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.
Category: Marketing Measurement
Google Analytics 4: The Transition is Here
Get ready for the imminent sunset of Universal Analytics. Starting July 1st, Universal Analytics will no longer process data. To ensure a smooth transition, it’s crucial to deploy and test Google Analytics 4 tags in place of your existing website analytics infrastructure. We understand that this task can be overwhelming, especially if you’re unsure where to begin. That’s why we’ve created a comprehensive checklist to guide you through the entire process:
Learn About GA4
Start by familiarizing yourself with Google Analytics 4. While it shares some similarities with Universal Analytics, there are significant differences in the user interface, event tracking, and conversions, as well as the modification or removal of certain metrics.
Google Resources:
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:
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:
- Identify desired business outcomes
- Determining the key performance indicators to signal success
- Evaluating incrementality
- Creating a cycle of testing and learning
- 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:
- Unify disparate data sets to better contextualize and assess data analytics
- Clearly communicate the impact of marketing on business outcomes
- 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:
Measure What Matters
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:
Driving Retail Traffic and Sales for a Beauty Brand
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.
What is the Coffeyville Effect and Why Does it Happen?
Coffeyville, Kansas
Have you ever seen an excessively large amount of US traffic supposedly coming from Coffeyville, Kansas in Google Analytics? This specific geolocation may even contribute the most amount of sessions worldwide. It is known as the Coffeyville Effect.
What is the Coffeyville Effect?
Even though you may not be targeting Kansas, internet users who enable IP masking tools will report their location back as the exact geographical center of the U.S. which is Coffeyville, KS.
This effect can also happen with some mobile devices that report back incorrectly or as “unknown”.
Analytics and Ad Serving programs will often attribute those unknowns to Coffeyville. An example of this that you might have experienced is when your phone’s location service (such as on Google or a weather app) estimates you are in a city several hours away when you are connected to mobile data instead of home wifi.
Is Google the Problem?
Google Analytics provides a number of geographical dimensions, such as City, Country, Continent, etc. The values for these dimensions derive automatically from the IP address of the hit. The Coffeyville Effect occurs when a location is not accessible by the data.
Google sends the IP addresses of traffic sources to a third-party data source to determine the location. If the third-party source determines the record of the visitor location is accurate, Google Analytics populates the fields with the location data. If the third-party source cannot find the location, the value of the corresponding fields will register as “(not set)” and then assigns the default location to the center of the US.
When Coffeyville, Kansas pops up as one of your traffic sources, it’s likely that this is the fault of one of the third-party data sources that Google uses, rather than Google itself. Unfortunately, Google does not disclose these data sources.
