The Best Ways to Measure Marketing Results
Marketers are quick to defend the “why” of their marketing decisions, but less than 40% can successfully measure marketing effectiveness. It’s unknown whether this stems from a lack of knowledge of marketing analytics or a lack of resources. Nevertheless, measurement is a crucial component to driving business growth and should never be overlooked. To help you build a successful marketing strategy, this blog will analyze a few of the best ways to measure marketing results.
Media mix modeling is one of the oldest forms of measurement and planning. This analytics solution enables marketers to measure the impact of their spending across various channels, with insight into how different elements contribute to a single goal (e.g. conversions or revenue). Media mix modeling can also be used to tackle common pain points in brand marketing including:
A media mix modeling analysis determines the relationship between a dependent variable (e.g. sales) and an independent variable (e.g. ad spend across channels). Oftentimes, media mix modeling is conducted at a geographical level. Let’s look at a hypothetical example for explanation: if a marketer spends more money on magazine advertising on the west coast, and more on TV advertising on the east coast, regional sales trends can be analyzed to understand the impact of these advertising efforts. Moreover, marketers can use media mix modeling to analyze how shifting spend from one publisher to another improves results.
The main reason media mix modeling is so well renowned by marketers is that it gives insight into external factors that can determine the success of a campaign. Through long-term data collection, marketers can measure the impacts of seasonality, holidays, promotions, and more across both traditional and digital channels.
Since media mix modeling is conducted at an aggregate level, it cannot account for recency, frequency, or sequences of media exposure at the individual level (e.g. impressions), all of which significantly impact effectiveness. Some other common flaws in using media mix modeling are infrequent reports, lack of insight on the brand and messaging, and failure to factor in the customer experience. For these reasons, most marketers also rely on attribution measurement.
What is it?
Attribution measurement consists of various attribution models used to track engagements throughout the customer journey. The goal of attribution is the same as media mix modeling: to quantify the influence of media exposure on revenue. However, attribution analyzes campaign data at the event level (views, clicks, etc.) as consumers move down the sales funnel. This more accurate measurement further enhances the key benefits of programmatic marketing—feeding even better results into the always-on performance feedback loop within a marketing platform.
There are two key approaches to “attributing” a conversion:
What are its pros?
What are its cons?
The challenge with single event-based attribution is obvious: only one channel receives credit. Let’s look at a hypothetical example for explanation: You’re watching a YouTube video and get served an ad from Walmart for new Beats headphones. You then Google search “new Beats headphones” to learn more about the product. The search results show a Walmart ad for the headphones—you click the ad and make the purchase. The Google ad ends up getting 100% of the credit for that purchase, although the buyer journey started on YouTube.
One of the challenges of multiple-touch attribution is that attribution modeling companies (e.g., C3, Visual IQ, Google/Adometry) vary in the results they provide (e.g., C3 found Adometry gave >360x more credit to Google). Also, consumer exposure is often tied to a single device and may undervalue omnichannel consumer experiences (unless a probabilistic identity graph that can measure activity across various devices for the same person or household at scale is applied).
Panel-based measurement analyzes activity for a sample of consumers and households. Then, it parlays the impact across the entire campaign. This ensures a more accurate collection of information across all of the same consumer’s devices relative to attribution. Panel-based measurement also captures metrics associated with brand awareness, affinity, and purchase intent.
Panels provide consumers an opt-in method (such as surveys) to track deterministic consumer activity. This gives marketers a trade-off in scale for an increase in precision. The panel results are then projected onto the larger population exposed to the marketing campaigns. Some advanced DSPs now incorporate panels and can use survey results to optimize inflight budget allocations.
Panel-based measurement is useful for gathering long-term audience insights. Moreover, panels provide a valid, representative, consistent, and comprehensive view of audiences so that marketers can understand what matters most to consumers.
Given the small sample size of a panel, there may not be sufficient statistics to ensure the measured results will be repeatable. Moreover, consumers who opt into a panel may not be representative of a marketer’s target audience.
What is it?
Customer acquisition cost (as the name suggests), is the cost of converting a prospect to a paying customer. It assesses the effectiveness of a marketer’s customer acquisition efforts by calculating the marketing cost per customer acquired during a specific timeframe (or specific marketing method). This can also help marketers determine which strategies are yielding the best results.
How does it work?
Customer acquisition cost (CAC) takes into account the entire customer journey, from the time a lead enters the funnel until they become a paying customer. The cost of acquiring a new customer varies from brand to brand and is dependent on the level of marketing efforts deployed for an individual:
Customer acquisition cost = Sales + Marketing Costs / # of New Customers
The benefits of measuring marketing results with customer acquisition costs are vast. Some worth noting include:
For less established companies, customer acquisition costs can be extremely high and therefore detrimental to growth. Finding the right balance between cost-effectiveness, scalability, and predictability when acquiring new customers can be a challenge, yet all are needed to properly measure the true impact of marketing results.
What is it?
