Press    What is Cross-Channel Attribution and Why is it Difficult?

Original Publisher

The goal of cross-platform or cross-channel attribution is to gain visibility into performance across the entire media mix and reveal how each marketing channel, tactic, or campaign contributes to conversions and sales.

What are the best cross-channel attribution Methods?

Common methods for cross-channel attribution include multi-touch attribution (MTA), media mix modeling (MMM), and incrementality measurement.

Multi-touch attribution collects individual, user-level data for addressable (trackable) media and conversion events to determine the impact each media event has on a customers’ path to conversion. Because MTA requires tracking and connecting all media at the user level, it does not account for non-addressable media, like print, radio, and traditional (linear) TV, which cannot be tracked to individuals.

Media mix modeling collects aggregated data from marketing and non-marketing sources over a multi-year historical period, also factoring in external influences such as seasonality, economic data, weather, and promotions. The data is then used to develop a demand model which quantifies the historical contribution of each marketing and non-marketing input to a business outcome, like sales or conversions.

Using MTA and MMM for cross-channel attribution has proven to be difficult for reasons including:

  • Many platforms like Amazon and Facebook are walled gardens and inaccessible to third-party tracking of impressions.
  • Identity resolution across media platforms is quite low.
  • Cross-device tracking is difficult and match rates are extremely low.
  • Instrumenting a tracking infrastructure by a third-party measurement provider has proved to be fraught with breakage and data leakage.
  • Both are extremely time-consuming to implement and maintain and require experience in data science and analytics.

Incrementality testing is an alternative approach to cross-channel attribution that isn’t plagued by all the issues above. Deployed within the publisher platforms themselves, incrementality experiments provide marketers with a true understanding of the incremental contribution of each marketing channel down to the most granular level.

While designing scientifically sound experiments for each channel, campaign and tactic can be a complex process, Measured has you coverd. Our proven experiments are meticulously designed for the unique requirements, operations, and data sets of each platform – and they are continuously updated to adapt to new regulations and changes.

With Measured, you can easily run incrementality measurement and testing on 70+ media publisher platforms. Utilizing our API integrations with media platforms, you get a cross-channel view of your marketing mix in less than 24 hours.

Video: Landing a source of truth cross-channel media reporting dashboard

 

 

 

While designing scientifically sound experiments for each channel, campaign and tactic can be a complex process, Measured has you covered.

The goal of cross-platform or cross-channel attribution is to gain visibility into performance across the entire media mix and reveal how each marketing channel, tactic, or campaign contributes to conversions and sales.

What are the best cross-channel attribution Methods?

Common methods for cross-channel attribution include multi-touch attribution (MTA), media mix modeling (MMM), and incrementality measurement.

Multi-touch attribution collects individual, user-level data for addressable (trackable) media and conversion events to determine the impact each media event has on a customers’ path to conversion. Because MTA requires tracking and connecting all media at the user level, it does not account for non-addressable media, like print, radio, and traditional (linear) TV, which cannot be tracked to individuals.

Media mix modeling collects aggregated data from marketing and non-marketing sources over a multi-year historical period, also factoring in external influences such as seasonality, economic data, weather, and promotions. The data is then used to develop a demand model which quantifies the historical contribution of each marketing and non-marketing input to a business outcome, like sales or conversions.

Using MTA and MMM for cross-channel attribution has proven to be difficult for reasons including:

  • Many platforms like Amazon and Facebook are walled gardens and inaccessible to third-party tracking of impressions.
  • Identity resolution across media platforms is quite low.
  • Cross-device tracking is difficult and match rates are extremely low.
  • Instrumenting a tracking infrastructure by a third-party measurement provider has proved to be fraught with breakage and data leakage.
  • Both are extremely time-consuming to implement and maintain and require experience in data science and analytics.

Incrementality testing is an alternative approach to cross-channel attribution that isn’t plagued by all the issues above. Deployed within the publisher platforms themselves, incrementality experiments provide marketers with a true understanding of the incremental contribution of each marketing channel down to the most granular level.

While designing scientifically sound experiments for each channel, campaign and tactic can be a complex process, Measured has you coverd. Our proven experiments are meticulously designed for the unique requirements, operations, and data sets of each platform – and they are continuously updated to adapt to new regulations and changes.

With Measured, you can easily run incrementality measurement and testing on 70+ media publisher platforms. Utilizing our API integrations with media platforms, you get a cross-channel view of your marketing mix in less than 24 hours.

