Cross-Channel Attribution

Adobe Analytics: Cross-Channel Attribution

Business need –

Third-party attribution platforms, specifically Adobe Analytics (formerly Omniture SiteCatalyst), excel in providing mid-large tier advertisers with insight into their customer journey. Whether it be via Paid Search, Display, Social, or Organic traffic, the goal of cross-channel attribution is to provide analytical understanding into the sequence of events prior to conversion. Today, the business need for cross-channel attribution is a topic that is well understood by advertisers, yet many companies struggle to adopt an attribution model for the reasons listed below:

  • Number of channels in a media mix – as competition in the market increases, as does the number of channels that companies use to reach and influence potential customers. Today, advertisers must not only track but attempt to optimize media across paid, organic, mobile, offline and more.
  • Getting your data in one place – in order to develop and utilize a cross-channel attribution model, advertisers must aggregate their channel data into a single user interface, in addition to tying together customer’s actions over relevant time periods.

 

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Analytics suites, such as Adobe Analytics, tie together customer’s actions from the first touch point to last interaction and purchase, both online and offline. Once the customer data is tracked, Adobe Analytics provides a variety of attribution models, made possible through complex machine learning and regression analysis, that allow advertisers to clearly make informed decisions regarding budget allocation across channel and optimization – ultimately, improving campaign performance.

Models –

Adobe Analytics allows advertisers to group a series of events with individual customers, allowing companies to build customer profiles based upon their own unique experience with the brand. By tracking every touch point and organizing by time, advertisers are capable of unique multi-dimensional analysis that sheds light on customer action prior to conversion. Marketers can not only leverage advanced attribution models in AA, but adapt each model to the unique needs and market of the business (consumer electronic, travel, finance). Adobe Analytics provides both algorithmic attribution in addition to rule-based, both of which can aid marketers in making informed decisions across channel.

  • Algorithmic – algorithmic attribution relies on machine learning to determine the incremental performance impact of particular touch points on the path to conversion. By analyzing behavior between converting and non-converting customers, AA can estimate a channel’s incremental “worth” in terms of true performance impact. Algorithmic attribution is based on econometrics.
  • Rule-based models, beyond first and last – go beyond last click attribution and gain insight into what combination and sequence of campaigns lead to conversion. Adobe Analytics provides many rule-based attribution models, including:

Linear
Latency
Adjacency
Pathing
Starter/Player/Closer

Goals –

With channel complexity providing us with more and more data, advertisers are no longer able to rely on ad-hoc analysis to make effective marketing decisions. Through platforms such as Adobe Analytics, marketers can have a clear, “holistic” understanding of true performance across channel – allowing individuals to make strategic investment decisions and clearly communicate those decisions across business groups.

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Dustin Lewis About the author
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