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Attribution is changing – how can you prepare?

Online marketers have started to understand that with the imminent demise of cookie tracking, Multi Touch Attribution (MTA) will never be the same again. But what exactly is happening and how can you prepare? During this era of rapid e-commerce growth and increasing use of digital marketing, it is more important than ever to understand the technologies affecting marketing measurement and data privacy. Marketing Attribution, which measures marketing to sale touch point by touch point, has implications for both topics, and the changing nature of browser technology and growing concerns over customer privacy, mean that attribution models will need to change. Marketers will need to adapt and plan for a long-term strategy that allows them to leverage first party data instead of relying on external data trackers. 

So, what are the challenges and solutions? Let us make sure we understand the technology shift first, which comes down to a shift away from a long established tracking technology, the browser ‘cookie’.

What are Cookies again? And are they really going away?

If you don’t already know, cookies are a widely used tracking technology that works through small data text files stored on your machine by your internet browser. Once you have accepted the use of cookies on a website domain, their cookies can  track your behavior on that site. Although strictly limited in data they collect, cookies are a powerful tool to track your user journey from point A to point B. You can think of a cookie as labelling each user with data about their observed behaviour and information they share with that site. The power for marketers comes from using this information for personalisation and even predictions about future visits to the site, including data driven attribution predicting whether a visitor will buy something. 

The most visible application of cookies in marketing has been advertisers using cookies to target consumers with numerous ads and promotions depending on their interests,  including re-targeting you after you visited their website  – this happens especially if the potential customer allowed the use of ‘third-party’ cookies. But what is the difference between first-party and third-party cookies?

What is happening to cookies?

Third-party cookies are dead! – Long live the cookie

The key thing to understand is that only certain cookies are affected by the recent privacy motivated shifts, and that certain cookies, specifically, ‘first party’ cookies, are here to stay. First party cookies are created by the website that you are visiting at the current moment and are quite harmless from a privacy perspective. First party cookies are typically used to personalise and improve your user experience. They cannot ‘spy’ on private data about you or read any files on your computer. It is generally accepted that retailers should be allowed to cultivate automated online relationships through interactions with their customers. Indeed, it is crucial to build up trust with consumers who in return are willing to forgive brands utilising this strictly limited and anonymous data to help market their brand to different audiences.

Third-party cookies on the other hand, are created by other domains usually to harvest your data and understand the underlying information and habits, for advertisers to predict your buying and searching behaviours across different web pages. These cookies have allowed ad-tech companies to create detailed profiles of users for building highly targeted marketing strategies. This is possible because third-party cookies not only allow for tracking the user journey on a particular website domain but across multiple domains, for any domain which shares the third party code, For example, if you visit a healthy eating website and accept the third-party cookie, other websites can be given access to that cookie, and you will likely receive a load of health and food related ads wherever you go online. Although this common use is usually fairly harmless, if sometimes annoying, it is the technical potential of the third party cookie to endlessly accumulate detailed information about a person from site to site. This has caused a concern among data protection advocates and privacy conscious consumers.  

In the end, technology which relies on third-party cookies has lost this battle and they are being phased out. Indeed if you are reading this article in 2023 or later, you will be reading about a technology that used to exist. What started as a tracking blocking feature of less well used browsers  like Safari and Firefox in 2013 has now become the norm, notably since Google announced in 2020 they would phase out third party cookies from Chrome which is used by over 50% of users. Google’s new ‘privacy first web’ will change the digital marketing space forever. 

As cookies are being phased out new tracking technologies are growing in importance, such as user cohorting (e.g. Google’s ‘FLOC’) and anonymous IDs (e.g. Unified ID 2.0). These are interesting but we think will be limited in terms of scale and no doubt will attract privacy concerns as well.  

