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:
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.
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.
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:
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.
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:
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:
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.
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.
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.
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.
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.
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.
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
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:
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:
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.
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:
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.
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.
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.
4X
ROAS
32%
YoY Sales
$3.73
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.
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.
16%
Lift in Brand Awareness
25M
Video Views in 2 Months
23%
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:
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.
More importantly, sales increased nearly 300% YoY, with 6% category market share; leading to the brand being acquired by Coca-Cola.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
Some methods for measuring media campaigns in the cookieless future include:
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.
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.
Begin testing and learning today to proactively understand what will and will not be effective in the cookieless future.
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.
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.
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.
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:
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.
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.
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:
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.
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.
There are three changes in audience research most marketers need to make to ensure the data tells an accurate story:
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.
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.
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.
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.
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:
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.
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.
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.
In short, these models should be considered and analyzed alongside each other, as they both offer valuable insights from unique perspectives.
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?
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.
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.
Efficiently analyzing data and providing ongoing campaign optimization recommendations is critical for driving the best results for our clients. However, the complexity and rigidity of data sets can make the most meaningful insights difficult to accurately uncover. In order to elevate your analysis and provide better insights for your campaign performance, you must stay curious, using knowledge and creativity to bolster your approach.
Analytics and insights – they are closely related, but not the same. Analytics is a way we approach a problem that is standardized and repeatable. But what we’re really after is turning these numbers into insights. For instance, you might see that a lot of people clicked on your ad – analytics. But going further to ask ‘What does the high click-through rate mean? and deciding how are you changing campaigns based on that knowledge – that’s insight.
In other words, analytics are repeatable, standardized processes to understand data and gain information. Insights are what we do based on that information.
When running a campaign, there are often thousands, sometimes millions, of data points to analyze. Even when campaign goals and KPIs are clearly defined, it can be difficult to immediately identify the more subtle and complex insights hiding within the numbers. That is where curiosity and creativity come into play – we start looking for whatever stories the data can tell.
Asking thoughtful questions and keeping an open mind about what you see in the data helps identify unexpected opportunities and avoid pre-existing biases in your analysis. This allows patterns to appear in the data and provide beneficial insights for future campaigns. No matter what kind of data you are working with, there is always a learning to uncover.
For example, perhaps you have a channel that is unexpectedly driving better sales numbers than others. Instead of broadly accepting the success of the campaign, investigate why that channel did well so you can apply those learnings and build on that success. Stretch your mind to stretch your data and let it come alive for your brand.
Anyone deeply entrenched in data analysis will know it is all too easy to get buried, and even lost, trying to extract every bit of intel from the data. And while we want to glean as much insight as possible, it can be difficult to know where to stop once you start down a rabbit hole in an effort to get every morsel of information. At some point, the return on that time investment will diminish and you’ll start coming to the same conclusions, just from different angles.
It’s critical to strike a balance, understanding when it’s valuable to take more time to really dive into the numbers, asking a variety of questions before taking action on the insight. Quarterly reviews, post-campaign reporting, planning phases – these are all times when deep analysis and questioning of the status quo is beneficial.
When you start to dig, the data can seem like an endless hole if you don’t know what you’re searching for. But if you root yourself in your overarching measurement strategy, centering on your business goals and each channel’s role in helping achieve that success, it will become much easier to determine if your media is working or not.
It is always best to spend more time upfront talking through the strategy as a whole before you begin analysis. Then it will all come into play and be very easy. The amount of time you spend should be way more upfront than it is actually building out final data visualizations and dashboards.
Curiosity and data analysis – yes, they do go together. Applying creativity and allowing space for human intuition and insights is key to data storytelling. But to be an effective and productive data analyst, you need to balance the art and science. Keep in mind what is and isn’t reasonable. Live for the data. Explore it and search for the stories that it weaves together. But, avoid investing your valuable time on a dark rabbit hole that leads nowhere.
