As a simple example we looked at a client which provides us information about the channel each conversion was attributed to.
Using this data we examined how likely an ad is to get attributed depending on its position in the conversion path using a last click attribution model.
The results are hardly surprising.
Our takeaway was that even if the early path activity generates a lot of interest by making the user aware of the brand and potentially making them search around for it, the channel is placed at a disadvantage by being measured on only those conversions which weren’t somehow picked up by all the other channels available closer to the conversion.
This kind of measurement is similar to valuing a defender based upon their goal scoring capabilities.
However, if you were to measure all channels according to the part they play, rather than their ability to generate conversions by themselves, you’d get much better clarity into the actual effect of the advert irrespective of how close to conversion it occurs.
You’d also find out that brand spend can be measured against and optimised towards interim metrics such as digital GRPs, brand search uplift and a myriad of other easily recordable actions.
Considering the path in this way allows you to start valuing each stage in the path to conversion as part of a larger whole, where media isn’t bought in spite of your attribution model and different channels start to work together as a team to fill various sales functions instead of crowding round the conversion event.
All this leads to greater ROIs and a larger market share, but it does require some thought to pull off. Below I’ve outlined a couple of questions which come to mind when planning a buy like this, it’s not exhaustive so please feel free to comment below if you want to discuss further.
Do I need to change my campaign structure?
Campaigns are already set up to match user purchase intent based upon classical definitions of the user’s path to conversion; ever notice how well the brand, prospecting, retargeting structure matches the AIDA model of purchase behaviour?
At each stage of their path a user exhibits different behaviours, little indicators that your advertising is working before the conversion event occurs. Using these metrics to help measure a campaign means that brand and prospecting spend can be measured and optimised towards the actions they were set up to generate.
Can I track multi-stage conversions?
Yes. Using pixels which are currently in place on your sites you can radically improve the measurement of your campaigns, potentially saving many thousands of pounds in media spend which favours last click models.
The use of multi-stage conversions raises an additional question of double counting as you measure effectiveness based upon new metrics in addition to conversions at the end of the funnel.
This can be counteracted by simply assigning a small proportion of the conversion to the events which preceded it, allowing you to set targets which relate back to your overall goal to all stages in the path.
This is a small step towards the creation of a more accurate and sophisticated attribution model which would ultimately assign a value to each touch point based upon the actions it contributed towards.
However simplified, this model has the potential to open up large portions of your media spend to optimised buying techniques which were previously not even considered for early path channels.
If you’re struggling with how to measure your early path campaigns, first think about what they’re trying to achieve and start from there, you might be surprised by the money you’re wasting.
Try to remember that your different media channels are part of a team, working together to score that all important goal.
Considering each one in the same way as the others overlooks the part they play in that team and could lead to unbalanced media buying and slowly eroding margins as your pool of cookies eventually deletes itself.
If you think outside the penalty box you’ll see there’s a large part of the game that you could be missing.