The End of ‘View-Through’ Attribution

The ‘view-through’ metric was never the best way to measure online ads. The idea was simple enough – when someone clicks on an online ad the performance can be measured by counting the clicks that convert for ‘click-through’ conversions, and so why not also count the ad views that lead to conversions as well, and call them ‘view-through’ conversions!? The only problem was how to count ad views when customers never actually interact with the ad before making a purchase. Enter once again the third party cookie, which could be dropped by the ad-server domain through the ad placement itself and then updated again later in the online conversion for that advertiser, in order to count each view-through sale. 

What seemed like a clever way to understand how ad impressions drive value for advertisers, the ‘view-though’ rapidly became a major source of mis-attribution. This is because the sale could happen many days after the user is shown the ad in which time many other ads and influences both offline and online may have occurred. The concept of measured ‘viewability’ helped this a little, but over attribution remained. One of the huge challenges has been targeting itself: a well targeted ad in a high reach campaign is almost bound to generate a ‘view through’ sale since all the ad needs to do is get loaded into a users browser at some point in the days before purchase. In this way display ads, in particular, have been rewarded for just showing up sometime before a sale with the actual incremental impact of the advertising unknown. With a click, the consumer is usually making a conscious choice to visit the advertiser, whereas most ‘views’ just happen because the consumer is online a lot. 

However, as explained, the view-through metric depends on the third-party cookie which is history. View based measurement has to connect a person buying something online to an ad seen earlier on a different website, which means you need the cross domain tracking capability of third-party cookies. 

Just as the third party cookie will not be missed by most consumers, likewise many serious measurement professionals will be glad to see the end of view-throughs. Before they celebrate though, it is worth noting that major sites like Facebook, Google and Amazon, can still link ad views to sales when they handle both the ad delivery and the sale on their platform, which of course they uniquely have the reach and power to do. So there may yet be an afterlife for the view-through metric.  

Multi-Touch Attribution Moves to Click or Visit Only

Third-party cookies made it possible to track both ad impressions and clicks in attribution models, but now it is not possible to collect data in this way. The ad view (impression) touchpoints are dropping out of the picture, and only direct clicks remain within the measurable customer journey. What people do on the web, including what ads they get exposed to, is now hidden by stronger privacy controls. However, an attribution which is based on clicks to the advertiser site can still be picked up by first party cookies and web analytics. Therefore online sellers can only tell if their ads are being noticed when a potential customer shows up by clicking on one.   

Attribution in the future

How Will Marketing Effectiveness Change?

The gap left for online ad view measurement means upper funnel brand activity including display, video and social media all become harder to measure. Performance journeys, such as affiliate and search clicks and including some mid funnel consideration activity like generic search and email, can all continue to be measured using data driven attribution methods. So this type of click attribution must now be combined with other methods to evaluate ad views.

The topic of alternative methods for measuring display and social ad impact is huge in itself, so we will just take a brief tour of the three main options: (1) New measurement solutions offered by the media tech giants (2) AB Tests/ Randomised Control Trials (3) Econometric methods. 

First off, the tech giants are rolling out new measurement systems which promise to track how ad exposure has changed people’s behaviour without the possibility of identifying any particular individuals.  Google has posted an explanation of their approach centred on their Privacy Sandbox, which includes solutions for targeting in a post-cookie world. Google’s FLOC and FLEDGE technologies both get around user ID tracking by aggregating users together in anonymous groups at browser level, with tightly defined criteria for the group definitions which prevent drill down or cross referencing to pick out individuals. FLOC (Federated Cohorts of Learning) groups users by affinity and interest for in-market audience targeting which can then be targeted by advertisers whilst retaining user privacy, while FLEDGE (First Locally-Executed Decision over Groups Experiment) focuses on enabling automated display retargeting at non-user level where interactions with advertisers are stored within the browser and then sold by Google as retargetable segments. Google says it will use these aggregation type methods for anonymous measurement through to conversion. The benefit to users of increased privacy protection also leads to increased advertiser reliance on Google to accurately measure performance with no way for independent measurement or verification.    

