If there’s one thing almost all marketers have struggled with in recent years, it’s attribution modeling.
Attribution modeling is the science of assigning credit to the marketing investments that led to your customer making a purchase.
According to a recent Google survey, it’s the #1 tactic that occupies marketers’ minds.
As many marketers will testify, capturing, collating and interpreting the increasingly complex data sets about how, when and why customers make a purchase is not easy.
Customers might engage with your marketing multiple times, through viewing online ads, engaging with social media, and using online searches. They can do these things using a mix of laptops, PCs and mobile devices. And then they might walk into your store and make a purchase.
Even where the data is captured effectively, many attribution solutions struggle to generate meaningful insights as they use arbitrary, rigid models that don’t reflect the complexities and variables in your customers’ decision-making journeys.
As a result, few marketers can pinpoint the real value of a channel, and they continue to rely on guesswork about where best to invest their marketing budget.
In 2018, this should change, as more advanced attribution modeling technology becomes readily available.
Data-driven attribution models
The alternative to formula-based attribution models is to use a data-driven model, such as the model which Google has had built into its enterprise-level tool, Google Analytics 360, for some time.
Data-driven attribution works by analyzing customer journeys – both converting and non-converting – measuring how different journeys correlate with different conversion rates, and attributing value based on the impact each channel has on conversion rates.
For example, if Google was analyzing the value of your display advertising, it will compare the conversion rates of journeys that didn’t include a display ad with those that did. It might find that you have a 2% conversion rate across all journeys, but 3% when a display ad is involved.
Using this data, Google Analytics 360 can tell you how much value is added by each channel, a method that’s far more sophisticated than most standard attribution models.
Google Analytics 360 demonstrates this by comparing, side-by-side, its own analysis with a last-click model. The differences in the value assigned to each channel can be huge – Search Laboratory has seen discrepancies as large as 270%.
Data-driven attribution isn’t just built into Google Analytics 360. DoubleClick uses it as well, and everyone has access to the version that is available for free in Google AdWords, although this only includes advertising spend through PPC and the Google Display Network (GDN).
The latest developments in AI, however, and the escalating sources of data, have led Google to further develop this approach into an even more sophisticated solution that is due to be released some time in 2018.