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

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 Model – In 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.

Measuring display advertising can be a challenge. It’s often difficult to account for views and ad impressions which can contribute to conversion goals driving your business. The most common & easiest method of measuring display advertising is through view-through and click-through measurements provided by the publishers. There are pros and cons to using publisher provided view-through and click-through data:

Advantages of Publisher Provided Display Advertising Measurement

1. The first obvious “pro” is that the measurement is provided by the publisher so there is no extra work required to measure the ad campaign.
2. You can use this data to optimize campaigns/audiences with that publisher, but it has its limitations.

Disadvantages of Publisher Provided Display Advertising Measurement

1. The main “con” of using publisher provided view-through and click-through conversions is that you will be double counting across other media channels. For example, if someone sees your display ad on site X and sees an ad on Facebook, how do you attribute the conversion?
2. If your media portfolio consists of more than 1 channel (which is most certainly the case) you can’t compare the results in an apples to apples way with other publisher measurement results.

In order to measure true contribution of the display advertising, marketers need to measure the incrementality of the display ads. The incremental contribution of display advertising can be measured by using audience holdouts, serving the held out audience a placebo ad, and comparing the measured conversion rate of the held-out audience versus the campaign (or exposed) audience.

This process is called Design of Experiments (DoE). When expertly designed, it has the ability to deliver on the promise of incrementality measurement at the vendor, campaign and audience level in a way that MMM cannot due to practical limits on data granularity and degrees of freedom.

Incrementality Testing for Retargeting Tactics

For heavily biased tactics like retargeting, DoE incrementality results can be actively incorporated into MMM as Bayesian Priors to improve MMM models across the board. For retargeting tactics, DoE offers the most unbiased measurement approach, as it randomly selects a subset of website visitors for exclusion from retargeting impressions, both in total and at the vendor level, in order to measure true incrementality of these tactics on a customer group that has already established interest and intent.

Incrementality Testing for Prospecting Tactics

For prospecting tactics such as Facebook, DoE randomly selects a subset of prospects to serve as the control group. One approach to capturing a control audience is to show them a placebo such as a PSA advertisement (charity ad) which has nothing to do with the brand, but serves as a way to initiate tracking and thus segmenting the user away from the exposed cohort. Because this is designed at the group level, DoEs are not subject to all of the user level data challenges encountered by MTA requiring only that campaigns exhibit enough reach to establish statistical significance at the group level. For most advertisers this statistical significance is achieved in a matter of weeks and can be meaningfully updated afterwards on a weekly basis to inform tactical campaign optimization.

Want to learn more about how to reveal incrementality on all your advertising channels? Read the guide!

What Is Incrementality in Marketing?

Incrementality in marketing is the lift or increase in the desired outcome (for example, awareness, web visits, conversions, revenue, profitability) provided by marketing activity. 

Incrementality testing and measurement can provide the true incremental contribution of your paid media at the channel, tactic, campaign, or ad set level. Incrementality in marketing is especially needed for channels where ad impressions are difficult to map and measure, such as “walled garden” social channels including Facebook, Snap and Pinterest, or even TV and direct mail.

Why Is Incrementality Measurement Important?

Because they don’t have access to all the data outside their own environment, every digital ad platform uses the same flawed last-touch attribution method. Reports provided by ad platforms will never match up with site-side analytics reports or what’s observed in a brand’s sales data. Taking platform reporting at face value is a risky practice that can lead to bad decisions and lost revenue.   

which media investments contribute to business metrics and by how much. Measuring for incrementality identifies where to eliminate waste and surfaces opportunities to scale, expand and reallocate media spend for maximum growth. 

With incrementality measurement, you can answer questions like:

• Which media channel, publisher, or campaign is contributing to my desired outcome (leads, LTV, ROAS, revenue, net profit/$, etc.)?
• What happens if I start buying media from a new platform or if I reduce or stop buying ads with a platform?
• Will launching new campaigns or ads increase contribution to conversions at the portfolio level or will it cannibalize from other channels?

How do you measure incrementality of media?

The most accurate way to measure incrementality of media is through testing and experimentation. To measure incrementality, audiences are randomly segmented into test and control groups. The difference in conversion rates between the two groups demonstrates the marginal incremental contribution of that media channel.

Incrementality measurement can vary in complexity from a simple holdout test as described above to multivariate experiments so elaborate they require the expertise of a trained data scientist. But, when carefully designed and cleanly executed, controlled experiments can utilize data from an unlimited number of sources to reveal the incremental impact of just about anything marketers want to test – on any outcome that can be measured.

Incrementality measurement in action 

Let’s look at some examples of incrementality in marketing and see how incrementality is calculated

Perhaps a brand wants to better understand the effectiveness of its retargeting campaign. To calculate the incrementality, they would withhold a small but statistically significant group of their audience (typically 10%) and not serve them the retargeting ads. Out of this group, which did not see the retargeting ads, 10% end up repurchasing the products.

The test group did receive the ads, and they repurchased 13% of the time. Using these numbers, we can use the incrementality formula to calculate incrementality.

The incrementality formula: (%CR Test – %CR Control) / %CR Test

So in our example, the incremental lift is 3%, which based on the formula, results in a 23% incrementality.

Incrementality Case Study: Soft Surroundings

Soft Surroundings, a women’s clothing retailer, chose to work with Measured to better understand and optimize their retargeting efforts.

Measured’s retargeting experiment revealed that the incremental cost per acquisition CPA(i) was well above CPA targets and what was reported by the vendors. The company’s largest retargeting vendor by spend was heavily over-indexed and serving ads beyond a recommended frequency cap.

As a result of these findings, retargeting budgets were reduced by 52% in the next few months. The extra budget was shifted to higher-performing prospecting tactics like Facebook. Topline revenue improved 17% MoM while yearly sales comps increased 12%.

The Impact of Incrementality Measurement in Marketing

Incrementality in marketing allows you to see which audiences should be served which ads and on what platforms. 

Other methods of measurement, such as media mix modeling (MMM) and multi-touch attribution (MTA), cannot measure views or impressions, so you’re essentially just measuring clicks. Incrementality measurement accounts for the impressions and clicks within each of the platforms under test to give marketers a more accurate view of the true contribution of their media across the entire portfolio.

Read our guide all about incrementality management!

Video: Leveraging Incrementality to Value Retargeting vs. Prospecting

Only Measured delivers ongoing, reliable insights based on scientifically sound experiment designs that incorporate your source of truth transaction data. Everything is automated – ingestion and management of data from hundreds of sources, experiment design and implementation, and continuous reporting for confident, agile decision-making.

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