‘Last click’ is the most common approach; attributing 100% of a sale’s value to the last or converting click.
‘Last click’ is a flawed, though, because it ignores all previous activities that led to the sale, which could result in reduced or cut investment regarding the key contributing activities.
To build a truly accurate attribution model you need current and historic individual-level data from your online channels.
Read on to see why.
Only by piecing together each individual customer journey can you build up a truly accurate picture of the inter-relationship between your digital marketing activities and the relative role, importance and value of each one in the overall strategy.
For example; without aggregated individual-level online data, how do you know if:
This example video shows how insurer, Hiscox, saved 10% of its PPC spend using full customer lifecycle attribution modelling:
Take a look at an example of how the concept of digital marketing attribution modelling works, using a fictitious multi-channel television retailer called Olympic TVs.
This table shows Olympic TVs data about the last or converting click for one particular sale, and the insight it can gain from this data:
|Individual-level online data||Insight from individual data|
|Arrived from Google search||Search engine role and choice|
|Arrived via PPC keyword “Olympic TVs cheap TVs”||Branded keyword and actual keyword used|
|Purchased £800 TV||Converted & value of sale|
Based on this data, Olympic TVs would assign 100% of the value of the sale to Google. It might then decide to increase investment in PPC, to the detriment of its other traffic building activity, such as online display and affiliate marketing. This decision, based on an incomplete view of the role and impact of the customer’s previous interactions with Olympic TVs, might damage rather than improve results.
Alternatively, if Olympic TVs considers the previous interactions of an individual with its brand over time, it gains a much more valuable picture of the role and importance of each digital marketing touch point with its brand.
The table below shows data from the individual's previous four visits, revealing some of the insight that can be gleaned from this data:
|Individual-level online data||Individual-level online data|
|Visit 1 - 1 month before|
|Search engine used was Google||Search engine role and compared to others|
|Arrived via SEO term “cheap TVs”||SEO term used and how well optimised site is|
|Signed up for e-newsletter||Could be viewed as a conversion in itself|
|Browsed the site then left||Did not convert on this visit|
|Visit 2 - 10 days before|
|Arrived via affiliate||Importance of affiliates and which specific one|
|Affiliate messaging was a product review||Which product drove that visit|
|Browsed the site then left||Did not convert on this visit|
|Visit 3 - 1 week before|
|Arrived from a display ad on Facebook||Impact of display ad|
|Message in ad was best TV picture quality||Campaign message effectiveness|
|Visit 4 - 3 days before|
|Clicked through from special offers e-newsletter||Impact of email marketing (see visit 1)|
|Put promoted TV into basket then abandoned||Did not convert on this visit|
It is immediately obvious the website visit is in fact the fifth visit by this individual. Based on this additional insight, Olympic TVs now knows, amongst other things:
With the proliferation of channels and devices available to consumers nowadays, it’s important to look beyond website interactions to include, for example, the brand’s Facebook page, m-commerce sites, mobile apps and other online channels and digital technologies. The same customer might:
You need to be able to connect up these multi-channel interactions to assign them to an individual.
As a result of its better understanding of cross-digital channel behaviour, Olympic TVs gains additional data and valuable insight, such as:
|Individual-level data from channels & devices||Insight from that data|
|Visit 2: they watched two embedded YouTube product review videos||The role of videos and the YouTube channel|
|Visit 3: the display ad was on Facebook and they then clicked the Like button on your home page||The role of Facebook and the importance of driving Likes|
|Visit 4: they read the email on their smartphone and clicked through to the m-commerce site||The impact of mobile marketing investments|
|Between visits 4 & 5: they downloaded the mobile app on their tablet but haven’t used it yet||The role of the mobile app and the fact that they used a tablet|
Even in this simple example, it’s possible to see how bringing together a rich blend of individual, current and historic multi-digital channel and device data allows Olympic TVs to start to build robust accurate digital marketing attribution models.
Olympic TVs can also attribute an actual value to each customer touch point, enabling it to make highly informed marketing investment decisions and replicate effective marketing programmes in the future.
This data can also be used to drive segmentation, targeting, personalisation, channel optimisation and a wide variety of other marketing activities… but that’s another subject!
Once you have all the individual-level online data, there is a series of decisions to be made in order to build attribution models that are relevant and right for your specific business; including:
Having the right data about individuals' interactions with your brand across all digital channels over time is an essential starting point in building digital attribution models that are capable of helping you to:
Thanks to Katharine Hulls for sharing her advice and opinions in this post. Katharine is VP Marketing for Celebrus Technologies, a provider of software that enables you to capture individual-level data from your online channels for use across a variety of customer intelligence and marketing applications. You can follow her on Twitter or connect on LinkedIn.
With thanks to Edelman Digital for use of the image.
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