Video: Landing a source of truth cross-channel media reporting dashboard

 

 

Original Publisher

 

While designing scientifically sound experiments for each channel, campaign and tactic can be a complex process, Measured has you covered.

Press    What is Marketing Attribution Software?

Original Publisher

Over the years, various attribution techniques have been developed and deployed as Software-as-a-Service (Saas) applications that marketers have come to rely on to measure and optimize advertising. This class of software has come to be known commonly as attribution software.

What is the Attribution Problem?

Marketers, especially digital marketers, have and still do heavily rely on click path data to measure media performance. Oftentimes, the campaign or media channel that drove the last click before a purchase receives all the credit. Typically these are very low funnel channels like SEM PPC, affiliate, and retargeting, which has led to overinvestment in these channels. By overvaluing those channels, marketers are ignoring or undervaluing other prospecting channels that may have contributed to the sale along the path to conversion. To solve this, attribution software companies have created multiple solutions to assign proper credit to the various media channels in a marketing portfolio.

What is Attribution Tracking and What are Attribution Models?

Attribution tracking can be performed multiple ways. One method is to use tools like Google Analytics, Segment, or one of the many open-source tracking pixels available. Tracking a single user across multiple platforms/publishers and marketing channels for the purposes of applying fractional credit to the marketing touch-points the user was exposed to, is commonly referred to as multi-touch attribution (MTA). Essentially you’ll be tracking clicks, not impressions. In most cases, you will not be able to capture impression-level data and pipe it into your models, as many publishers are walled gardens do not share it. Impression views are a major portion of the overall picture and this lack of visibility is a big detractor to using MTA.

Enterprise MTA platforms such as Neustar MarketShare, or Nielson VIQ set up the tracking for their customers. The methods they use to deploy their tracking services across your media varies, but because they rely on their own proprietary tracking infrastructures and not the platform’s/publisher’s tracking, it can be prone to breakage and data reconciliation issues.

Once tracking is set up you’ll need to consider which type of model you’ll use. Attribution modeling is a method for assigning credit to advertising intended to drive sales. The most common and simplistic approach for attribution is called last-click attribution. This method offers 100% credit to the last click in the user’s path. In general, last click attribution is considered overly simplistic, over credits lower funnel tactics (such as retargeting and affiliates) and is used in a limited tactical way by marketers for making decisions.

First click attribution gives credit to the first media touch point that delivered the visitor to the website and delivered a conversion, or sale. This is probably the least used method for attribution, but can be helpful to show which top of funnel campaigns are more effective than others.

Some common multi-touch attribution Models are:

  • Rules Based Weighted Distribution – ex) 60% first touch, 30% last touch, 10% other touchpoints – This puts the majority of the weight on the first and last touches. The problem with this model is you still must decide what you want the weights to be for each touch along the path to conversion. It requires a lot of diligence, review and updating often to keep it close to a version of the truth.

  • Rules Based Even Distribution – Credit is divided up equally across all touchpoints in the path to a conversion. It’s not a common model and is less accurate than weighted or Algorithmic.
  • Algorithmic – This model uses machine learning to objectively determine the impact of marketing stimuli along a consumer’s path to conversion. Building this type of model is extremely time consuming and labor intensive. It is also fraught with data breakage/leakage.

  • Last Touch Attribution Model – In the last-touch attribution model, the last touchpoint receives 100% of the credit for the sales conversion.

  • First Touch Attribution ModelIn the first-touch attribution model, the first touchpoint receives 100% of the credit for the sales conversion.

  • Time Decay Model – In the time-decay attribution model, the touchpoints closest in time to the sales conversion get the most credit. In this case, the last four touch points before the sales conversion receive the most credit, whereas the others receive significantly less.

 

What is an Attribution Tool?

The primary goal of attribution tools (or MTA tools) is to provide marketers with an out-of-the box, or semi-customized attribution tracking & modeling to help marketers understand how much credit should be given to each marketing touch-point. There are free or cheap attribution tools and software available like Google Attribution and Rockerbox. These entry level tools will provide a better attributed view of your marketing than using last touch. However, there are severe drawbacks to these tools. a) They are click based so if your site does not or cannot drop a cookie, you won’t see that person. b) Upper funnel impression based channels like YouTube, TV, Display and others are very difficult to account for. And c) walled ecosystems like Facebook, do not provide access to user or impression level data.

