Tag: Marketing Data and Analytics

Make Smarter Marketing Decisions with Media Mix Modeling

Posted on by Lindsey King

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:

How Curiosity Can Improve Your Data Analysis

Posted on by Coegi

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. 

Moving from Data Analytics to Data Insights

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.  

The Benefits of Using Curiosity in Your Data Analysis

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.  

Avoiding Analysis Paralysis

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. 

Guide Curiosity with Measurement

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. 

Key Takeaways

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.

Data Strategy Podcast Episode

How to Prove ROI for CPG Brands Using Loyalty & Purchase Data

Posted on by Coegi

The Brief

Bahlsen partnered with Coegi to relaunch their brand across six geographic locations in the United States. Partnering with Catalina, a consumer data company, the teams were able to target, measure, and optimize CTV and display campaign results in real-time across multiple platforms. This resulted in significant incremental sales lift and an increase in new buyers and repeat purchases. 

Highlights

28%
Sales Lift

38%
New Buyer Base

8%
New Buyer Repeat Purchase Rate

Challenge

Measuring marketing campaign ROI can be complicated for CPG brands. Data from online and in-store sales combined with the cyclical purchasing habits of consumers can significantly blur the lines of marketing attribution. It can also handicap a marketer’s ability to make informed optimizations throughout. Without these insights, a true understanding of campaign success can be out of reach for most brands without the assistance of advanced measurement. 

 

Solution

We knew the key to success for Bahlsen was gathering real-time sales data to inform quick optimizations and gain feedback on sales lift. It was also important to reach audiences across multiple channels to facilitate consideration and keep the brand top of mind. 

We looked to Catalina to assist with this challenge. With almost 40 years of consumer data and one of the largest in-store media networks, Catalina has built an activation, measurement and attribution model. This allows CPG marketers to build and target hyper-focused niche audience groups across multiple channels. 

Using Catalina’s measurement technology and data, we developed and activated highly segmented first-party data lists across CTV and digital display platforms. These audiences consisted of current customers, lost/lagged customers and potential consumers. These rich targeting segments were more likely to engage with and purchase the product than the broader U.S. population. This set the foundation for campaign success. 

However, the real key to driving results for our client was in the cross-channel targeting of these lists to keep the brand top of mind at point of purchase. To do this, our team activated CTV and display campaigns using Catalina’s in-house network. Simultaneously, we were targeting the same audiences on Facebook and Instagram. 

The seven week campaign resulted in an incremental sales lift of 28% (16% benchmark) with a 38% increase in new buyer base and a repeat purchase rate of 8% in those new users. For CPG brands looking to prove advertising ROI, prioritize collecting high quality customer sales data to accurately track and measure sales lift throughout campaigns. To amplify results, use segmented audience lists with a cross-channel strategy to increase reach and frequency among key consumer groups.

AI Optimization – The Paperclip Theory

Posted on by Jacob Amann

Paperclip Maximizing: Machine Learning And The Problem Of Instrumental Convergence

What do paperclips have to do with digital marketing and machine learning? Admittedly, pretty much nothing. The ‘paperclip maximizer’ thought experiment comes from Nick Bostrom at Oxford University. In essence, it looks at the idea that if you tell a machine to optimize to a specific goal it will do so at all costs.

If you told a machine to maximize the number of paperclips it produces the machine would eventually start destroying things like computers, refrigerators, or really anything made of metal to make more paper clips once other sources of metal run out. This concept has been coined as instrumental convergence. 

Paperclip vs Pay-Per-Click

If you transform the idea of paperclips into the idea of paying per click this becomes very relevant to digital marketing. Nowadays, almost every platform touts some version of AI or machine learning to revolutionize campaign performance, which is a boon to everyone.

By releasing control to machines, media buyers can focus on more strategic tasks such as identifying deeper insights for reports and understanding clients’ goals while campaigns continuously improve themselves. They do this by finding and optimizing for what works while avoiding the things that are not driving performance for the brand. 

See this in action on Our Work page. 

Defining Performance

What exactly is ‘performance?’ The easy answer is whatever your KPI may be. It could be clicks, it could be video views, it could be any trackable metric. However, this is already a simplified goal. If you are running a traffic campaign, the marketing goal should not just be to “get more clicks”.

The goal should be something designed to move the needle for the business – brand affinity growth, sales lift, etc.  For instance, driving qualified users from a target audience to an advertiser’s website and increasing brand favorability is a strong goal. This is something bigger than a single metric. A KPI can be a stepping stone and an important indicator of success, but it is not the final objective.

