Introduction

This paper defines the marketing attribution problem and looks at the ways marketers can generate an accurate view of their return on marketing and media investment.

There are lots of views, approaches and tools available to assist marketers with marketing and media attribution but unfortunately, most are limited in one way or another and some are actually dangerously misleading.

This paper aims to sort the wheat from the chaff when it comes to attribution – especially in paid media environments.

First, let’s define the problem attribution modelling seeks to solve.

Marketers are investing large sums of money for their companies and and for many larger brands, that money is often shareholder funds. Marketers therefore have a responsibility to ensure that their budgets are being invested optimally.

The marketing ecosystem is much more complex than it appears; different channels work in different ways across the marketing funnel. And, in addition, the channels that present sales are not necessarily the channels that drive sales. Moreover, what appears to be a cause often isn’t; before marketers can make judgements about their own investments they must strip out the effect of extraneous factors like competitors, pricing, climate, the economy, seasonally and any other variables that may have an impact on how their own sales are, or are not generated. Failure to consider all these effects will ultimately lead to flawed analysis.

Inaccurate analysis will lead to poorly informed media investment decisions at best and ineffective – potentially loss making – media investment decisions at worst. With media budgets running into millions, those mistakes can be expensive.

So it’s critical that the best approaches are used. Anything less carries risk. Let’s take a look at some of the main forms of attribution here:

1 – Last touch media attribution

Last touch attribution is the most readily available but potentially most misleading form of marketing attribution. Last touch measures the last journey point that consumers touched before they reached your business or brand. Usually this is a google search, but it could also be a banner view or click or a visit to an aggregator. Either way, it’s the last action the performed before they visited your site visit or call centre. All these touch points can be measured – Google produces a full suite of last touch metrics via Google Analytics and GA360. Direct site visits can be measured by your own web stats package or GA. Phone calls can be measured and attributed to a source through the use of unique phone numbers.

Because these last touch channels are so readily measurable and can be linked so closely with sales it’s tempting to make them the focus of your attribution analysis. But there’s a problem here – focusing your attribution measurement on last touches doesn’t give any insight into the previous interactions in the customer journey i.e. what consumers were doing just before that last touch – in other words, what really drove them to you.

This could mean you fail to spot the important stages in the customer journey before you see that last touch. Let’s look at an example: a consumer follows a typical need-research-shortlisting-choice-purchase customer journey. A number of influences will be at play in the early stages of this process – a display, AV or outdoor ad may encourage our consumer to say “I must do something about that <problem>. The prospect may wait a few days and then another AV ad re-prompts a visit to solving the problem. The consumer then moves to the “information search” stage of the buying process – they may use Google, they may visit a retailer, they may visit a brand directly – in the case of financial services, they may visit an aggregator. Still no final decision though. And then they receive a bill from their current supplier. At that point they perform a brand search and review competitors. From this brand search our prospect shortlists three potential suppliers and then another AV ad prompts them to undertake another brand search and make a final purchase.

We can see this is a complex journey with many touch points. The last touch is a tiny part of the journey. It’s the last one you see, but it is by no means the only one.

This means that rather than focusing on the last touch you have to find out how where credit is due in the customer journey.

2 – ‘Artificial’ attribution models

One of the most popular ways of attributing across the journey is to use a predefined attribution model to break prospect journeys into a number of stages. The best example of these is six variants often used in digital marketing – last touch, first touch, even weight across all touch points etc. These models aim to split the responsibility (and revenue) for driving the prospect to your business across several pre-last touch points.

Unfortunately, there is often very little factual basis in these models. The postulated pre-last touch pattern is little more than an unproven hypothesis of what might have happened in the journey. As such, artificial models offer little or no scientific value to discerning marketers.

3 – Direct URL or phone number tracking

This approach – sometimes called “linear” tracking – was originally developed by traditional direct marketers. They used coupon codes and later unique phone numbers to track the origin of sales back to different ads and media channels. As the internet grew, this approach was trialled by digital marketers using unique page URLs. However, whilst unique phone numbers were a reliable way of capturing the last touch, the web URL approach was not. Consumers tend to search brands and products they’ve seen advertised, or distinctive phrases that have lodged in their memory – “compare the meerkat / market” for example. But consumers don’t search www.abtaholidays.com / tube. And, in any event, direct tracking does not move us beyond the last touch attribution problem.

