Marketing Attribution Models: Go Beyond Last Click

Marketing Attribution Models: Go Beyond Last Click

Attribution models help assign credit for sales and conversions to touchpoints in conversion paths and they are part of fundamental optimization techniques for marketing channels. Although it’s not hot topic (like conversion attribution window), it is a mandatory process, especially for companies that spend large advertising budgets and marketers who want to allocate their money in a sophisticated and efficient way.

Default Last Click Attribution Model

Default last-click attribution would be a good solution if users made just one interaction before they convert. Just check -into your Google Analytics- your users’ type allocation (returning vs new visitors) and realize that for each returning visitor there are at least 2 touch points, which means that if we use last-click attribution we’ll lose minimum 50% of the conversion value overall. Yes, at least half of the conversion value (best case scenario), gone. Now, imagine that you have 10 interactions before a conversion and you will realize that you lost 90% (!) of the conversion value overall.

I am not going to re-invent the wheel about attribution models, Avinash made our day easier about them with this comprehensive article, providing all the necessary information about default attribution models (along with their pros & cons) and his preferred custom attribution model as well. I just want to share a different point of view based on personal learnings about marketing attribution models.

Lessons Learned: Attribution Models

  • Ode to Time Decay

    If you don’t want to create your own custom attribution model, I would suggest selecting Time Decay attribution model (click only). This model is useful for digital touchpoints that drive conversions AND touchpoints that increase the likelihood of a conversion in the near future. Please double check and confirm your lookback window before you implement this model. The time decay factor (click only) calculated as follows:

    TDF is defined by two things: (1) the time span the interaction is away from the conversion x and (2) the time decay parameter p. The smaller p is, the quicker the value of the TDF decreases with increasing distance of the interaction event from the conversion.

     

  • Custom attribution model 

    There are a lot of custom models out there (from “bathtub model” to this “mindblowing” model) but most of the times we have to create our own attribution model in order to meet our needs and solve our problems. If we want to create our own attribution model we need to consider the following:
    Time lag 
    We need to check the time lag of our conversions in order to add it in our attribution model. A big time lag means that the user needs more time to convert, so the initial touchpoints don’t influence him equally with touchpoints that are closer to conversion (avoid time periods with seasonality, for more relevant results). For more information about time lag data check here.
    – Path Length
    Path length is critical. Along with time lag will help us define our approach. If the main time lag and page length are big (ex. 90 days – 4+ touchpoints), then we should consider giving more credit to touchpoints that are close to final conversion. If the main path length and time lag are small (ex. 3 days – 2 touchpoints) then we can maybe split equally the conversion value because all touchpoints matter almost the same.
    – Interaction types/Engagement adjustments
    “Click” interaction type is the most useful since Google Analytics does not track impressions and rich media interactions from other channels besides Google products. You can add impressions to your attribution model and specify the relationship between general impression weighting and relative time weighting. Finally use user engagement metrics and distribute credit proportionally based on page depth rather than time on site.

  • Google Analytics Premium: use the data-driven attribution model

    If you use Google Analytics Premium you have 2 additional tools that you can use, ROI Analysis & Model Explorer (both in Beta). The model explorer displays the weighted average value for path positions prior to conversion for each channel based on data-driven attribution model.

    % reflects the overall weighting of a channel at a particular position in the path

    From the report above we can see that while paid search and display help users initially connect with the website, referral, organic search & direct are close to the end of the conversion path.

 

Marketing attribution models have their limitations as well. For example, we can’t measure the effect of our offline or TV ads and we are still depended on cookies (when it comes to tracking) but attribution models still can help us allocate our budget efficiently across the digital landscape and lower our cpc’s and cpa’s. As Charles Babbage said: “Errors using inadequate data are much less than those using no data at all”