What is Marketing Attribution Software?

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.