaigns.
  • Conversion Rate Analytics: Aggregated, anonymized data should be used to assess and optimize conversion rates, keeping campaigns both effective and compliant.

Conclusion

By strategically using first-party data in compliance with privacy regulations, healthcare organizations can maximize the impact of their marketing efforts. The approach of focusing on non-PHI data, using tokenization, and applying lookalike modeling ensures the organization maintains patient trust and regulatory adherence, while driving more targeted and effective marketing campaigns.

Want to learn more about how to maximize your first party data? Contact us to schedule a discovery call. 

Unleashing the Power of Data Velocity

Imagine you have a state-of-the-art garden hose connected to an endless supply of water. Sounds fantastic, right? Here’s the problem: unless you turn the handle and direct that water where it’s needed, your garden won’t grow. 

Data in today’s digital marketing landscape is just like that water – abundant and full of potential, but only valuable when we put it to work.

In today’s marketing world, data velocity is that crucial turn of the handle. It’s not enough to have a database reservoir filled with information, sitting idly; you must build insights from this information with speed and efficiency. Both are critically important in being able to channel that data to nurture your customer relationships, grow operational efficiencies, identify and act on market trends, and see your business reap its benefits. 

The Data Dilemma: From Stagnant to Strategic 

Just as a garden without access to the water won’t grow, data that remains untouched in a database, no matter how vast, cannot influence decision-making, improve processes, or provide return on investment. To harness the true value of your data, it’s important to: 

Activate Your Data:

Breathe life into raw information by transforming it into actionable insights. It’s in this transformation – whether through business intelligence, machine learning models, or insightful reporting – that your data’s true value emerges, empowering your team to make informed, impactful decisions.

  • Implement robust ETL (Extract, Transform, Load) processes to clean and structure your data.
  • Leverage advanced analytics tools to uncover hidden patterns and predictive trends that shape strategy.
  • Focus on the insights that directly influence your marketing strategies. Whether it’s identifying customer segments, optimizing ad spend, or tracking campaign performance, prioritize data that can be directly applied to your decision-making process.

Prioritize Relevance:

Not all data is created equal – the value of data is inherently tied to its context and use case. Focus on the data points that truly matter for your specific goals.

  • Master the art of data wrangling as a critical process. Clean, structure, and enrich your raw data to transform it into a goldmine of insights by combining disparate data sets, filling in missing values, or normalizing data for consistency.
  • Incorporate third-party data strategically. External data sets can provide valuable context, but crucial to recognize that not all third-party data sets are universally valuable. Some datasets you can reference range from demographic data to search and social trend data to weather data. For instance, weather data might be incredibly valuable for a retail business that needs to anticipate changes in consumer purchasing behaviors in terms of likelihood to visit store locations or which products that may be of greater interest. Yet, for a technology company, weather may hold little value compared to demographic data. Which leads to the next point…
  • Don’t fall into the trap of data hoarding for the sake of information gathering. This can cause unnecessary analysis paralysis that leads to insignificant outcomes. 
  • Streamline Access: A state-of-the-art garden hose can’t deliver clean, pressure-perfect water if it isn’t paired with a perfectly engineered plumbing system. 

The same is true of your data. 

You must produce data feeds that will serve as your digital plumbing, allowing team members to simply “turn the handle” and instantly access the insights they need.

  • Develop a data governance framework that balances security with accessibility. Create role-based access systems to provide team members with the data they need, when they need it.
  • Invest in user-friendly interfaces that make data exploration intuitive for non-technical staff. Platforms like Google Analytics, HubSpot, and various CRM systems offer dashboards and reports that make it easier to interpret data and apply it to your marketing efforts.
  • Encourage curiosity to uncover new data-driven marketing opportunities. Regularly review your data feeds to see if there are any new trends or patterns that could benefit your campaigns.