A second approach to consider for any kind of ad measurement is to run an experimental AB test, or to be precise, a Randomised Control Trial. This is where a group of people who are not exposed to the ad are compared to a well matched group who are exposed to the ad (control vs treatment groups). Done properly, nothing beats an experiment for accurate measurement of incremental impact. Targeting can help with experiments, helping in ensuring the two groups are well matched and therefore suitable for comparison to make ad impact estimations. The problem now is that targeting is increasingly dependent on the big tech media owners i.e. with Facebook and also with Google FLOC, you can target only within their networks and on their terms. The measurement challenge increases when you want to combine or compare media impacts across more than one media network e.g. a campaign on both Google and Facebook combined. Without 3rd party cookies cross media AB testing is technically very difficult since you cannot be sure if someone in the non-exposed control group on one site might nonetheless be exposed to an ad on another one. Solving this requires some kind of common targeting framework such as matched location geo-targeting (a good option, but not without challenges) or otherwise using an opt-in consumer research panel that can work across multiple networks (requiring research company and media owner support). 

The third type of solution is old measurement tech – Econometrics, or Market Mix Modelling. These are statistical methods which represent the earliest forms of classical machine learning and grew in popularity back in the 1990s as computational power made it possible to estimate a wider variety of more complex models with relatively small amounts of data.  The idea is to use several years of observations of ads and sales, as well as data on economic, seasonal and business drivers, to build a ‘best fit’ model. This model measures how the drivers work together to generate sales, in particular estimating a coefficient (a multiplier) for each media channel to estimate how many sales it generates. The great thing about econometrics is that the data is readily available and can be increased in scope by sampling across multiple locations. Given the fragmentation and complexity of online data, it is not surprising that Econometrics is making something of a comeback. The downside is that it remains a tool of very imprecise high level directional estimation, and requires significant expertise to get right. If attribution is a microscope examining the user marketing journey closeup, econometrics sees it through a telescope in the gloom of a cloudy day. 

The marketing analyst is left with these imperfect tools to try and evaluate the now hidden user journey from ad exposure to sales.  A combination of methods will usually work best – for example using a one off experiment to help validate an econometric estimation.  

What do these changes mean for businesses and how do you adapt?

At the highest levels, the big shift going on here is that big tech companies such as Google and Facebook increasingly keep the data about user interactions with ads within their own walled gardens. This increases the dependency that brands have on these giants especially for targeting and measurement. This suggests the long term strategy for marketers should involve growing their own first party consumer data using sources like web analytics, transactions data, customer data and CRM, rather than relying on third-party data collectors. Likewise, a marketing strategy will have to include mechanisms collecting data from their own data points such as logins, subscriptions, email forms and call centres.

Investing in resources to build brand awareness and capitalising the pull approach of the PPC techniques and keywords targeting will be vital. This allows companies to ‘pull’ potential customers from the web as they are already interested in the product or service as they are browsing.  Advertising partners will continue to be used to reach new audiences and grow the customer base, but ad effectiveness measurement of less tangible brand and impression based impacts will increasingly rely on aggregated data methods which group customers together to protect their privacy. Relying on the media platforms to measure how your customers respond to your campaigns will only increase your dependency on them and provides no independent means of validation on your media spend. 

With the increased focus on first party data it is essential to build a strategy around customer relationship and trust. Since businesses will be focused on their own data collection it will be imperative to build up trust with your consumer, who will, in turn, allow you to use their data. This can be accomplished by following strict data privacy policies and using data for the consumer benefits such as user experience enhancement and content personalisation.

It will also be important to understand the aggregated data, as well as first party individual data, grouping consumers based on their common buying behaviours and search habits. Google’s FLOC and FLEDGE should prove helpful for audience analysis, allowing marketers to choose a target audience, segmented by common buying behaviours and interests. Targeting by the audience and contextual targeting will grow in importance as cookie based targeting falls away. 