A truly successful campaign will not simply be the campaign that drove the most clicks at the cheapest price point. Success lies in the campaigns that drive true performance towards core business objectives.

Looking for a partner to harness AI technology and drive marketing performance? Reach out to Coegi for a discovery call today.

What Machines Lack

Context. Context is something that a robot has not yet mastered.

As a marketer, I know that increasing clicks, directionally, should push me closer to my goal. The machine knows this too, but this is all the machine knows. It will endlessly optimize to a single goal.

Maximizing the number of clicks given a fixed amount of budget. This can lead to unintended consequences – think back to the paperclip example.

A machine might say only run display banners and forget about high impact formats such as video and CTV. The machine might push 100% of impressions into in-app environments. It would say never buy another out of home ad again. The machine would never know to build brand awareness, because there is no optimization point it can use. 

Machines Need Guidance

We understand the role of the media buyer is not going away, but it is morphing. There is a new symbiotic relationship between buyer and machine which empowers them to maximize your brand as a whole. Successful media buyers do not need to spend 80 hours per week finding every winning media combination.

Most of these tasks can be done through harnessing technology and freeing up time to look at the media plan from a higher level.  These benefits of using AI go directly to our clients in the form of more time dedicated to listening to client needs, smarter digital media plans, and ultimately higher performing campaigns. 

How To Incorporate Smart AI In Your Media Buying

  • Release media optimization controls to AI machines and spend time on strategic campaign elements. 
  • Define performance success beyond the metrics by establishing meaningful campaign goals 
  • Use context to avoid instrumental convergence and potentially harmful optimizations from unattended machines.

Click the button to view our full YouTube playlist on AI for Marketers.

Learn More

Measure What Matters

Posted on by Coegi

One of my long-standing mantras at Coegi is ‘Measure What Matters’.

So when the ANA released a report entitled ‘Media KPIs That Matter’, I was more than a little intrigued.  What the report found won’t surprise too many of us that work in performance marketing: most brands focus on KPIs that don’t really align with their business objectives.  

So why is this?  For starters, there is a lot of pressure for digital campaigns to be ‘data driven’.  I bet if the ANA asked if their members organizations are data driven, 100% would say yes.  The challenge is that there is too much data for the decision makers to truly understand. For marketing veterans that came from creative or PR backgrounds (that weren’t exposed to digital media buying earlier in their careers), it is challenging to grade the effectiveness of an omni-channel digital marketing.   Thus, they lean on the stats they feel most comfortable with: CPM, CPC and CTR.  Website traffic, reach and completion rates.  What we have longed referred to at Coegi as vanity metrics. To be fair, media efficiency should be a factor, but far less than many brands think. As my friends at The Trade Desk say, you can’t report on CTR on an earnings call.  

But what about ROAS?

Isn’t that the magic metric we should all be optimizing to anyways?  It should be in theory, but in practice, it all depends on attribution.  Is 100% of the conversion credit going to the last touch or last impression?  There are very few digital programs that are even attempting multi-touch attribution, and those that try are stymied by walled gardens that don’t share a unified measurement framework.  ROAS numbers are only as accurate as the data you use to analyze it, and too often there is more noise than signal in last-touch attribution.  Recent changes to app tracking on Apple phones and the impending elimination of third-party cookies on Google Chrome make attribution all the more challenging.

So what about the agencies?  Isn’t it their job to advise their clients as to the metrics they should be measuring?   Certainly many performance strategists are pushing to move towards more meaningful measurement, but it often involves a lot more institutional buy-in at the brand that you would expect.  Advocating an advanced measurement framework at the end of a proposal just isn’t going to cut it.  Often, you not only have to educate the marketing team, but the C-suite, product and sales teams as well.  

Creating a path to measure what matters

So what is the path forward for marketers trying to determine their media KPIs?  From my perspective, there is no singular KPI that defines success for any digital marketing campaign.  Instead, we should build custom measurement frameworks across multiple KPIs, that incorporate not just media efficiency metrics, but also engagement, brand lift, transactional data, and ROAS analysis, to get a better understanding of your digital program as a whole. Furthermore, it can be worthwhile to revisit the more academic and statistical forms of analysis, such as media mix modeling, matched market tests, and regression analysis, to get to the heart of success. 

Recommended reading:

How to Increase QSR Market Share and Awareness

Posted on by Coegi

The Brief

A QSR client was faced with uncertainty as the pandemic hit the United States in March 2020. Coegi was tasked with coming up with a flexible media strategy to address the new dynamic.