The Velocity Advantage: Why Speed Matters in Marketing

Timing is everything. Data velocity in marketing empowers you to:

Respond to Market Shifts in Real-Time

  • Monitor social media sentiment and search trending topics to adjust messaging on the fly
  • Use real-time bidding data to optimize ad placements and budgets instantly
  • Quickly pivot campaign strategies based on emerging competitor actions or market disruptions

Personalize Customer Experience

  • Leverage real-time browsing behavior to serve dynamic website content
  • Implement AI-driven recommendation engines that learn and adapt with each customer interaction
  • Use location data and purchase history to deliver hyper-targeted mobile notifications and offers

Optimize Budget Allocation

  • Implement automated budget reallocation based on real-time performance metrics
  • Conduct rapid A/B tests to quickly identify winning creative and copy.
  • Use predictive analytics to forecast campaign performance and adjust spend proactively.

Empowering Marketers: Identify Ways to Help Cross-Department Teams Tap Into Data

As a marketer, you don’t need to be a technical expert to put data to work for your campaigns. What’s important is understanding how to leverage the data that’s been prepared and made accessible to you. 

 Here’s how to get started:

Seek Out Your Power Players:

Work closely with your data and analytics team to identify which data sets are most relevant to your marketing goals. Explain your campaign objectives clearly so they can help you pinpoint the data that will provide the most actionable insights.Together, you can:

  • Conduct a comprehensive audit of your current data sources and analytics capabilities
  • Align key performance indicators (KPIs) with overall business objectives
  • Develop a prioritized list of metrics that directly influence marketing decisions

Invest in Agile Analytics:

Implement tools that provide real-time insights and easy-to-use dashboards.

  • Evaluate and adopt cloud-based analytics platforms for scalability and real-time processing
  • Implement machine learning algorithms to automate data analysis and predictive modeling
  • Develop custom dashboards that provide at-a-glance insights for different roles within your organization

Foster a Data-Driven Culture:

Encourage your team to make decisions based on insights, not just instincts.

  • Provide regular training sessions on data interpretation and analysis tools.
  • Celebrate data-driven successes to reinforce the value of this approach.
  • Implement a system for sharing insights across departments to break down data silos.

The Bottom Line: Data Velocity Drives Value

In the race for market growth, it’s not just about having data – it’s about how quickly you can turn that data into results. By focusing on strategic data velocity in marketing, you’re not just keeping up with the competition; you’re setting the pace. 

Ready to turn on the full force of your data and watch your marketing flourish? Don’t let your valuable data sit idle – contact us today to start your journey towards data-driven marketing excellence. 

It’s time to turn that handle and let your data flow!

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.

The Drum – Attribution Matters: Navigating an ‘Uncomfortably Complex Topic’

Attributing results to particular channels or campaigns is arguably digital marketing’s most dogged problem, with an array of approaches mutating as platforms change. Coegi’s SVP of Marketing and Innovation advises that “it’s not just GA4 that will upend your attribution models. The latest iOS17 update will reportedly strip link trackers from being passed through message, mail, and private browsing. It’s yet another action chipping away at the scale and effectiveness of last-click attribution and website analytics.” Learn more from Ryan and other experts here.

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.

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

5 Step Guide to Successful Marketing Measurement

Marketing measurement is one of the greatest challenges for modern advertisers. In particular, brands have an uphill battle to face when proving full-funnel marketing ROI across a variety of digital and physical channels. We’re here to change that. 

Coegi takes a unique approach to marketing measurement and campaign learnings centered around reaching core business objectives. This is the focus of every digital media strategy and campaign we execute.

Learn How to Succeed in Marketing Measurement With Five Simple Steps: 

  1. Identify desired business outcomes
  2. Determining the key performance indicators to signal success
  3. Evaluating incrementality
  4. Creating a cycle of testing and learning
  5. Using data storytelling for better insights.  

Using these steps, you can ensure clear strategy and efficiencies in any marketing campaign. This is your guide to calculate and prove marketing ROI.  Apply these core principles and watch your business transform. Using this approach will allow you to track and communicate meaningful data, no matter how complex your channel strategy may be. 

How Can You Prove Marketing ROI? 