In conclusion, utilising a data-driven approach for click based attribution modelling will continue to enable accurate insights for commercial decision making and performance ROI, leveraging first party data. Companies will have to rely heavily on their own data collected web analytics and other customer facing systems in order to understand the true value behind each touchpoint in the customer journey. For data around how customers passively interact – for example, ad exposure effects – aggregated data is the only way forwards. At Metageni we only use anonymised first party data for click attribution and then use econometric approaches to understand the branding potential of display, social as well as all offline media channels. While businesses still have time to adjust to the new reality, the digital marketing space has always been subject to change. The question for brands is are they getting closer to their customers or relying on the tech companies to do it for them? 

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

Gabriel Hughes PhD


Does this article resonate with you? Check out other posts to the right of this page.

Can we help unlock the value of your analytics and marketing data?

Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Cross-device attribution challenge you can’t ignore

Attribution across devices is one of the major measurement challenges for digital advertising particularly affecting how mobile and upper-funnel activity is valued. Many choose to ignore the problem and plump for guesswork. Is this wise? And what are the best strategies for dealing with this key source of measurement bias in cross-device marketing?

What exactly is the problem here?

The challenge is simply that we cannot measure actual user marketing influences before sales when users switch devices. For small purchases, most people research and buy in a single visit on a single device. But for a larger more complex purchase, such as a holiday, TV, or an insurance policy, often a little research is done on the phone, maybe a bit more on a tablet, and then the purchase might be made on a laptop.   

A website visitor is anonymous unless they explicitly sign in at some point. So to be visible as the ‘same’ person on more than one device, users have to be an existing customer already. Heavily used online services like Amazon, Google and Facebook have done a good job of streamlining user management but the norm for the rest of the internet is sites cannot tell whether visits from different devices are made by the same person or different people. This is a major challenge for advertising measurement since an ad click followed up by a purchase on a different device shows up as the ad as leading nowhere and having no apparent ROI.

This measurement bias generally gets worse with more complex and expensive purchases which involve ‘upper funnel’ channels and have longer multi-touch paths to purchase. The early touchpoints are already penalised by last-click attribution, and then the cross-device challenge penalises them even further.

Cross device attribution

How big is the issue?

Comscore produces global stats on this using their usage panels and reports that globally around 46% of users use multiple devices within any given month, but the figure is much larger in the more developed market at around 50-60% in countries including the US, Germany and UK. The more people use multiple devices in this way, especially during a purchase process, the greater the issue.

Although the growth in the devices is a relatively recent phenomenon, the tracking issue is not new. For as long as internet use has been the subject of research, analysts have worried about ‘home versus work’ use whereby someone might do some shopping research during a lunch break and then pick it up later at home – in fact, this is still a major measurement issue. Also, note that the cross-device issue is strictly a cross-browser issue i.e. a user who switches browser or app on the same device cannot usually be linked across events either.

The bias in marketing measurement is clear: user journeys appear much shorter and less multi-touch than they really are.

Most companies still overvalue the last click and undervalue earlier touchpoints. With cross-device switching, even a first click positional model will give too much credit to the last click. This is because a single click is both first and last, and for many advertisers, these are the largest single type of user journey visible in their data. In reality, many of these single touch journeys will be from users who have visited before, maybe even very recently, but just on another device.

Multi-touch journeys will also get broken by a switch to a different device. Maybe you make 2-3 clicks doing some research on your phone, then 2-3 more choosing and then purchasing on another device, with a short break in between. Once again the first few touches via upper funnel channels get undervalued and their true contribution to marketing ROI is partially hidden.

How to ‘solve’ cross-device attribution

Even just thinking about taking this issue into account is a major step that the majority of advertisers don’t take.

The first step is to consider how big a problem it is for your business specifically. As with other attribution issues, the more considered and complex purchases, often the higher value ones like holidays, high tech or financial services, tend to have a long path to purchase and therefore a greater attribution challenge. You can use published research to get cross-device estimates but it better is to get some idea of your own customers’ cross-device behaviour, for example looking at the cross report in Google ads reporting which leverages the benefit of their own cross-device tracking.

Many companies have some customer tracking across devices thanks to user login and sign up apps, and email/ CRM – and while this data is heavily skewed to the purchasers (more on this below….) it does at least provide a window into your customer cross-device behaviour which you can use to explore just how big a challenge this is for your attribution.

