Analytics and optimisation consultancy Logan Tod has developed an approach that incorporates attribution modelling within its own optimisation framework so its clients can decide where to invest their ad spend.
The framework classifies individual marketing activity, from high-level channels down to low-level keywords, with specific recommendations associated with each. One visualization of Logan Tod’s process looks a little like this:
So, if you can work out how much money you spent on any particular ad channel and how much revenue it generated, you can figure out just what to do next. But, identifying the latter is incredibly difficult. That’s why Adrian Nash, Head of Analytics and Insight at Logan Tod, welcomes the opportunity to work on clients with TagMan.
Speaking at TagMan’s second client event, TagMeet 2, Nash told the assembled throng: “Whenever a client says they have TagMan, I always say ‘great’ because it provides a great data source for us to work on.”
TagMan data enables Logan Tod to see how much revenue any online channel contributed to, not just in terms of being the last click, but throughout a customer’s path to conversion. This means a channel’s revenue contribution can be assessed much more fairly, particularly those channels that tend to be more effective ‘upstream’ of the last click.
But, the level of complexity that Logan Tod is applying to understand, report and recommend based on this kind of reporting boggles the mind.
Nash combined sweeping insight such as “it is better to be precise than accurate” (because precision is repeatable) with ultra-practical advice. For example, he explained that the path-to-conversion data in some tools doesn’t report anything over 30 days from the last click, which, based on recent Google research, indicates that up to 30% of research behaviour prior to a sale may be missed; “in those cases the first click isn’t the first click at all, it’s actually in the middle”.
He then explained how Logan Tod produces complex scorecards to rate the effectiveness of channels and applies predictive models to test how different mixes would affect return on ad spend.
In all, Nash’s session showed the level of expertise that can be applied to the data TagMan provides. However, using attribution data, Logan Tod recommends clients start with something simple:
Since using TagMan data makes it very simple to value every channel’s revenue contribution as first touch or last touch, advertisers can ask new questions of their marketing strategy and campaigns. If activity tends to appear only as the last touch before a conversion and doesn’t play a role further up the conversion path – is it’s role being overvalued or vice versa? TagMan data contains the information required to perform simple analysis like this through to more complex attribution modelling and ensures that there is a very low barrier to entry for clients wishing to use attribution to improve the performance of their marketing.