Highlights

32%
Increase in Delivery App Purchases

$5MM
Incremental Attributed Sales

Challenge

Most of this QSR’s franchised-owned stores had a 50%+ decrease in traffic and sales in the second half of March 2020. With a limited challenger brand budget, we needed to boost market share while addressing the shift in consumer behavior.

 

Solution

We focused specifically on growing market share among loyal customers. Initially, we drove them to make delivery purchases and later to in-store. To do this, we leveraged existing first-party data that was tied to point-of-sale. 

The outcome was a proprietary scoring model, dubbed “The Crave Score.”  This custom scorecard analyzed brand lift attributes, visitation, and point of sale data to dynamically align budget allocation and creative strategy. It also allowed us to segment based on store visit frequency and share of wallet.

For high share of wallet customers who hadn’t visited recently, we focused on high frequency with ads promoting top-selling sandwiches. We focused spend on areas with high pre-COVID brand recognition, knowing that consumers would be more selective during this time.  

Then, as stimulus checks were distributed, we applied lookalike modeling against the strongest customer segments to identify high potential new consumers. 

These were the key results: 

  • 32% increase in delivery app purchases in 6 key markets.
  • $5MM in incremental attributed sales in the Q2 post-COVID period
  • Positive press write ups in Bloomberg and Restaurant Business

Driving Retail Traffic and Sales for a Beauty Brand

Posted on by Coegi

The brief

Coegi created an omni-channel campaign to drive in-store retail traffic and attributable sales for a beauty client during a key sales period.

Highlights

$0.25
Cost Per Store Visit

4.6M
In-Store Conversions

Challenge

Coegi’s beauty brand client sells skin and lip care products online and in retail locations across the US. In-store retailer purchases drive the majority of their revenue and were the priority for our marketing strategy. 

However, it can be challenging to track and measure the impact of paid media on in-store purchases. Coegi needed to show how this campaign drove sales and purchase consideration during the holiday season.

Solution

Coegi used three core audiences to target this campaign – Eco Consumers, Millennial Moms and College Consumers. Additionally, we used high-intent holiday shopping audiences to maximize the time of year.

We activated these audiences across display and video campaigns, optimizing for reach and completion rate to drive in-store traffic. This was reinforced through retailer-specific creative to ensure shoppers knew where the brand was available. A foot traffic study was also implemented using mobile app ID data to correlate ad exposure with store visitation. 

We took a test and learn approach, using traffic and purchase data to determine top performing retail locations. We then reinforced those top stores in key geos, further building upon sales momentum. This campaign drove over 4.6 million store visits, with an average cost-per-store-visit of $0.25 across all media and millions in sales. This was highly efficient for driving brand consideration compared to the $3-7 product price point. 

Q4 sales reports indicated that the strong revenue numbers were directly tied with efficient cost-per-visit metrics. Analysis of foot traffic conversions also helped identify top markets for the brand. This campaign displayed the importance of combining advanced measurement studies and non-media data to determine the incremental impact of digital media on driving retail traffic and sales. 

Data Storytelling: How to Act on Analytics

Posted on by Jacob Amann

Data storytelling transforms brands. Take an inside look at how Coegi crafts stories with actionable recommendations for our clients by finding the human element in the numbers.

As marketers, we now have access to vast amounts of data. There’s been a major influx of analyst jobs in the last several years as a result.

But are we telling compelling stories with that data and adjusting our strategies based on the insights? If not, what’s the point?

The true value in data lies in how we use key insights to take informed actions for businesses. In other words, with data storytelling.

4 Steps to Set up Data Storytelling in Your Analytics Practice

Gather: Set up a measurement framework to capture metrics that matter most

First, set performance KPIs that ladder up to your business goals. For more information on how to do this, feel free to reference our Marketing Measurement Playbook. Then, prepare a learning agenda to determine the types of information you are looking to understand from your campaign.

Are there hypotheses you want to validate? Assumptions you want to challenge? Audience learnings you want to gather? Use the agenda to help answer these questions.

Learn: Capture and visualize data to pull key insights

Once the campaign is running, you begin to gather data: this is your “what.” Now, it’s up to you and your media partners to uncover the “why.” Look at the underlying narrative running through your data to build a meaningful story arc.