Since most companies just ignore the issue, educated guesswork may actually be a better than average approach. In an attribution course which I periodically run we do an exercise where participants estimate the size of the cross-device bias, simply by considering the proportion of sales that are likely to be affected by the issue and using this estimate to up weight upper funnel attribution models. Maybe you guess around 40% of your sales involve multi-touch cross-device journeys. This suggests that when comparing first and last click models, the shifts that occur for each channel whether up or down, are weaker than they should be – in reality shifting by a factor of a further 40% or so.

This kind of simple analysis may be enough for you to give an apparently low ROI channel the benefit of the doubt, as your estimates could show the channel driving more upper-funnel activity than initially appears.

Device graphs and other technical solutions

The main thrust of technical solutions fall into either ‘deterministic’, or ‘probabilistic’ methods and are generally a mixture of both. This is not as scientific as it sounds. Deterministic means you can actually track a user using some kind of technology, while probabilistic means you make inferences (guesswork) most robustly and correctly possible given the data you have. Crucially the probabilistic approach depends on some level of deterministic linking since it relies on using information about ‘known’ cross-device behaviour to try to infer the unknown cross-device behaviour.

Cross device attribution

So the basic idea is to link as many users as you can using a tracking database, and then make a well educated statistical guess as to the rest.

When you consider a cross-device solution you will no doubt encounter platforms that promise that you can leverage their massive ‘device graph’ database to join users up. It sounds like they have all the information you need. A ‘graph’ in this context means a dataset describing a network of relationships, in this case between different devices. The tactic employed by these companies is to draw on data from multiple companies and networks to work map cookies to a wide set of more robust and persistent login based IDs. They, therefore ‘know’ that when there are visits from two different browsers, they actually belong to the same user: this is deterministic linking.

Technology providers also use their linked data to train a model to predict what happens when these ID mappings are not available, and which visits are likely to be associated with other visits from other browsers and locations, and following a similar pattern as observed in the deterministic linking. This modelling process is called probabilistic linking.

Before you decide to shell out the large sums required to use these solutions, there are some really major challenges you need to be aware of.

GDPR and user consent, and other challenges

First, the only way a third party can track someone who is not logged in by mapping IDs based on shared login and tracked users from other sources. This type of large-scale mapping is almost certainly a violation of user privacy and identity under the GDPR legislation, and it is only a matter of time before these platforms have to delete this data. User data which has been properly consented to is likely to be very small relative to the total universe of users out there. After all, would you consent to a company you deal with sharing cross-device usable data with a whole network of other companies?

Second, the data gets out of date quite quickly. Users frequently change logins, and suppliers, and also change and upgrade devices. So a large proportion of users who show up have not been already linked to the device graph ID set, which is only ever a small subset of the total number of users who login in. They cannot track everyone, so, for the most part, they have to estimate.

This brings us to the third issue – and this is actually the biggest one – which is that claims about probabilistic linking are almost certainly overstated. Claims about highly accurate matching rates tend to fudge the difference between precision and recall (look these terms up if you want to understand more). When you dig into the problem, a basic fact hits you square in the eyes: there are many occasions when users do something and then switch devices, and many occasions where users do the same thing, but then do not switch devices. No amount of probabilistic modelling can change this fact.

For example, suppose I see 100 people make 4 clicks on a mobile device, and my data tells me that there is a 3% chance that their next click is on a desktop. This suggests that 3 people should now be modelled as switching to desktop. But which 3 people? There is no way of knowing from the data you have. If you knew, you wouldn’t be using probabilistic linking in the first place! In technical terms, the ‘recall’ is fine, but ‘precision is very weak.

DIY linking

Deterministic linking is clearly superior since this is where we simply have all the data we need to match user to user on different devices. What solution providers do then is to effectively offer deterministic linking based on their database, with probabilistic linking used as a fallback option to ‘plug in the gaps left by this method.