4 – Universal Journey Tracking

Universal tracking can only be implemented in digital environments. It involves setting a tracking tag or cookie at each touch point – a banner ad view, an email opening or a site visit. If each tag is time-stamped it is possible to plot the chronology of the different times and channels in which views or visits were made and the tag was activated. This allows analysts to plot the journey before, up to and including the last click. Setting up this type of campaign can enable a reasonable amount of journey reporting through DV360, GA360 or simple GA. but the problem here is that this type of tracking is restricted to digital only environments. It can’t be deployed in offline media – and it’s often offline media that drives the upper funnel by providing high reach and scale-up opportunities. So, for many brands the challenge is to understand the relationships between upper, mid and low funnel activities.

5 – Attribution through patterns, trends and correlation

What are the options if you want to move beyond last touch or linear tracking to get an understanding of how your marketing and media drives clicks, leads, calls, sales and market share?

There are two options here – econometrics or simple trend and correlation analysis. We will cover econometrics in point 6 – first, let’s look at more simple correlation analysis.

When marketers run a campaign they usually have an objective to change a variable. Let’s call it sales. In the statistical space, the sale becomes the “dependent variable” – that means it’s the variable we are trying to change by applying different “forces” to it. We might apply TV, OOH, press, radio or digital advertising to drive reach, brand uplift and visits.

By collecting daily or weekly levels of investment over time in each media channel, alongside the dependent variable, it will be possible to observe whether any one or combination of media channels are correlated with changes in our dependent variable.

So we might observe for example, that direct web traffic increases are highly correlated with a new TV campaign.

Some TV “spot matching” tools like Adalyser and TVSquared fall into this category. These tools match two trend datasets – TV impacts over time and web or call traffic over the same period – often on a minute by minute basis.

These trend approaches based on time-series data give us a much clearer picture than “last touch” metrics. “Last touch” would simply observe an increase, but it wouldn’t necessarily explain why it was happening – especially if the driver variable/s are outside the digital ecosystem. But trend analysis has an Achilles heel – any changes observed must be separated from what might have happened if no advertising had run at all. This “baseline” question is a key issue in trend analysis and only econometrics can solve it correctly.

6 – Econometrics / Market and Media Mix Modelling

The trend analysis outlined in point 5 above takes us to a more sophisticated level of multi-channel attribution. But it doesn’t consider the external factors that shape the effectiveness of our own advertising – things like underlying seasonality, competitor spend, price promotions, incentives, consumer confidence, weather and even house prices and other wider economic measures like RPI and unemployment. It’s only when you consider your own marketing investments within the context of all the factors that you have the full and clear picture of your marketing effectiveness.

And that’s where econometrics comes in. Econometrics considers all these factors and attaches a value to them – positive, negative or neutral. It will also help you understand what the unique impact of these factors is and it will also allow to identify factors that work together.

But most importantly, econometrics allows you to understand how your brand, promotions, media and pricing sit in the context of the way your category works and in particular the impact of competitors.

Econometrics also gets marketers and media planners over the “last touch” problem because it allows you to quantify the effects of all channel metrics on sales. It will reveal the effect of all upper funnel channels sales channels – so for example – econometrics will tell you how many sales TV generates but it will also tell you how the sales generated by TV distribute across all lower funnel channels like search, direct traffic, phone and retail.

So in any time period econometrics can attribute your total sales back from the channel where they appeared to the channel that generated them – whether online or offline – with consideration for all factors driving sales and therefore with a high degree of accuracy.

Conclusion

Because of the sums being invested and the value of the results at stake, marketing and media attribution are core responsibility areas for marketing directors, CMOs and agency media practitioners. But it’s a field where last touch linear metrics offer a tempting but misleading view of how marketing and media investments really work. More sophisticated approaches require a wide number of datasets and take time to enable, but they give you a much more accurate view of how your investments are working. The time and patience required to deploy advanced techniques will pay off and enable you to fully understand how these investments work and optimise them to produce the best possible results.