To prove marketing ROI, you need to focus on aligning quantifiable data points with your overarching business objective. This will look different for every brand, which is why we incorporate custom scorecard models for our clients at Coegi. 

By following the five steps outlined in this guide, you can produce clear, measurable results – in other words, return on investment. These steps are crucial to accurately and effectively measure success and progress within online marketing strategies for any brand. Our specialists at Coegi utilize these tactics daily, and optimize results for clients with consistency by consistently implementing this process. 

Download the Five Step Guide to Successful Marketing Measurement now to get started on your path towards clearly defined success. If you have any questions, don’t hesitate to contact us to set up a discovery call with our team. 

5 Steps to Successful Marketing Measurement 

Step 1: Identify Core Business Outcomes

Clearly established OKRs are the basis for a strong marketing plan. Without clear objectives, you run the risk of prioritizing metrics, tactics, and strategies that don’t translate to meaningful growth – wasting valuable time and dollars. So understand the core business goals at the company level, as defined by key stakeholders. This will be the centerpiece of your marketing decisions. 

From there, your team has a roadmap to clearly understand the organizational expectations of marketing. You can then build a strategic marketing plan to fulfill your role in meeting those bottom line goals.

Make your marketing goals universally known – within your team and with key stakeholders across the organization – to ensure everyone is enthusiastically rowing in the same direction.

Elise Stieferman – Director of Marketing, Coegi

Step 2: Determine the KPIs to Signal Success

Next, determine which metrics are indicators of making progress toward your core business objectives. These will be your key performance indicators or KPIs. 

Be cautious of using media efficiency metrics like CPMs and CPCs as your primary KPIs. They can be effective for evaluating campaign performance on an operational level. But, they often do not ladder up to business objectives. Incorporate metrics such as engagement, brand lift, transactional data, and ROAS analysis to gain better understanding. It can also be beneficial to explore more statistical forms of analysis, such as media mix modeling and matched market tests to get to the heart of success.

Step 3: Evaluate Incrementality

Determining which tactics are helping reach your KPIs isn’t always easy. Just because Facebook’s last click attribution reports show better metrics than other channels does not mean it is the leading driver of results. A purchase today could have been impacted by a connected TV ad served last week that was reinforced by an influencer on TikTok yesterday. 

With decreasing data availability with iOS 14 and impending cookie deprecation, attribution modeling is becoming increasingly difficult and problematic. Marketers should get back to the basics of marketing measurement, such as evaluating incrementality. 

Incrementality shows the influence your collective marketing channels had on the final conversion, no matter where it took place.

Step 4: Create a Test and Learn Cycle 

The goal is to create a cycle of continuous improvement for your marketing. You can do this by using a learning agenda that informs variable testing and optimization points. 

A learning agenda helps identify the key questions you can answer to determine which marketing components are driving the best outcomes. This could mean a better understanding of your target consumer or determining which tactics are most effective. So what could these questions look like?

  • Millennial Moms is an audience with untapped potential for our brand.
  • Our target consumer is more likely to convert on Facebook than Instagram.
  • Lead generation will be more cost efficient on TikTok than Snapchat. 
  • Running CTV and linear TV together will drive an increase in sales versus running only linear.

Whether or not your hypotheses turn out to be true, you will be more informed and your campaigns will become more data-driven and effective. 

Now you have meaningful measurement data – it’s time to connect the dots. 

Step 5: Use Data Storytelling for Better Insights

  • How did various channels work together? 
  • Which areas were most and least successful? 
  • What story is the data telling about your audiences, your creatives, and your selected channels?

Use these types of questions to identify the underlying narrative running through your data. To aid this process, visualize the data so you can easily pinpoint trends and understand performance relative to goals. This intel can guide new creative or adjustment of certain tactics and spend allocation to make your future campaigns even stronger. It should also highlight any gaps between customer touch points and eventual conversion or retention. 

Looking from a macro lens helps weave the micro data points into a cohesive story that makes sense to marketers as well as external teams. From there, you can lay out clear, actionable steps based on analytic insights to transform your digital marketing strategy.