Again, if you are piggybacking on someone else’s user data, then there are major privacy concerns to consider. However, it is worth noting that most companies already have some data allowing them to join up users across devices – what we might call ‘Do It Yourself’ (DIY) linking. For example, if your website asks users to log in each time, and collects an email address, then if they read their email from you on a phone, you can potentially match them from device to device. It seems there should be a way to leverage that.

Of course, the challenge is that this is always going to be a limited percentage of users, representing a big gap in your knowledge of cross-device usage. However deterministic linking is never 100% complete. So one way to leverage it is to try your own probabilistic matching, using the data you can match to make inferences about the data you cannot match.

If you do go down this route you should be aware of another major challenge with this partially linked data, which is that it’s heavily biased towards fully signed up customers and against non-customers. It would be very easy to use your own sign-in data to conclude that people who buy from you have complex cross-device usage, whereas people who do not buy from you have simpler single device interactions. The problem with this is that the people who sign up and thus become trackable tend to be the buyers, and so you inevitably have more visibility on their complex user journey data than for the non-buyers. This kind of data bias can easily generate misleading conclusions.   

An alternative solution

Here is where you have to forgive me for plugging our own Metageni solution which we call a ‘cross-device inference model’. We are interested to know the feedback from the community, so let me explain the principles.

The basic idea is that the known or ‘matched’ cross-device data can be viewed in both its matched and non matched forms, and we can observe how the process of matching itself changes the data distribution. The distributions of interest are relevant data features such as the user path length, device use frequency, and device position in the user path. We use this information to resample from our raw data, to create a sub-sample of non matched data that has a similar distribution of these features.

Thus, unlike probabilistic matching, we give up on the attempt to somehow create new cross-device data out of the unknown and instead settle for trying to make the overall sample more representative of what the complete cross-device data set would look like.

For example, we might find that when known data is linked, there tends to be a reduction in single clicks from tablet devices, as these get matched to multiple clicks on other devices. Let’s say these types of interaction fall by 10%. So before we use the raw data which has no matching ID, we create a subsample that randomly drops around 10% of these single click tablet interactions. We do this but for many different features of the data which we know shift around when the data can be matched. We end up with a data set that is not fully linked across devices, but which includes only non-linked data which is considered representative.

We would love to hear what other experts think about this less aggressive and more GDPR compliant approach.

Cross-device use continues to evolve but is not going away

Whatever you decide to do, hopefully, you can now see that it is best not to ignore the problem. Recent research suggests we may be past the peak of cross-device usage for some users who now just use smartphones for almost all their internet use. Mobile has become dominant over desktop, vindicating those companies that adopted a mobile-first strategy a few years back.  

However cross-device use is in many ways a symptom of a continuing rise in multi-task activity, as we sit listening to music on Spotify, watching a show on YouTube on the TV and playing with holiday ideas on our mobile phone, often all at the same time. Measuring how users move through their digital universe continues to be vital to understanding their behaviour. Marketing analysts ignore this problem at their peril.  

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering custom solutions for in-house capabilities.

Please email us at hello@metageni.com

Build your own attribution model with machine learning

Sounds too good to be true? Maybe so, but as machine learning and cloud data technology become more accessible and scalable, building your data-driven Multi-Touch Attribution (MTA) model is becoming increasingly realistic. The advantage of a predictive machine learning model is that it can be objectively assessed based on a blind hold-out data set, so you have clear criteria as to whether one model is ‘better than another.

What you need for an in-house approach

First, you need data and lots of it. Fortunately, digital marketing is one of the most data-rich activities on the planet. Chances are that if you are a B2C online business, you have hundreds and thousands, or else millions of site visit logs generated every week. These logs contain referrer data which helps you analyse and understand the customer journey to sale, the first essential step in building your attribution approach.

Second, you need a solid experienced data science and marketing analytics team. Go for a mixture of skills. Typically, some data scientists are strong on theory, but weaker on the application, while others are great communicators and see the strategic angle but are weaker at data-related programming. You also need domain expertise in marketing analytics. You need visualization experts and data engineering experts. The fabled ‘unicorn’ data scientist is impossible to hire, so instead, you should go for a team with the right mix of skills, with strong leadership to move the project forward.