A great way to do this is by visualizing the data in a way. This method of data storytelling allows you to easily identify trends and understand performance relative to goals. Consider layering campaign data with third party data to see a holistic picture and identify outliers or interesting correlations. Look at the data from a macro lens. This helps weave the micro data points into a cohesive story makes sense to both marketers and external team members like the sales team or the executive suite.

We often talk about blending art and science in our marketing strategies – that same concept applies to data analytics. When communicating results to internal stakeholders, qualitative information with direction from quantitative data often speaks volumes for executives. But only if you tell the right story. You want to layer in context, feeling and understanding – the human emotion and behavior will amplify the data you’ve collected. Knowing the audience and tailoring your story to their point of view will help ensure the information resonates.

Brent Dykes, author of ‘Effective Data Storytelling’, says “Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes”. This leads us into the next step: application.

Apply: Transform insights into actionable strategies, and repeat.

Data storytelling provides an opportunity to connect the dots between various media spend across channels and show how they work together to reach your customer when and where it mattered. If done right, it will also show areas that didn’t succeed. Those failures can guide new messaging or creative on particular channels, or the adjustment of certain tactics and spend reallocation. Additionally, it should highlight any gaps between customer touch points and eventual conversion or retention. Lay out clear, actionable steps based on analytic insights to transform your digital marketing strategy.

Refine and Repeat

Marketers create an infinite cycle of improvement through this data feedback loop. The digital ecosystem is constantly in flux. New platforms, privacy laws, consumer behavior and more, creating twists and turns in the media landscape. This process is never perfect. But, by using performance marketing data to tell your brand story, you can ensure it is always evolving and being refined. This practice minimizes media waste and allows marketers to make more informed decisions and craft winning strategies.

“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” – Stephen Few

If you need help finding the story in your data, Coegi is here to help. Set up a discovery call with our team to explore opportunities for your brand.

Balancing the Art and Science of Advertising

Posted on by Coegi

 

Advertising may elicit thoughts of uniquely designed print ads and Super Bowl commercials; the output of creative minds with the ability to persuade consumer decisions. For some people, advertising seems to be a strictly artistic discipline when all one sees is the final creative product. In truth, the art and science of advertising must blend together in order to maximize marketing campaign results. 

“The solution to capturing consumers comes down to a sophisticated blend of art and science.”

– Paul Robson, President International at Adobe

On one end of the spectrum we have science, the known and the unknown, for the analytical and curious minds looking to uncover unique insights and trends. At the other end lies art, a subjective and ever-changing expression of unique thoughts and imagination in which there is truly never a right or wrong. There are a variety of perspectives on what the core of advertising is, when realistically both science and art’s synergy are central to achieving sustainable, successful strategy and activation. 

Why you need both art and science to build a brand

With art highly visible and science working behind the scenes, both pillars are critical to build the brand foundation. Our President, Sean Cotton, recently said that data is best used as a guide to craft engaging campaigns inspired by the numbers, keeping creative at the forefront while ensuring it is impactful with analytics. Sometimes this synergy is simple when you are working with a full-service agency. But, it is often more effective to work with separate creative and performance media agencies. As long as both sides communicate and prioritize business outcomes, the brand is set up for success.

How to optimize creative with data insights

At Coegi, we are the science fueling the art. We dig deeper into the what’s, why’s, and how’s of digital media through robust data analysis and industry research. The basis for our campaigns is research and analysis of our brands’ audiences. Then, we rely on machine learning and human intuition to optimize.

However, when it comes to strategy, it all really starts with the measurement framework.  This ensures we can understand if the research and thinking we put into action is actually impacting the brand’s bottom line. As a result, this process is not completely devoid of art. In fact, around 75% of an ad’s impact can be attributed to quality creative.

However, great creative pieces need data-driven insights to be delivered effectively. Our teams have to get creative with how and where we reach audiences to make the greatest impact. By doing so, we can better deliver solutions that make the art work harder, thus building up ROI. In essence, our strategy is our art.

Collaboration is key to success

At the end of the day, effective collaboration is at the core of the art and science of performance advertising. Communication and transparency between departments and our partners offers balance, allowing for seamless work processes and better results for clients. When this is done well, the lines between art and science begin to blur – proving that advertising isn’t black and white. It’s the molding of colors as the science and art of an agency work together to create a balanced composition paving the way for brand growth.

“The purpose of marketing is to influence the behaviors of others to bring them closer to your brand, organization, product, or service. The best way to achieve it is to strike a balance between the hard data and evidence that support the best path to take, and the human appeal and creative approach necessary to solidify its impact.”