Neustar MarketShare provides an enterprise level multi-touch attribution platform which encompasses a full suite of technology services designed to track, model and report against user level marketing data and provides consulting services to help interpret and use the data. While their offering is more comprehensive than the providers mentioned above, they are still subject to the same limitations. Where Neustar Marketshare does excel is in their Marketing Mix Modeling (MMM) and consulting practice. See What is Marketing Mix Modeling? for more on MMM.

Measured Marketing Attribution & Incrementality Measurement

For making more impactful decisions rooted in incrementality measurement, proven to be the most reliable and accurate way to measure marketing contribution, we have developed advanced methods to account for the limitations of MTA models.

One example of this is the ability to accurately measure marketing contribution within walled gardens because many of these platforms enable experimentation deliberately or coincidentally. This is fundamentally different than MTA. In platforms like Facebook it is possible to select and target audiences in randomized ways but target them differentially. This enables us to design experiments and test audiences for different marketing treatments. Incrementality measurement is a direct substitute for MTA and is very complimentary to MMM.

Measured’s advanced cross-channel measurement provides true incrementality measurement across all your media channels where you can make decisions based on proper attribution. Learn More!

 

Multi-Touch Attribution tools are now "click fed," hence unable to measure impression based channels with accuracy and rendering it ineffective for omni-channel media portfolios.

Over the years, various attribution techniques have been developed and deployed as Software-as-a-Service (Saas) applications that marketers have come to rely on to measure and optimize advertising. This class of software has come to be known commonly as attribution software.

What is the Attribution Problem?

Marketers, especially digital marketers, have and still do heavily rely on click path data to measure media performance. Oftentimes, the campaign or media channel that drove the last click before a purchase receives all the credit. Typically these are very low funnel channels like SEM PPC, affiliate, and retargeting, which has led to overinvestment in these channels. By overvaluing those channels, marketers are ignoring or undervaluing other prospecting channels that may have contributed to the sale along the path to conversion. To solve this, attribution software companies have created multiple solutions to assign proper credit to the various media channels in a marketing portfolio.

What is Attribution Tracking and What are Attribution Models?

Attribution tracking can be performed multiple ways. One method is to use tools like Google Analytics, Segment, or one of the many open-source tracking pixels available. Tracking a single user across multiple platforms/publishers and marketing channels for the purposes of applying fractional credit to the marketing touch-points the user was exposed to, is commonly referred to as multi-touch attribution (MTA). Essentially you’ll be tracking clicks, not impressions. In most cases, you will not be able to capture impression-level data and pipe it into your models, as many publishers are walled gardens do not share it. Impression views are a major portion of the overall picture and this lack of visibility is a big detractor to using MTA.

Enterprise MTA platforms such as Neustar MarketShare, or Nielson VIQ set up the tracking for their customers. The methods they use to deploy their tracking services across your media varies, but because they rely on their own proprietary tracking infrastructures and not the platform’s/publisher’s tracking, it can be prone to breakage and data reconciliation issues.

Once tracking is set up you’ll need to consider which type of model you’ll use. Attribution modeling is a method for assigning credit to advertising intended to drive sales. The most common and simplistic approach for attribution is called last-click attribution. This method offers 100% credit to the last click in the user’s path. In general, last click attribution is considered overly simplistic, over credits lower funnel tactics (such as retargeting and affiliates) and is used in a limited tactical way by marketers for making decisions.

First click attribution gives credit to the first media touch point that delivered the visitor to the website and delivered a conversion, or sale. This is probably the least used method for attribution, but can be helpful to show which top of funnel campaigns are more effective than others.

Some common multi-touch attribution Models are:

  • Rules Based Weighted Distribution – ex) 60% first touch, 30% last touch, 10% other touchpoints – This puts the majority of the weight on the first and last touches. The problem with this model is you still must decide what you want the weights to be for each touch along the path to conversion. It requires a lot of diligence, review and updating often to keep it close to a version of the truth.

  • Rules Based Even Distribution – Credit is divided up equally across all touchpoints in the path to a conversion. It’s not a common model and is less accurate than weighted or Algorithmic.
  • Algorithmic – This model uses machine learning to objectively determine the impact of marketing stimuli along a consumer’s path to conversion. Building this type of model is extremely time consuming and labor intensive. It is also fraught with data breakage/leakage.

  • Last Touch Attribution Model – In the last-touch attribution model, the last touchpoint receives 100% of the credit for the sales conversion.

  • First Touch Attribution ModelIn the first-touch attribution model, the first touchpoint receives 100% of the credit for the sales conversion.

