Measure What Matters: Incrementality vs PPS vs MTA
by
Elaine Wei
Last updated:
Last updated:
Oct 3, 2024
Oct 3, 2024
Choosing the Right Measurement for Your Brand
Measurement is critical for marketers and businesses looking to understand how different channels shape customer journeys and impact conversions. However, selecting the right measurement solution can be challenging since it really depends on the business objectives and what matters the most for the business.
If the focus is on accuracy, you might prioritize a model that answers “What’s the result of this marketing effort?” For deeper insights, such as “Which creative performs best?” or “Which tactic has the lowest CAC?” you need a more granular analysis. Forecasting adds another layer, requiring brands to predict how changes—like increasing budgets for brand search—will affect sales. Finally, when it’s time to optimize, a test-and-learn approach demands models that offer actionable insights, enabling brands to fine-tune strategies based on real data.
In this post, we’ll explore three measurement solutions: post-purchase survey, incrementality test, and multi-touch attribution, to help you determine which one best suits your business.
Incrementality Test: Measuring True Impact
Incrementality testing focuses on the actual incremental lift generated by a specific marketing channel, identifying true conversions from those that would have happened regardless of the marketing activity. This approach provides more accurate insights and a clear answer to the question, "How much sales do I lose if I stop doing this?"
Geo-Incrementality Test: A proven testing approach for measuring incrementality is Geo-incrementality Testing, where geographic regions are split into test and control groups to measure incremental impact. While the process can be complex—from design and scheduling to post-test analysis—it can be automated and take as little as 21 days using expert platforms like WorkMagic. Brands using this method have achieved 50% higher ROAS and a 19% increase in Marketing Efficiency Ratio (MER).
Limitations: Incrementality testing, while powerful, can be resource-intensive and time-consuming. Running controlled tests requires careful planning and coordination, and the results can sometimes be influenced by external factors such as seasonality or competitive activity.
Post-Purchase Survey: Capturing All Channels
Post-purchase surveys rely on direct customer feedback to determine which channels influence purchase decisions. This method can capture the impact from all channels, including those that are hard to track, such as podcasts, influencers, and word-of-mouth.
HDYHAU surveys: One of the most effective ways to gather insights is by integrating "How Did You Hear About Us" (HDYHAU) survey questions into customer touchpoints like emails, text messages, in-store forms, or post-purchase prompts on eCommerce platforms. The survey helps brands gain insights into customer behavior and order-level insights. According to research from Fairing, a post-purchase survey platform, businesses using HDYHAU surveys have seen over a 20% increase in marketing attribution.
Limitations: While post-purchase surveys offer a holistic view of all channels, it relies on customer memory and sample size, which can lead to biased or inaccurate data. Respondents may not clearly recall which specific channel influenced their decision, and mapping their answers to actual marketing data (e.g., “Friends & Family” vs. affiliate or organic) can be challenging.
Multi-Touch Attribution: Distributing Credit Across User Journey
Multi-touch attribution (MTA) distributes credit to multiple touchpoints along a user’s journey, helping brands understand how different ads or channels contribute to a conversion. MTA can answer questions about the granular performance of your advertising like “Which ads brought me 10k orders this week?”
Data-driven Attribution: There are several different MTA models, such as first-click, last-click, and any-click, etc. Data-driven attribution (DDA) is a signal-enhanced attribution model where machine learning algorithms evaluate all previous user paths and give credit for conversions based on how people engage across touchpoints. DDA helps brands gain deeper insights into channel performance, especially when they're not able to run incrementality tests.
Limitations: The biggest challenge for MTA is signal loss, especially in an increasingly privacy-focused environment where third-party cookies are becoming phased out and user tracking is more restricted. Furthermore, determining which model provides the most accurate representation of the customer journey can be challenging, leading to discrepancies in how credit is assigned.
Case Study:
To compare the results of different solutions, we conducted an in-depth analysis over months, using data from a real brand with 9-figure annual GMV. Here is what we learned:
Meta Ad Platform reported that 25% of total orders were generated through Meta Ads, counting all click-based conversions and view-through conversions.
Incrementality test revealed that 12% of total orders would not have happen without running Meta ads, indicating a 108% overstatement in Meta's reported impact.
Survey results aligned with the findings from the incrementality test, showing 9% of the brand's orders were driven by Meta ads.
Using any-click attribution, one of the traditional MTA models, we identified that click-based conversions contributed 6% of total orders.
Conclusion & Takeaways
In today’s omnichannel world, brands need to leverage insights from a comprehensive measurement toolkit to gain a complete view of the customer journey and make more informed decisions. Survey responses are a great way to capture order-level marketing data, particularly for channels that are traditionally harder to track. Incrementality tests reveal the true value of marketing efforts that allow brands to increase revenue and marketing efficiency through better allocation of budgets and resources.
WorkMagic, the ultimate platform for measurement solutions, has recently launched an integration with Fairing. By calibrating attribution models with incrementality test results and survey data, we provide brands with both accuracy and granularity to understand and improve marketing performance. As a special offer, Fairing and WorkMagic clients can now enjoy a 30-day free trial, a complimentary incrementality test, and exclusive measurement consultations with the former Head of Growth at TikTok Ads! Get your free slot today!
