Frequently Asked Questions

You ask, we answer.

Most of the most common analytics packages, such as GA360 or Adobe Analytics, use pixels and cookies which show device behaviour rather than the behaviour of the person behind that pixel. If you’re relying on an “off-the-shelf” solution, it’s a fairly safe bet that you’re being prevented from knowing what is – and just as importantly isn’t – working across your marketing mix. So, assessing and improving “true” marketing performance isn’t really possible for you.

A few considerations to keep front of mind when searching for effective attribution:
– Is your data poor quality?
– Are your reporting dashboards giving you real actionable insights?
– Can you track the entire customer journey – both online and offline?
– Do you have access to deep dive analysis capability that impacts ROI?
– Can you use predictive analytics to automate and drive conversions?

Multi-touch attribution is a method of marketing measurement that evaluates the impact that each touchpoint has in driving a conversion, thereby determining the value of that specific touchpoint. Multi-touch data attribution models are the only way to understand the impact of marketing initiatives and media across the customer journey, to provide the long-term insights you need for actionable results to drive your marketing ROI.

The oversimplification of First and Last Click models, where 100% of the value of the conversion is given to the first or last marketing engagement, completely ignores all the previous points in the customer journey: almost the same as applying no attribution at all!

Using a data-driven multi-touch approach to attribution allows marketers to see the full customer journey and weight the influence of each touchpoint in a logical way which gives a clearer understanding of where budget should be spent to increase ROI.

How does Corvidae stack up against Last-Click? Find out in our blog
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Deterministic data refers to data collected through marketing activities that is clear, unequivocal, and exact. With this data, you can be certain of the identity of an individual that has interacted with your brand. Examples of deterministic data include age, gender, interests and previous purchasing history.

Probabilistic data relates to data you might receive from offline advertising or social media channels, like Facebook or Amazon. You know that your paid, earned, or shared channel exposure is likely to have reached a given number of people that fit within the profile of your target consumer. But it’s not possible to be certain that someone who takes the desired action on your website was one of those consumers.

Visit Level Attribution provides marketers with an accurate view of an individual’s probability to convert at every stage of their journey so you can confidently move budget from channels – and campaigns – that aren’t contributing to conversions and to start driving growth and ROI by focusing time, money and effort into the activities in your marketing mix that will produce more sales at the lowest cost and acquire brand new customers for your business.

Download our brochure to find out more.

Last-click is heavily biased towards direct visits. Last non-direct click ignores all the preceding channels and first click rewards only the first touchpoint, meaning marketers can never understand the true value of their marketing mix. It is impossible for them to understand the impact each channel has on total revenue and the customer journey.​

The machine learning that Corvidae uses enables us to ‘see’ the customer behaviour behind the generated clickstream data, meaning we can stitch together data points that represent a single user moving across multiple channels and devices.​

With the user journey accurately mapped, Corvidae determines how valuable each event in the journey was in making someone transact. With that, Corvidae can split the overall revenue across many different events that can be grouped into campaign, creative, channel, etc groupings to evaluate overall performance”.

QC studies have shown that in comparison to Google 360 Last Click, we are able to attribute over double the amount of revenue.

Corvidae attributes revenue to the most granular level possible – the individual visit or impression, and then allows aggregation up to insightful segments. Shapley and Markov models cannot handle such a large amount of data points in their operation and the underyling mathematics fails. Due to this, we have developed our own attribution modelling techniques to allow our own approach to be viable.

The Corvidae pixel has two use functions depending on where it is deployed.​ We place the pixel on the client’s website using Google Tag Manager to collect clickstream data​. We can collect clickstream from GA360 and Adobe Analytics, so if the client was unwilling to deploy our pixel we could use JUST that. ​
However, our pixel deployment gives us more freedom and control. Where possible, we will use another platform alongside our pixel which means we have more identifiers to stitch over. This helps us improve the overall quality of the data we use for attribution modelling.​
We embed the pixel in the creative used by partners to track impressions for activity such as Display and Native.​
The pixel is 1st party; once it is placed on the client’s website we need to tell it information about the clickstream data it collects so that we can categorise each visit correctly.

As part of the attribution process, walled garden data is collected at the lowest granularity possible from whichever platform is being ingested.​ The true value of the activity behind these walled gardens is calculated looking across the time series of the data, and the weight of value given to impressions and clicks is adjusted/refined. ​

This is built into our unique Visit Level Attribution (VLA) outcome to better show the comparative efficacy of this activity and allow optimisation of spend at the campaign level. ​This should give an excellent opportunity for agencies managing walled gardens spend to demonstrate value by optimising across this now de-siloed data with normal campaign optimisation strategies.

Read our blog to discover how to unbundle Google Ads and Facebook data for successful attribution.

We tailor the lookback window based on the average length of time of the customer journey.

You will have access to Corvidae within one month.

3-4 weeks from pixel implementation to channel level attribution reporting.

You will have access to the interface within one month so you will be able to begin taking actions based on your newly rebuilt data after that first month.

Discover how Corvidae transformed attribution for some of the biggest UK-based retailers. Download the case study.