Bonus Marketing Measurement Steps

#1 Tailor Reporting to Individual Stakeholders 

Create a reporting system so each decision-maker clearly understands the impact of marketing. Show ROI to your CFO. Show trends in marketing qualified leads and sales to your COO. Show percent change in new customers to your CEO. Knowing the audience and tailoring your story to their unique point of view will ensure the information resonates and your efforts are valued. 

#2 Move from Campaigns to Long-Term Transformation

This process fuels a data feedback loop, creating an infinite cycle of improvement. Over time, you’ll minimize media waste and make more intentional decisions. It’s never perfect, but by using meaningful data to tell your brand story, you can ensure it is always evolving. 

Contact Coegi for additional information on how to accurately measure your business objectives and see clear marketing results. 

Why the Performance Scoring Model is the Future of Marketing Measurement

 

Is your marketing measurement strategy founded in business intelligence or in media metrics? 

No single marketing metric can equate to business success. Likewise, no single marketing measurement strategy can translate success for all brands. You need a custom solution to accurately track and measure holistic brand health – based on your unique definition of success. 

This is why we believe every brand needs a performance scoring model. 

What is a performance scoring model?

A performance scoring model uses multiple, weighted data sources to define your media’s impact on business goals. This model should combine media data, business data, and advanced measurement studies, weighting each of the data points per their significance. 

Then, you can use this custom formula to create an overall brand performance score. By standardizing reporting and insights from both the granular campaign level to a broader business strategy perspective, this will allow you to make smarter, and more results-based marketing decisions.

Here is a simple example of how this formula can look:

Lift in Unaided Brand Awareness (45%) + Location Visits (20%) + Clicks (10%) + Sales (25%) = Performance Score

Using the Performance Scoring Model to Measure True Marketing Success

Advertising needs to be held more closely accountable to business outcomes. Marketing leaders are feeling this pressure more intensely now than ever. It’s uncomfortable and challenging – but these are necessary growing pains. As the industry navigates increasing consumer data privacy regulations, marketing plans require more complex planning and measurement. 

Simply put – today’s business challenges require more than basic in-platform forecasting and metrics. Media data – impressions, reach, cost-per-click – are too in the weeds to illuminate the full landscape. A performance scoring model incorporates both media and non-media data enabling marketers to make smart business decisions and more accurate predictions. 

It is simply a living, breathing business dashboard that allows marketers to accomplish three key things: 

  1. Unify disparate data sets to better contextualize and assess data analytics
  2. Clearly communicate the impact of marketing on business outcomes 
  3. Predict and inform smart campaign optimizations and strategic decision-making

3 Key Benefits of the Performance Scoring Model

1. Unify disparate marketing data sets

Data aggregation is at the core of this marketing measurement strategy. You may already be using measurement tools to combine media channels in one dashboard. But, business challenges require taking that a step further to reveal brand insights. 

The scoring model gives you a new understanding of marketing performance across the business using both conventional, and unconventional, metrics. This levels up your data analysis to go beyond engagement rates or a cost per action. You can add context by bringing in factors such as economic indicators, health trends, or any other data points impacting the business or consumer behavior. 

It’s not necessarily a tool to drive new sales or leads. But, it does allow you to frame conversations about multiple KPIs in a concise, digestible way. It can guide your marketing strategy so the media can perform better, which will impact long-term growth of bottom line metrics. Ultimately, it resets expectations and aligns teams on the incremental impact of media on business decisions. 

“With the custom scoring model, we work to see a holistic view of performance, setting meaningful KPIs and holding media accountable to business goals.”

– Ryan Green, VP of Marketing & Innovation, Coegi

2. Clearly communicate marketing results

The custom scorecard offers a more objective, quantifiable number you can use to communicate to key stakeholders. Communicating media’s value to non-marketers can be challenging at best, especially if you’re speaking with acronyms that do not apply to their daily jobs. By standardizing disparate data sets, you will be able to more easily achieve buy ins. 

For example, which of these is easier to understand? 