  • Time Decay Model – In the time-decay attribution model, the touchpoints closest in time to the sales conversion get the most credit. In this case, the last four touch points before the sales conversion receive the most credit, whereas the others receive significantly less.

 

What is an Attribution Tool?

The primary goal of attribution tools (or MTA tools) is to provide marketers with an out-of-the box, or semi-customized attribution tracking & modeling to help marketers understand how much credit should be given to each marketing touch-point. There are free or cheap attribution tools and software available like Google Attribution and Rockerbox. These entry level tools will provide a better attributed view of your marketing than using last touch. However, there are severe drawbacks to these tools. a) They are click based so if your site does not or cannot drop a cookie, you won’t see that person. b) Upper funnel impression based channels like YouTube, TV, Display and others are very difficult to account for. And c) walled ecosystems like Facebook, do not provide access to user or impression level data.

Neustar MarketShare provides an enterprise level multi-touch attribution platform which encompasses a full suite of technology services designed to track, model and report against user level marketing data and provides consulting services to help interpret and use the data. While their offering is more comprehensive than the providers mentioned above, they are still subject to the same limitations. Where Neustar Marketshare does excel is in their Marketing Mix Modeling (MMM) and consulting practice. See What is Marketing Mix Modeling? for more on MMM.

Measured Marketing Attribution & Incrementality Measurement

For making more impactful decisions rooted in incrementality measurement, proven to be the most reliable and accurate way to measure marketing contribution, we have developed advanced methods to account for the limitations of MTA models.

One example of this is the ability to accurately measure marketing contribution within walled gardens because many of these platforms enable experimentation deliberately or coincidentally. This is fundamentally different than MTA. In platforms like Facebook it is possible to select and target audiences in randomized ways but target them differentially. This enables us to design experiments and test audiences for different marketing treatments. Incrementality measurement is a direct substitute for MTA and is very complimentary to MMM.

Measured’s advanced cross-channel measurement provides true incrementality measurement across all your media channels where you can make decisions based on proper attribution. Learn More!

Original Publisher

 

Multi-Touch Attribution tools are now "click fed," hence unable to measure impression based channels with accuracy and rendering it ineffective for omni-channel media portfolios.

Press    What is Multi-Touch Attribution (MTA)?

Original Publisher

Multi-touch attribution (MTA) collects individual, user-level data for addressable (trackable) media and conversion events to determine the impact each media event has on a customers’ path to conversion. Because MTA requires tracking and connecting all media at the user level, it does not account for non-addressable media, like print, radio, and traditional (linear) TV, which cannot be tracked to individuals.

Implementing an effective multi-touch attribution model is a complex and difficult process, but can deliver results far superior to first or last click reporting, especially if the media mix is largely made up of addressable media. 

How is multi-touch attribution implemented?

User-level tracking is typically performed using Google Analytics, tools from data tracking vendors, or one of the many open-source tracking pixels available. Theoretically, the tracking data is then used to create detailed user click paths that map out the media touchpoints a customer encountered leading up to a conversion. 

illustration of marketing funnel multi touch attribution including YouTube, Snapchat, Pinterest, Google Search, Facebook, Instagram

Capturing impression-level data and piping it into attribution models can be a challenge because more and more publishers and platforms have become walled gardens and refuse to share user data. Impression views are an important part of the overall picture and this lack of visibility has been the biggest detractor to using MTA. Access to this critical data will become even more restricted with Google’s recent decision to disable cookies and new privacy-driven policies associated with Apple iOS 14 and Facebook attribution.

what is the difference between a wholesome attribution model and a fractional attribution model?

A wholesome attribution model assigns all the credit to the first touch or the last touch. A fractional attribution model spreads credit across all marketing touchpoints in the consumer journey leading to a conversion event.

What types of attribution models are there?

The most common multi-touch attribution models are:

  • Rules-Based Weighted Distribution – Assigns weight percentages to first-touch and last-touch, then the third percentage to all the touchpoints in between. (Ex: 60% first-touch, 30% last-touch, 10% other) This model requires diligence, ongoing review, and frequent revisions to the weights to keep it close to a version of the truth.

  • Algorithmic – Uses machine learning to objectively determine the impact of marketing events along the path to conversion. Building this type of model is extremely time-consuming and labor-intensive. It is also fraught with data breakage and lack of impression visibility in many major marketing channels.