Choosing the Right Measurement for Your Brand
Measurement is critical for marketers and businesses looking to understand how different channels shape customer journeys and impact conversions. However, selecting the right measurement solution can be challenging since it really depends on the business objectives and what matters the most for the business.
If the focus is on accuracy, you might prioritize a model that answers “What’s the result of this marketing effort?” For deeper insights, such as “Which creative performs best?” or “Which tactic has the lowest CAC?” you need a more granular analysis. Forecasting adds another layer, requiring brands to predict how changes—like increasing budgets for brand search—will affect sales. Finally, when it’s time to optimize, a test-and-learn approach demands models that offer actionable insights, enabling brands to fine-tune strategies based on real data.
In this post, we’ll explore three measurement solutions: post-purchase survey, incrementality test, and multi-touch attribution, to help you determine which one best suits your business.
Incrementality Test: Measuring True Impact
Incrementality testing focuses on the actual incremental lift generated by a specific marketing channel, identifying true conversions from those that would have happened regardless of the marketing activity. This approach provides more accurate insights and a clear answer to the question, "How much sales do I lose if I stop doing this?"
Geo-Incrementality Test: A proven testing approach for measuring incrementality is Geo-incrementality Testing, where geographic regions are split into test and control groups to measure incremental impact. While the process can be complex—from design and scheduling to post-test analysis—it can be automated and take as little as 21 days using expert platforms like WorkMagic. Brands using this method have achieved 50% higher ROAS and a 19% increase in Marketing Efficiency Ratio (MER).
Limitations: Incrementality testing, while powerful, can be resource-intensive and time-consuming. Running controlled tests requires careful planning and coordination, and the results can sometimes be influenced by external factors such as seasonality or competitive activity.
Post-Purchase Survey: Capturing All Channels
Post-purchase surveys rely on direct customer feedback to determine which channels influence purchase decisions. This method can capture the impact from all channels, including those that are hard to track, such as podcasts, influencers, and word-of-mouth.
HDYHAU surveys: One of the most effective ways to gather insights is by integrating "How Did You Hear About Us" (HDYHAU) survey questions into customer touchpoints like emails, text messages, in-store forms, or post-purchase prompts on eCommerce platforms. The survey helps brands gain insights into customer behavior and order-level insights. According to research from Fairing, a post-purchase survey platform, businesses using HDYHAU surveys have seen over a 20% increase in marketing attribution.
Limitations: While post-purchase surveys offer a holistic view of all channels, it relies on customer memory and sample size, which can lead to biased or inaccurate data. Respondents may not clearly recall which specific channel influenced their decision, and mapping their answers to actual marketing data (e.g., “Friends & Family” vs. affiliate or organic) can be challenging.
Multi-Touch Attribution: Distributing Credit Across User Journey
Multi-touch attribution (MTA) distributes credit to multiple touchpoints along a user’s journey, helping brands understand how different ads or channels contribute to a conversion. MTA can answer questions about the granular performance of your advertising like “Which ads brought me 10k orders this week?”
Data-driven Attribution: There are several different MTA models, such as first-click, last-click, and any-click, etc. Data-driven attribution (DDA) is a signal-enhanced attribution model where machine learning algorithms evaluate all previous user paths and give credit for conversions based on how people engage across touchpoints. DDA helps brands gain deeper insights into channel performance, especially when they're not able to run incrementality tests.
Limitations: The biggest challenge for MTA is signal loss, especially in an increasingly privacy-focused environment where third-party cookies are becoming phased out and user tracking is more restricted. Furthermore, determining which model provides the most accurate representation of the customer journey can be challenging, leading to discrepancies in how credit is assigned.
Case Study:
To compare the results of different solutions, we conducted an in-depth analysis over months, using data from a real brand with 9-figure annual GMV. Here is what we learned:
Meta Ad Platform reported that 25% of total orders were generated through Meta Ads, counting all click-based conversions and view-through conversions.
Incrementality test revealed that 12% of total orders would not have happen without running Meta ads, indicating a 108% overstatement in Meta's reported impact.
Survey results aligned with the findings from the incrementality test, showing 9% of the brand's orders were driven by Meta ads.
Using any-click attribution, one of the traditional MTA models, we identified that click-based conversions contributed 6% of total orders.
Conclusion & Takeaways
In today’s omnichannel world, brands need to leverage insights from a comprehensive measurement toolkit to gain a complete view of the customer journey and make more informed decisions. Survey responses are a great way to capture order-level marketing data, particularly for channels that are traditionally harder to track. Incrementality tests reveal the true value of marketing efforts that allow brands to increase revenue and marketing efficiency through better allocation of budgets and resources.
WorkMagic, the ultimate platform for measurement solutions, has recently launched an integration with Fairing. By calibrating attribution models with incrementality test results and survey data, we provide brands with both accuracy and granularity to understand and improve marketing performance. As a special offer, Fairing and WorkMagic clients can now enjoy a 30-day free trial, a complimentary incrementality test, and exclusive measurement consultations with the former Head of Growth at TikTok Ads! Get your free slot today!