  • In March, FB CPMs decreased by 9.5%, CPLPV rose by 33.4%, and CTR was 1.7%. 

OR

  • In March, our overall media score was 7.5 out of 10, a 1.2 point increase from February.

Ultimately, the custom performance scorecard is a more tangible way to showcase directional return on marketing investment, in particular for stakeholders that aren’t in the marketing department (like finance or operations). Plus, it’s a very flexible data model. You can easily change the weights of each factor in your scorecard formula to accommodate input from other stakeholders or changing business needs. (We’ll get to how to create your custom formula in the next section.)

3. Inform smart marketing campaign optimizations

Finally, you can leverage custom performance scoring models to evaluate and identify leading indicators of success. You can use it to identify which parts of your media strategy are working in near real-time, rather than waiting months for results. Depending on the non-media data you incorporate, it can also help you make real-time pivots based on external factors. 

For example, you can use this model to identify highest performing DMAs. Then, you could distribute your budget and adjust messaging in softer markets versus stronger markets. Alternatively, you can swap geographic region as the optimization point with different audience groups. You can break down audiences to understand the strengths and weaknesses of each segment. Then again, strategically decide whether you will double down on strong audiences or focus on weaker audiences. 

How to Create Your Brand’s Performance Scoring Model

Ready to create your own scorecard? As you begin, media metrics are your most readily available and straightforward data points, so it’s fine if they make up the majority of your scorecard (at least initially). However, it’s important to pull in some external data as you iterate on your model over time. Otherwise, you’re siloing your marketing from other business factors. It’s like driving while wearing blinders. 

Outside perspective from non-media data guides smarter media decisions. Having that additional context can help you determine optimal frequencies, efficiencies, and top-line analytics goals. 

Examples of Non-Media Data Sources for Your Scoring Model:

  • Sales data: Sales by product/service, retailer, region, etc. 
  • Financial data: Consumer price index, stock market, interest rates, 
  • Infection rate data 
  • Net promoter score (understand your greatest customer advocates from  customers who need greater nurturing)
  • Consumer survey data: brand reputation, store cleanliness, product quality, service quality, brand loyalty
  • Advanced measurement data: sales lift, brand awareness lift, foot traffic lift

And this is just scratching the surface. You can get creative here and pull in more obscure data as long as it’s relevant to the success of your business and able to be analyzed at statistical signficance. 

Weighting Your Performance Scorecard Formula 

How do you determine what weight to give each input? I recommend leading with your intuition. But it should also be a group effort. Collaborate with the people closest to the data as well as the people closest to the brand. To avoid biases, be sure to gather input from several stakeholders:

  • CMO/Marketing Manager – Lead the discussion based on existing knowledge and marketing KPIs.
  • Data Analysts – Help provide guidance as to what data is available for use.
  • CEO/Board of Directors – Ensure strategy aligns with overarching business goals and external stakeholder needs.

As you have these discussions, remember it is an iterative process. The first formula you create certainly will not be the last. That’s the beauty of this custom model. It is adaptable, flexible, and increases in accuracy and relevancy over time as your data collection grows and your formula improves.

Implementing a Performance Scoring Model: Marketing Use Cases

Here are three ways Coegi has applied the performance scoring model to our clients:

Use Case #1 – Attributing CPG Sales to Advertising in Real-Time

Point-of-sale data lets consumer packaged goods brands see exactly how much was sold. However, the problem is speed. You often find out results weeks after a campaign. This is far from the real-time results you need to make agile marketing decisions. 

To identify CPG marketing ROI, brands typically need to go back and attempt to attribute that sales lift. Was it from your media spend? The media people certainly think so. Or did the economic boom really do all the work? Maybe it was the in-store displays… The custom scorecard model measures all of those things at once giving you a better idea of what drove sales. 

If you locate those leading indicators of success, you can have an idea of what’s working in real time. Then, when the sales data rolls in 4-12 weeks later, you can confirm what you assume to be true and adjust as necessary. 