Incrementality measurement is how marketers go about obtaining the true results of any marketing initiative. Using incrementality measurement, a marketer can determine the exact influence (i.e., the incremental benefit) in achieving their desired business outcome (e.g., more revenue). It measures the true lift of a campaign against its set goals.
How does it work?
Incrementality measurement allows marketers to determine whether a given campaign creates real value (e.g., the sale WOULD NOT have occurred without exposing the consumer to an ad) or apparent value (e.g., the sale WOULD occur whether or not the consumer saw an ad). In order for incrementality to be successful, marketers must determine what KPI (revenue, registrations, etc.) they want to measure for and measure the exposed audiences against the appropriate baseline.
Some pros of incrementality measurement include:
Most marketers eat hidden costs when it comes to incremental measurement. These costs revolve around the construction of a control group, and they’re costs that marketers don’t need to pay.
Moreover, noise from organic buyers is not removed when using incremental measurement and only onsite events (sells or visits) can be analyzed (it’s impossible to compare clicks or costs).
While in certain instances media mix modeling, attribution measurement, and panel-based measurement are great for measuring marketing results, customer acquisition and incrementality measurement are the best approaches for solving the measurement problems marketers are facing today.
With the right partner, incrementality measurement and customer acquisition can paint a clear picture of the return generated by your marketing efforts by activating knowledge and media at the individual level and ensuring the right customers are being reached at the most opportunistic moment. To be successful, customer acquisition and incrementality measurement strategies must be cost-effective, scalable, and predictable period to period, month-over-month.
The Zeta Marketing Platform helps marketers solve these challenges with independent technology that is data-intensive and identity-based to deliver against the higher standard of customer acquisition and incrementality measurement. By activating media, holding exposures, and measuring the impact of your marketing efforts at the individual level, your brand can see measurable results that drive business growth.
1. Media Mix Modeling
What is it?
Media mix modeling is one of the oldest forms of measurement and planning. This analytics solution enables marketers to measure the impact of their spending across various channels, with insight into how different elements contribute to a single goal (e.g. conversions or revenue). Media mix modeling can also be used to tackle common pain points in brand marketing including:
- Understanding impact across digital channels
- Determining the right mix of spend allocation that will drive the highest return on investment
- Predicting future channel performance based on optimized spend allocation
How does it work?
A media mix modeling analysis determines the relationship between a dependent variable (e.g. sales) and an independent variable (e.g. ad spend across channels). Oftentimes, media mix modeling is conducted at a geographical level. Let’s look at a hypothetical example for explanation: if a marketer spends more money on magazine advertising on the west coast, and more on TV advertising on the east coast, regional sales trends can be analyzed to understand the impact of these advertising efforts. Moreover, marketers can use media mix modeling to analyze how shifting spend from one publisher to another improves results.
What are its pros?
The main reason media mix modeling is so well renowned by marketers is that it gives insight into external factors that can determine the success of a campaign. Through long-term data collection, marketers can measure the impacts of seasonality, holidays, promotions, and more across both traditional and digital channels.
What are its cons?
Since media mix modeling is conducted at an aggregate level, it cannot account for recency, frequency, or sequences of media exposure at the individual level (e.g. impressions), all of which significantly impact effectiveness. Some other common flaws in using media mix modeling are infrequent reports, lack of insight on the brand and messaging, and failure to factor in the customer experience. For these reasons, most marketers also rely on attribution measurement.
2. Attribution Measurement
What is it?
Attribution measurement consists of various attribution models used to track engagements throughout the customer journey. The goal of attribution is the same as media mix modeling: to quantify the influence of media exposure on revenue. However, attribution analyzes campaign data at the event level (views, clicks, etc.) as consumers move down the sales funnel. This more accurate measurement further enhances the key benefits of programmatic marketing—feeding even better results into the always-on performance feedback loop within a marketing platform.
How does it work?
There are two key approaches to “attributing” a conversion:
- Single event-based attribution (more commonly known as last-touch attribution): It’s important to note that this is the least accurate form of attribution measurement. This is because assigning 100% of the credit back to the consumer’s last touchpoint does not distinguish between the search engine navigation and the advertising that influences people to enter the keywords into the empty search box.
- Multiple-event-based attribution (often called multi-touch attribution):
This form of attribution measurement enables greater precision and control. This approach assigns partial credit to each consumer touch before a conversion is made. Most modern marketers rely on self-learning algorithms that adapt the importance of exposure over time. Some DSPs even offer automatic budget reallocation based on the best-performing media to deliver better results by streamlining the feedback loop for their clients.
What are its pros?
Some pros of single-touch attribution are:
- Easy implementation and low cost
- Offers insight into what’s driving top-of-funnel conversions
- Straightforward insight into cost-per-lead metrics
Some pros of multiple-touch attribution are:
- Complete visibility of every touchpoint along the customer journey
- Provides fast-paced insights to understand consumer behavior shifts
- Allows for rapid optimization
What are its cons?
The challenge with single event-based attribution is obvious: only one channel receives credit. Let’s look at a hypothetical example for explanation: You’re watching a YouTube video and get served an ad from Walmart for new Beats headphones. You then Google search “new Beats headphones” to learn more about the product. The search results show a Walmart ad for the headphones—you click the ad and make the purchase. The Google ad ends up getting 100% of the credit for that purchase, although the buyer journey started on YouTube.