Choosing the Right Measurement for Your Brand
Measurement is critical for marketers and businesses looking to understand how different channels shape customer journeys and impact conversions. However, selecting the right measurement solution can be challenging since it really depends on the business objectives and what matters the most for the business.
If the focus is on accuracy, you might prioritize a model that answers “What’s the result of this marketing effort?” For deeper insights, such as “Which creative performs best?” or “Which tactic has the lowest CAC?” you need a more granular analysis. Forecasting adds another layer, requiring brands to predict how changes—like increasing budgets for brand search—will affect sales. Finally, when it’s time to optimize, a test-and-learn approach demands models that offer actionable insights, enabling brands to fine-tune strategies based on real data.
In this post, we’ll explore three measurement solutions: post-purchase survey, incrementality test, and multi-touch attribution, to help you determine which one best suits your business.
Incrementality Test: Measuring True Impact
Incrementality testing focuses on the actual incremental lift generated by a specific marketing channel, identifying true conversions from those that would have happened regardless of the marketing activity. This approach provides more accurate insights and a clear answer to the question, "How much sales do I lose if I stop doing this?"
Geo-Incrementality Test: A proven testing approach for measuring incrementality is Geo-incrementality Testing, where geographic regions are split into test and control groups to measure incremental impact. While the process can be complex—from design and scheduling to post-test analysis—it can be automated and take as little as 21 days using expert platforms like WorkMagic. Brands using this method have achieved 50% higher ROAS and a 19% increase in Marketing Efficiency Ratio (MER).
Limitations: Incrementality testing, while powerful, can be resource-intensive and time-consuming. Running controlled tests requires careful planning and coordination, and the results can sometimes be influenced by external factors such as seasonality or competitive activity.
Post-Purchase Survey: Capturing All Channels
Post-purchase surveys rely on direct customer feedback to determine which channels influence purchase decisions. This method can capture the impact from all channels, including those that are hard to track, such as podcasts, influencers, and word-of-mouth.
HDYHAU surveys: One of the most effective ways to gather insights is by integrating "How Did You Hear About Us" (HDYHAU) survey questions into customer touchpoints like emails, text messages, in-store forms, or post-purchase prompts on eCommerce platforms. The survey helps brands gain insights into customer behavior and order-level insights. According to research from Fairing, a post-purchase survey platform, businesses using HDYHAU surveys have seen over a 20% increase in marketing attribution.
Limitations: While post-purchase surveys offer a holistic view of all channels, it relies on customer memory and sample size, which can lead to biased or inaccurate data. Respondents may not clearly recall which specific channel influenced their decision, and mapping their answers to actual marketing data (e.g., “Friends & Family” vs. affiliate or organic) can be challenging.
Multi-Touch Attribution: Distributing Credit Across User Journey
Multi-touch attribution (MTA) distributes credit to multiple touchpoints along a user’s journey, helping brands understand how different ads or channels contribute to a conversion. MTA can answer questions about the granular performance of your advertising like “Which ads brought me 10k orders this week?”
Data-driven Attribution: There are several different MTA models, such as first-click, last-click, and any-click, etc. Data-driven attribution (DDA) is a signal-enhanced attribution model where machine learning algorithms evaluate all previous user paths and give credit for conversions based on how people engage across touchpoints. DDA helps brands gain deeper insights into channel performance, especially when they're not able to run incrementality tests.
Limitations: The biggest challenge for MTA is signal loss, especially in an increasingly privacy-focused environment where third-party cookies are becoming phased out and user tracking is more restricted. Furthermore, determining which model provides the most accurate representation of the customer journey can be challenging, leading to discrepancies in how credit is assigned.
Case Study:
To compare the results of different solutions, we conducted an in-depth analysis over months, using data from a real brand with 9-figure annual GMV. Here is what we learned:
Meta Ad Platform reported that 25% of total orders were generated through Meta Ads, counting all click-based conversions and view-through conversions.
Incrementality test revealed that 12% of total orders would not have happen without running Meta ads, indicating a 108% overstatement in Meta's reported impact.
Survey results aligned with the findings from the incrementality test, showing 9% of the brand's orders were driven by Meta ads.
Using any-click attribution, one of the traditional MTA models, we identified that click-based conversions contributed 6% of total orders.
Conclusion & Takeaways
In today’s omnichannel world, brands need to leverage insights from a comprehensive measurement toolkit to gain a complete view of the customer journey and make more informed decisions. Survey responses are a great way to capture order-level marketing data, particularly for channels that are traditionally harder to track. Incrementality tests reveal the true value of marketing efforts that allow brands to increase revenue and marketing efficiency through better allocation of budgets and resources.
WorkMagic, the ultimate platform for measurement solutions, has recently launched an integration with Fairing. By calibrating attribution models with incrementality test results and survey data, we provide brands with both accuracy and granularity to understand and improve marketing performance. As a special offer, Fairing and WorkMagic clients can now enjoy a 30-day free trial, a complimentary incrementality test, and exclusive measurement consultations with the former Head of Growth at TikTok Ads! Get your free slot today!