Use Case #2 – Identifying Audience Likelihood to Travel 

The travel and tourism industry is impacted heavily by macro-environmental factors. How is the weather? What are flight tickets and gas prices? Is there a health pandemic halting travel? These kinds of factors influence where media should be placed for maximum results. 

This was especially prevalent during the COVID-19 pandemic. We used the scorecard approach for a state tourism client to create a “COVID-19 Scoring Model”. This scorecard gave each county in the state a score indicating level of opportunity for travel in each market. Using it, we were able to inform media decisions and ensure the strategy aligned with public safety. You can read the full case study here for more details. 

Use Case #3 – Identifying Highest Opportunity Geographic Markets for QSR Chain

Quick service restaurants operate in a competitive, cluttered space. Customer loyalty and share of wallet are major factors driving long-term QSR success. 

Knowing this, we create a performance scoring model for a QSR client factoring in brand lift attributes, visitation, and point of sale data. We even included data on how highly customers rated their french fries. Using this model, we were able to allocate budget to top markets and tailor messaging to boost market share among loyal customers. Read the full case study here

There are infinite ways to apply this methodology across any industry and any brand. At the end of the day, the performance scoring model is about getting to the WHY to inform the what – making our marketing strategies stronger and our clients even happier.

For help applying this approach to your brand, contact Coegi today for a discovery call

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. 

Make Smarter Marketing Decisions with Media Mix Modeling

Every brand wants to pre-optimize their campaigns for success right out of the gate, with parameters in place to quickly attribute success to specific channels, audiences, and creatives. However, as we look toward a future without the simple attribution offered by cookies, we need to get back to our statistical roots of traditional media strategies, those without the easy button involved. That’s why marketers should lean further into the value that’s offered with media mix modeling.

What Is Media Mix Modeling?

At a high level, media mix modeling is a way to define optimal budget allocation for media channels by looking at previous campaign performance. It requires analyzing sales-related data and media metrics (Coegi’s data and technology team recommends 2-3 years worth) to make predictions and strategy optimizations that will, in theory, improve future campaign performance

Is Media Mix Modeling Superior to Attribution Models?

In short, these models should be considered and analyzed alongside each other, as they both offer valuable insights from unique perspectives.

  • Attribution gives you quick, real-time information about how specific media parameters are impacting your business goals. This information is useful when making mid-flight optimizations and short-term reporting.
  • Media mix modeling zooms out to give a bird’s-eye view of how all the pieces are working together to affect long-term strategy and performance. Each model informs the other, but tells different stories. 

How Can Marketers Build Strategies from Modeling Learnings?

It is crucial to fully understand the data you’re analyzing, not just the standard media metrics from campaign reports. What are all of the factors that may have contributed to performance fluctuations?

  • Creative?
  • Messaging?
  • Audience strategy?
  • Seasonality?

Knowing the context surrounding the numbers will give you a strong foundation to build future strategies upon. Using that context as the framework, determine what story the data points are telling you. The numbers don’t lie, but they don’t always tell the whole story. By asking the right questions, and maintaining a test and learn mentality, you will ensure strategic decisions are based on multiple factors rather than just one KPI.

How Do You Know Your Media Mix Model Is Working?

A media mix model makes predictions, but it’s not a crystal ball. Just because Facebook historically performed well does not mean it will continue to do so forever. That said, it is important to develop a continuous learning agenda to design your models.

Test your assumptions based on historical performance. For example, what will happen if you increase the budget for programmatic channels? Do overall business results change? The only way to know is to strategically make the budget adjustment and measure incremental results. From there, you can make more informed decisions about your channel strategy and budget allocation. 

Priming Your Media Mix Model For Success:

  • Keep business goals at the center of your strategy 
  • Gather quality, historical data to measure actionable results 
  • Understand the contextual factors impacting your data results 
  • Consider the sales cycle when designing tests – a longer cycle needs more time between strategic adjustments
  • Share strategic details and learnings across teams. Seemingly trivial aspects to you may impact how a model is built. 
  • Data can be easily manipulated to tell an inaccurate story. Think critically and apply business acumen to make sure you have sound methodology. 

Further Reading:

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