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.
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.
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.
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.
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
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.
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.
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?
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.
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.
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.
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.
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.
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.
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
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:
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
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?
OR
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.)
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.
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.
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.
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:
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.
Here are three ways Coegi has applied the performance scoring model to our clients:
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.
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.
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.
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.