  • Rules-Based Even Distribution – Divides credit up equally across all touchpoints in the path to a conversion. While much simpler to calculate, this model is less common and less accurate than weighted or algorithmic models.
  • Last Touch Attribution Model – In the last-touch attribution model, the last touchpoint receives 100% of the credit for the sales conversion. 

  • First Touch Attribution Model – In the first-touch attribution model, the first touchpoint receives 100% of the credit for the sales conversion.

  • Time Decay Attribution Model – In the time-decay attribution model, the touchpoints closest in time to the sales conversion get the most credit. In this case, the last four touch points before the sales conversion receive the most credit, whereas the others receive significantly less.

 

Can MTA be used for forecasting?

MTA models estimate propensity to convert rather than demand and are therefore not directly applicable to forecasting. While demand curves can be inferred from MTA models, they typically do not have much validity at the campaign level and only inform sub-channel tactical decisions without forecasting or strategic decision-making support.

What is an attribution platform or attribution solution provider?

Rather than take on the enormous task of building an MTA system in-house, many brands choose to implement an attribution platform, marketing technology software that captures user-level events across marketing channels and applies an algorithmic model to assign appropriate credit to the media touchpoints. There are also “full-service” MTA providers that instrument the tracking of the user-level events across media publishers and platforms, apply their own proprietary attribution models, and deliver a bespoke reporting tool. 

Is multi-touch attribution right for me?

Whether the system is built in-house or an attribution provider is brought in, MTA is an extremely difficult exercise to land. With each new channel added to the digital marketing mix comes another level of added complexity. MTA can take months to implement. It’s expensive. It’s complicated. And now, without user-level tracking, it’s not likely to survive.

Anticipating the degradation of ID-tracking, Measured bet on incrementality testing and cohort-analytics as the future of measurement. It’s an effective solution to the growing conflict between performance measurement and privacy because it is not plagued by user-level data challenges encountered by MTA.  

Deployed within the publisher platforms themselves, Measured experiments provide marketers with a true understanding of the incremental contribution of each marketing channel down to the most granular level. In addition, incrementality measurement is quicker than MTA to set up, can be used for scale testing and forecasting, and measures the impact of both addressable and non-addressable media. 

Compare Measured to platform reporting, MTA & MMM

Measured

Measurement – Other

Measured Advantage

Incrementality

Platforms

MTA

MMM

General

Neutral & Independent

Green Check Mark

Grey Check Mark

Grey Check Mark

Trusted Measurement

Measurement

Causal Incremental Contribution

Green Check Mark

Grey Check Mark

Grey Check Mark

Productized Experiments

Scale Testing

Green Check Mark

Identify Saturation Curves

Granular Insights

Green Check Mark

Grey Check Mark

Future Proof

Comprehensive & Cross Channel

Green Check Mark

Grey Check Mark

Depth of Measurement

Walled Garden Support

Green Check Mark

Grey Check Mark

Grey Check Mark

Comprehensive

Transparent

Green Check Mark

Transparency = Trust

Decisions

Tactical Decisions

Green Check Mark

Grey Check Mark

Daily & Weekly Insights

Strategic Planning

Green Check Mark

Grey Check Mark

Bottom Up Forecasting

Timely Insights

Green Check Mark

Grey Check Mark

On Time, Reliable

Data Management

Purpose Built for Marketing Analytics

Green Check Mark

Analytics Ready

Data Quality

Green Check Mark

Grey Check Mark

Reconciled to Source of Truth Platforms

 

Multi-touch attribution (MTA) collects individual, user-level data for addressable (trackable) media and conversion events to determine the impact each media event has on a customers’ path to conversion. Because MTA requires tracking and connecting all media at the user level, it does not account for non-addressable media, like print, radio, and traditional (linear) TV, which cannot be tracked to individuals.

Implementing an effective multi-touch attribution model is a complex and difficult process, but can deliver results far superior to first or last click reporting, especially if the media mix is largely made up of addressable media. 

How is multi-touch attribution implemented?

User-level tracking is typically performed using Google Analytics, tools from data tracking vendors, or one of the many open-source tracking pixels available. Theoretically, the tracking data is then used to create detailed user click paths that map out the media touchpoints a customer encountered leading up to a conversion. 

illustration of marketing funnel multi touch attribution including YouTube, Snapchat, Pinterest, Google Search, Facebook, Instagram

Capturing impression-level data and piping it into attribution models can be a challenge because more and more publishers and platforms have become walled gardens and refuse to share user data. Impression views are an important part of the overall picture and this lack of visibility has been the biggest detractor to using MTA. Access to this critical data will become even more restricted with Google’s recent decision to disable cookies and new privacy-driven policies associated with Apple iOS 14 and Facebook attribution.

what is the difference between a wholesome attribution model and a fractional attribution model?