– Eminent SEO CEO, Jenny Stradling

 

 

Why Walled Gardens Will (and Won’t) Be More Critical in the Future

Posted on by Coegi

 

As we explore the world of cookieless digital advertising, marketers will be focusing much of their attention on walled gardens with valuable first-party data. However, even walled gardens have issues we will need to navigate through in order to achieve business goals which are tangible to the financial guardians of brands.

Most analysts predict that walled gardens (in particular Google) will be the safest place to conduct audience targeted buys in 2024.  Even while Google’s DSP allows marketers to buy a lot of inventory, it is currently more limited in audio, connected TV and DOOH inventory.  These are channels where context is probably more important than the precision of the audience and where there is likely going to be a need to diversify to other advertising platforms to achieve a successful omni-channel strategy.

Using Facebook User Data

Facebook does have robust behavioral data from signed-in users; however, iOS 15 makes it more challenging to perform audience-based buys and to attribute conversions.  Some of our early campaigns showed a 15x increase in CPA within the platform, but nearly no impact on actual sales.  This means that conversion data on the Facebook platform was (and is) solely directional for most advertisers. While good for the business, this might be more challenging for marketers trying to prove their marketing is “working.”

Should You Trust the Algorithm?

The big ad tech players, and thus some agencies, will likely advise brands to ‘trust the algorithm’ even more than they have in the past, as Google, Facebook and Amazon don’t give specialists a lot of control over or insights about many aspects of their buying decisions.  Facebook in particular makes it challenging to control frequency, and DV360’s lookalike modeling is very opaque.  Against a lack of accurate measurement across each walled garden, brands and their agencies need to develop more holistic, advanced measurement frameworks.

How Will Cookie Deprecation Affect CPMs?

While scale is impacted slightly outside of Google Chrome and Android apps, there are still ample opportunities to bid for inventory in these environments.  However, with fewer buying platforms to conduct audience-based buys and fewer impressions to scale against, CPMs will likely increase, in particular on video.  This might put pressure on agencies to ‘keep the costs down’, which in turn may increase traffic from bots and fraudulent inventory.  Brands need to expect an increase in CPMs while not incentivizing a decrease in inventory quality.

A Walled Garden SWOT Analysis

Strengths – Google, Facebook, Amazon and Apple each have huge first-party data sets.  And not just in volume of users, they have robust metadata around each profile as well, from account information, purchase history and behavior.  Even if ID-based solutions grow in count, it’s possible we may not be able to append significant amounts of secondary data to each profile to be scalable for marketers.

Weaknesses – You will undoubtedly need adjustments in terms of attribution and measurement.  Even today, if you were to believe the metrics from each platform, nearly all of your automated marketing channels would have +ROI for the same purchase. Paid search, Facebook conversion ads and programmatic retargeting can’t all have a CPA of $10. They can’t produce 10,000 sales when you only sold 3,000 products. This is because each is taking credit for any time a user touches their ad. Because the walled gardens don’t share a common user profile, multi-touch attribution can be disjointed and inconsistent. It’s safe to say the methods of achieving measurement will have to change.

Opportunities – Lean into zero-, first- and second-party data in walled garden platforms, and rely less on retargeting. This allows for stronger prospecting and less reliance on audiences that were likely to “convert” anyway and, therefore, inflate marketing metrics.

Threats – Because many marketers and brands will be leaning into walled gardens, there will likely be an increase in advertising costs on these platforms. Budgets will need to increase to achieve the same scale as before.

So what does this mean?

At present, our suggestion is to lean into walled gardens for precise audience targeting. But, begin measuring success of your advertising program at a higher level.  Some examples of this include matched market tests, media mix modeling, and control vs. exposed methodologies.

Yes, this will make it more challenging to know which 50% of your marketing spend is effective. But, it’s the best solution with the reduction of transparency in algorithmic data and therefore less understanding of success from a conversion data standpoint.

This will also force marketers to start looking at the data as a whole. It’s time to get away from optimizing towards last-click and last-touch metrics. They have provided misleading signals for years.

Regarding measurement changes, advertising campaigns need to be set-up to reach business goals rather than just media metric KPIs.  To achieve this, individual channels and tactics will need to identify leading indicators to optimize toward.  Engagement rates, reach, completion rate, and measures of media effectiveness like CPM/CPC should become more of a focus rather than CPAs.

Check out our 5 Step Guide to Measuring Marketing ROI to get started:

Download Coegi’s Measurement Guide
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