Third, you need patience. The truth is, getting to an attribution model using machine learning is not easy. It is not a case of throwing some data at a model and waiting for the answers to pop out by magic. Your team needs to decide what data to use, how to configure it as a feature set, what types of model to use, what an appropriate training set is, how to validate the model and so much more besides. You will need to make multiple attempts to get to a half-decent model.

Choosing the final model

The best candidate ML models depend on your data – we have had good results with well-optimized classifiers and regression models, which we find often outperform even high order Markov models. While a black box or ensemble method may get better predictive accuracy, you need to consider the trade-off in terms of reduced transparency and interpretability. The best advice is not to commit to a particular modelling approach or feature set too early in the process, but to compare multiple methods.

But what then? An advanced machine learning model does not speak for itself. Once you have a model, you then need to be able to interpret it in such a way as to be able to solve the marketing mix allocation problem. What exactly is the contribution of each channel? What happens if spend on that marketing channel is increased or decreased? How does the model handle the combined effects of channels?

All of this will take months, so it is a small wonder that many companies ignore the problem or else go for a standard vendor out-of-the-box approach. It’s worth remembering then that there are some key benefits of a, ‘do it yourself approach to consider.

Benefits of an in-house model

Own attribution model

If you create your model, you will discover a great deal about your marketing activity and data in the process. This can lead to immediate benefits – for example with one major international services firm we worked with we found significant marketing activity occurring in US states and cities where the company has no local service team. Even with no attribution model defined at that stage, the modelling effort uncovered this issue and saved the company huge sums right away. The point is that your data quality is tested and will become cleaner and more organised through the process of using it, and this, in turn, supports all your data-driven marketing.

Another beneficial side effect is that if you create your attribution model you will also learn about your business and decision making. This process will force your marketing teams to collaborate with data scientists and engineers to work out how to grow sales. Other teams need to be involved, such as finance, and your agencies, and this will often spawn further opportunities to learn and collaborate across all these marketing stakeholders.

Attribution is all about how different marketing channels work together, so your various marketing teams and agencies need to collaborate as well – social, search, display as well as above the line, and brand and performance more broadly. Again, this provides intrinsic and additional value over and above the modelling itself.

Finally, it is worth pointing out that you will never actually arrive at the final model. This is quite a fundamental point to bear in mind. By its nature, a machine learning approach means you need to train the model on fresh data as it comes in. Your marketing and your products are also changing all the time, and so are your customers. So really you need to build a marketing attribution modelling process more than you need to build a single attribution model.

So, go ahead, build your model, be less wrong than before, and then when you have finished, start all over again. As we say at Metageni, it is all about the journey.

Gabriel Hughes PhD


Can we help unlock the value of your analytics and marketing data? Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Your attribution model is unique – what are the best practicies?

‘What attribution model should we use?’ is now a question being asked not just by marketing analysts, but by the CMO and even the CDO, CFO and CEO.

It would be fantastic if there was a simple formula to answer that question but of course, life is never so easy. Here are some simple guidelines you can follow to help you answer this question.

Differences in attribution reflect differences in your business

If your business offers a higher value considered purchase, like a holiday or a financial product, then you should expect a longer more complex research to purchase journey, and therefore a higher value given to the earlier touch points. This means attribution models with longer ‘look back’ windows and rules favouring earlier clicks.

If your product is in an especially competitive market, with lots of similar alternatives available to your customers, then you need to focus on the research and comparison phase, again valuing earlier clicks, and thinking about how to address the cross-device attribution challenge. Users who shop around research on their phones and tablets, and flip through several options, before settling on their final choice. Your product needs to be part of that journey so if you are to have a chance of being selected then leaving it all to the last click is leaving it too late.

If your business relies heavily on repeat custom, then you should attach higher attributed value to new customer acquisition and retention, exploring a cautious application of lifetime value. You cannot assume customers will stay loyal without work, but a repeat purchase is almost always easier and less costly to achieve than the first one.  