A wholesome attribution model assigns all the credit to the first touch or the last touch. A fractional attribution model spreads credit across all marketing touchpoints in the consumer journey leading to a conversion event.

What types of attribution models are there?

The most common multi-touch attribution models are:

  • Rules-Based Weighted Distribution – Assigns weight percentages to first-touch and last-touch, then the third percentage to all the touchpoints in between. (Ex: 60% first-touch, 30% last-touch, 10% other) This model requires diligence, ongoing review, and frequent revisions to the weights to keep it close to a version of the truth.

  • Algorithmic – Uses machine learning to objectively determine the impact of marketing events along the path to conversion. Building this type of model is extremely time-consuming and labor-intensive. It is also fraught with data breakage and lack of impression visibility in many major marketing channels.

  • Rules-Based Even Distribution – Divides credit up equally across all touchpoints in the path to a conversion. While much simpler to calculate, this model is less common and less accurate than weighted or algorithmic models.
  • Last Touch Attribution Model – In the last-touch attribution model, the last touchpoint receives 100% of the credit for the sales conversion. 

  • First Touch Attribution Model – In the first-touch attribution model, the first touchpoint receives 100% of the credit for the sales conversion.

  • Time Decay Attribution Model – In the time-decay attribution model, the touchpoints closest in time to the sales conversion get the most credit. In this case, the last four touch points before the sales conversion receive the most credit, whereas the others receive significantly less.

 

Can MTA be used for forecasting?

MTA models estimate propensity to convert rather than demand and are therefore not directly applicable to forecasting. While demand curves can be inferred from MTA models, they typically do not have much validity at the campaign level and only inform sub-channel tactical decisions without forecasting or strategic decision-making support.

What is an attribution platform or attribution solution provider?

Rather than take on the enormous task of building an MTA system in-house, many brands choose to implement an attribution platform, marketing technology software that captures user-level events across marketing channels and applies an algorithmic model to assign appropriate credit to the media touchpoints. There are also “full-service” MTA providers that instrument the tracking of the user-level events across media publishers and platforms, apply their own proprietary attribution models, and deliver a bespoke reporting tool. 

Is multi-touch attribution right for me?

Whether the system is built in-house or an attribution provider is brought in, MTA is an extremely difficult exercise to land. With each new channel added to the digital marketing mix comes another level of added complexity. MTA can take months to implement. It’s expensive. It’s complicated. And now, without user-level tracking, it’s not likely to survive.

Anticipating the degradation of ID-tracking, Measured bet on incrementality testing and cohort-analytics as the future of measurement. It’s an effective solution to the growing conflict between performance measurement and privacy because it is not plagued by user-level data challenges encountered by MTA.  

Deployed within the publisher platforms themselves, Measured experiments provide marketers with a true understanding of the incremental contribution of each marketing channel down to the most granular level. In addition, incrementality measurement is quicker than MTA to set up, can be used for scale testing and forecasting, and measures the impact of both addressable and non-addressable media. 

Compare Measured to platform reporting, MTA & MMM

Measured

Measurement – Other

Measured Advantage

Incrementality

Platforms

MTA

MMM

General

Neutral & Independent

Green Check Mark

Grey Check Mark

Grey Check Mark

Trusted Measurement

Measurement

Causal Incremental Contribution

Green Check Mark

Grey Check Mark

Grey Check Mark

Productized Experiments

Scale Testing

Green Check Mark

Identify Saturation Curves

Granular Insights

Green Check Mark

Grey Check Mark

Future Proof

Comprehensive & Cross Channel

Green Check Mark

Grey Check Mark

Depth of Measurement

Walled Garden Support

Green Check Mark

Grey Check Mark

Grey Check Mark

Comprehensive

Transparent

Green Check Mark

Transparency = Trust

Decisions

Tactical Decisions

Green Check Mark

Grey Check Mark

Daily & Weekly Insights

Strategic Planning

Green Check Mark

Grey Check Mark

Bottom Up Forecasting

Timely Insights

Green Check Mark

Grey Check Mark

On Time, Reliable

Data Management

Purpose Built for Marketing Analytics

Green Check Mark

Analytics Ready

Data Quality

Green Check Mark

Grey Check Mark

Reconciled to Source of Truth Platforms

Original Publisher