People usually receive or respond to multiple communications on their way to making a purchase. These can be online, such as a Facebook ad, or offline, such as receiving a catalogue. Customer journey-based marketing attribution looks at all the interactions between a customer and an organisation before a sale in order to analyse the role of each interaction, and hence their contribution to the sale.

We expect to include all ‘direct’ channels, in other words those where there is a one-to-one relationship between the customer and the company, or where there is a direct link through an online user clicking on a digital ad (such as PPC) and visiting the company’s website. An email is direct because it goes to a known recipient, whereas a press ad is not because the recipient is unknown. However indirect channels, such as TV, press, and outdoor, can also be very important. These require a different attribution technique called econometrics.

We start by putting a snippet of code on your website to enable us to download into our attribution platform all of your (first party) website visitors’ browsing activity, including, most importantly, how they arrived at your site. This allows us to distinguish, for instance, between a referral, a branded search or a natural search. We then link the browsing to any offline journey steps by joining them to individuals using identifiers they have provided. To obtain the non-web contact activity we ingest feeds into our Customer Data Platform (CDP) from sources such as your email service provider, direct mail contact history, and your order processing system.

The brutal truth is that for attributing value to marketing activities we ignore them, which is not to say that the unsuccessful journeys are unimportant as they are key when we are describing customer journeys overall and understanding the total spend through a particular channel (i.e. we need successful plus unsuccessful spend). But a campaign will only have value attributed to it from the journey steps it created that led to a successful outcome.

We normally look back 90 days before each sale, although some clients ask us to look at shorter periods, e.g. 30 days. To a large extent it depends on the type of purchase and the channels used. For instance, a catalogue will have a much longer shelf life than an opened email, so we need to give it time to have its effect.

It is obvious that all journey steps are not of equal importance, so weighting them correctly is crucial to obtaining a successful attribution outcome. There is a great deal of online discussion about this subject, with different approaches being debated, but we have opted for a method which is mainly based on the time intervals before and after each journey step.
If, for example, an event happens just before a sale, we give it a high closing score. In contrast, if there is a long interval after the first event, then we conclude that it could not have had too much of an impact on initiating the sale. We also give credit to events that help keep the customer interested without actually closing the sale. If you would like a more detailed note on how our weightings work, we would be pleased to share this with you.

Yes. Multiple opens of the same email on the same day is one example, as is a visit to PayPal just before closing a sale. We try to eliminate anything that does not contribute to the customer’s decision to purchase.

We can. For example, new and existing customers behave entirely differently in terms of the kinds of journeys they make and what marketing events they respond to. Another way we divide up customers is between those who mainly search and buy online and to those who order through a call centre. But you may also wish to look at the impacts of marketing on different types of customer segment, and our platform can support that.

We do, and we find very significant seasonal differences. To show this, we have a specific report providing month-by-month summaries so that we can, for instance, compare email or any other channel’s performance in one month with another.

We often look at non-sale outcomes, and, for instance, recently we have been working for a charity that they wanted us to look at how they get their users to take up different tools that they provide on their website. When looking at non-sales outcomes we lose the value element that we have in a sale, but otherwise the process works in an identical way.

They are always available online at any time and the data behind them is processed each night. So on any day you will be looking at results up to midnight the day before.

We are finding that this is an increasingly important area, and to respond to our clients’ requests we are building a whole suite of customer journey reports. These will answer questions about the lengths of journeys, the mix of channels used and the sequence in which they appear in the journeys.

Yes. It uses only your organisation’s first party data and excludes cookie and analysis opt-outs, for example.

Whether you are using Google Ads or the new or old version of Google Analytics, they have well-documented flaws – namely inaccuracy and incompleteness. Google Ads uses the last Google Ads click, but Google Analytics uses the last click across all channels, so over-reports as it does not take into account other channel’s contributions. Google recognises that this is an issue and has developed Google Analytics 4 (GA4) to
replace the previous version called Universal Analytics – but this will not solve the core flaws. GA4 will use black-box algorithms and again will not take into account all marketing activity. Google is sampled and segments cannot be applied retrospectively for analysis.
Google does not use first-party data or individual identifiers, so it cannot be joined to other data sources of marketing activity.