Do you have a diverse portfolio of different products, targeting different customer needs and with a wide range of price points? In this case, you may need different models for different product categories. It sounds complex, but if you think about it makes no sense to treat the buyer of (say) an expensive hi-fi system the same as the buyer of a replacement phone charger. Different customers also behave in different ways. Customer segmentation should therefore map on to differences in marketing attribution.

Is your market heavily driven by brand perception and do you invest in TV, outdoor or print? In this case, you will need to explore how to link above the line analysis to your digital attribution.

And so, it goes on…. The truth is, each business is unique, with unique complexity around the product offer, their customers, how they engage, and the value of each sale. The only way to address the uniqueness of your business is to develop appropriate unique attribution models.

Incorporate these unique features into your model

Your attribution model is unique

Once you accept that your attribution challenge and therefore your attribution model is unique, everything becomes easier. For example, instead of plugging into a standard tag or data collection framework, you can leverage the special and unique features of your data as inputs into your model.

You can structure your attribution model to reflect the different customer journeys that you see for each customer segment and product group. As you learn more and more about your customer journeys, your data-driven modelling can adapt.

At Metageni we believe a customised data-driven approach unique to your business is the only way to get to grips with this complex business challenge and turn it into opportunities to increase marketing efficiencies and grow sales. You will not look back!

Gabriel Hughes PhD


Can we help you with your custom attribution model? We collaborate with our clients’ organisations, helping marketing & data science teams create integrated on & offline investment analytics solutions for optimising sales growth.

Please email us at hello@metageni.com

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ClickGeni

Digital click & cross-device attribution for efficiency & growth

People shop around when they buy online, so you need to ‘attribute’ (value) each click, to avoid wasting marketing money and missing opportunities. Imagine if you could correctly value every click and every marketing channel in the journey based on how it changes the probability of sale?   

Accurate attribution using custom machine learning is our speciality since Metageni founder Gabriel Hughes developed the original positional attribution modelling tool in Google Analytics, while he was at Google.

Since then ‘data-driven’ models have become possible but the challenge is making sure these are relevant to your business, and not just a one size fits all black box model. Your attribution model is unique.

Key benefits –

We use your first party analytics data to analyse all clicks to your site, so can include paid and organic search, social platforms, affiliates, email, video – indeed any visit to sale driver available. If you are not sure what we can measure, just ask: this is a custom build model.

Do you know how accurate your attribution model is? Our models have high levels of objective accuracy measured on hold out samples, so you can see if yourself how good the model is, and reallocate your marketing spend with confidence.

Attribution by device leveraging your own tracking and our cross device inference model

Most attribution models treat all customers and purchases the same, which makes no sense. Adding in your customer and product data makes your attribution models even more accurate and yields valuable insights, so you can better understand how to target your marketing to different groups of potential buyers.

If you track call centre sales, we can attribute these just as well as ecommerce transactions, and have worked with most of the major call tracking providers.

This is machine learning but not ‘black box’ modelling. Metageni have developed an approach that translates your custom model into a set of rules so you can see not just how much value marketing drives, but how and why it does this, so you have deep insight into your marketing effectiveness.

We use custom machine learning trained on your data so you measure without relying on media tech platforms or agencies to ‘mark their own homework’. This gives you confindence to invest your marketing budgets wisely.  

We can run the model reports through our in house reporting system or you can use ours. We can also make attributed visit level signals available for your marketing tools such automated bidding. We can produce dynamic reports for any of the major visualisation tools and will create custom reports your business as needed, so you have exactly the data you need to take action.

Is this for you?

  • ClickGeni is a custom designed and built attribution model and reporting pipeline built on your first-party analytics data.
  • Combine with MediaGeni econometrics to incorporate impressions and offline effects.
  • We work in close consultation with your marketing and analytics experts.
  • We offer free workshops around customer journey challenges, for brands who may be interested.
  • Interested to know more? Contact us:  hello@metageni.com

Learn how other clients have worked with us