One of the challenges of multiple-touch attribution is that attribution modeling companies (e.g., C3, Visual IQ, Google/Adometry) vary in the results they provide (e.g., C3 found Adometry gave >360x more credit to Google). Also, consumer exposure is often tied to a single device and may undervalue omnichannel consumer experiences (unless a probabilistic identity graph that can measure activity across various devices for the same person or household at scale is applied).
3. Panel-based Measurement
What is it?
Panel-based measurement analyzes activity for a sample of consumers and households. Then, it parlays the impact across the entire campaign. This ensures a more accurate collection of information across all of the same consumer’s devices relative to attribution. Panel-based measurement also captures metrics associated with brand awareness, affinity, and purchase intent.
How does it work?
Panels provide consumers an opt-in method (such as surveys) to track deterministic consumer activity. This gives marketers a trade-off in scale for an increase in precision. The panel results are then projected onto the larger population exposed to the marketing campaigns. Some advanced DSPs now incorporate panels and can use survey results to optimize inflight budget allocations.
What are its pros?
Panel-based measurement is useful for gathering long-term audience insights. Moreover, panels provide a valid, representative, consistent, and comprehensive view of audiences so that marketers can understand what matters most to consumers.
What are its cons?
Given the small sample size of a panel, there may not be sufficient statistics to ensure the measured results will be repeatable. Moreover, consumers who opt into a panel may not be representative of a marketer’s target audience.
4. Customer Acquisition Cost (CAC)
What is it?
Customer acquisition cost (as the name suggests), is the cost of converting a prospect to a paying customer. It assesses the effectiveness of a marketer’s customer acquisition efforts by calculating the marketing cost per customer acquired during a specific timeframe (or specific marketing method). This can also help marketers determine which strategies are yielding the best results.
How does it work?
Customer acquisition cost (CAC) takes into account the entire customer journey, from the time a lead enters the funnel until they become a paying customer. The cost of acquiring a new customer varies from brand to brand and is dependent on the level of marketing efforts deployed for an individual:
Customer acquisition cost = Sales + Marketing Costs / # of New Customers
The idea is to keep your CAC as low as possible to see true business growth.
What are its pros?
The benefits of measuring marketing results with customer acquisition costs are vast. Some worth noting include:
- It helps marketers avoid wasting resources by helping determine which channels are most profitable and yield the highest results.
- Allows marketers to identify where adjustments can be made to the marketing process.
- Customer acquisition as a whole can lead to increased revenue.
What are its cons?
For less established companies, customer acquisition costs can be extremely high and therefore detrimental to growth. Finding the right balance between cost-effectiveness, scalability, and predictability when acquiring new customers can be a challenge, yet all are needed to properly measure the true impact of marketing results.
5. Incrementality Measurement
What is it?
Incrementality measurement is how marketers go about obtaining the true results of any marketing initiative. Using incrementality measurement, a marketer can determine the exact influence (i.e., the incremental benefit) in achieving their desired business outcome (e.g., more revenue). It measures the true lift of a campaign against its set goals.
How does it work?
Incrementality measurement allows marketers to determine whether a given campaign creates real value (e.g., the sale WOULD NOT have occurred without exposing the consumer to an ad) or apparent value (e.g., the sale WOULD occur whether or not the consumer saw an ad). In order for incrementality to be successful, marketers must determine what KPI (revenue, registrations, etc.) they want to measure for and measure the exposed audiences against the appropriate baseline.
What are its pros?
Some pros of incrementality measurement include:
- Easy to implement.
- All buyers are considered (even those that buy without being exposed to an ad).
- Ability to compare different bidding policies directly
- Ability to compute the percentage of additional buyers or sales.
What are its cons?
Most marketers eat hidden costs when it comes to incremental measurement. These costs revolve around the construction of a control group, and they’re costs that marketers don’t need to pay.
Moreover, noise from organic buyers is not removed when using incremental measurement and only onsite events (sells or visits) can be analyzed (it’s impossible to compare clicks or costs).
The Conclusion: Which Approach Solves The Marketing Measurement Problem the Best?
While in certain instances media mix modeling, attribution measurement, and panel-based measurement are great for measuring marketing results, customer acquisition and incrementality measurement are the best approaches for solving the measurement problems marketers are facing today.
With the right partner, incrementality measurement and customer acquisition can paint a clear picture of the return generated by your marketing efforts by activating knowledge and media at the individual level and ensuring the right customers are being reached at the most opportunistic moment. To be successful, customer acquisition and incrementality measurement strategies must be cost-effective, scalable, and predictable period to period, month-over-month.
The Zeta Marketing Platform helps marketers solve these challenges with independent technology that is data-intensive and identity-based to deliver against the higher standard of customer acquisition and incrementality measurement. By activating media, holding exposures, and measuring the impact of your marketing efforts at the individual level, your brand can see measurable results that drive business growth.
