Category: Attribution Handling

The what and how of Attribution in Digital marketing

The what and how of Attribution in Digital marketing

This article is a contribution from Pranav Thapak, Management Trainee at HDFC Life 

Today’s marketers rely on multi-channel strategies to carry out marketing campaigns, both online and offline. While this practice enables marketers to customize the customer journey along every step, it also presents unique challenges once it’s time to analyze the overall impact of a particular campaign on marketing ROI.

What is Marketing Attribution?

The Short Definition: Put simply, marketing attribution is the analytical science of determining which marketing tactics are contributing to sales or conversions.

The Long Definition: Marketing attribution is the practice of evaluating the marketing touchpoints a consumer encounters on their path to purchase. The goal of attribution is to determine which channels and messages had the greatest impact on the decision to convert or take the desired next step. There are several popular attribution models used by marketers today, such as multi-touch attribution, lift studies, time decay, and more. The insights provided by these models into how, where, and when a consumer interacts with brand messages allow marketing teams to alter and customize campaigns to meet the specific desires of individual consumers, thus improving marketing ROI.

Why is Marketing Attribution Important?

Advanced marketing attribution programs require marketing teams to aggregate and normalize consumer data from across channels to ensure each interaction is properly weighted. For example, if a consumer is exposed to a display ad and an email campaign, but only converts after seeing a special promotion in the email, marketers can note that this piece of collateral played a bigger role in driving the sale than the display ad. They can then devote more resources to creating targeted email campaigns.

To achieve the level of data granularity required for effective attribution, marketing teams need advanced analytics platforms that can accurately and efficiently distill big data into person-level insights that can be used for in-campaign optimizations.

Benefits of Marketing Attribution

Advanced attribution models can be time and resource-intensive to get right, especially complex models that evaluate a variety of datasets for online and offline campaigns. However, when done effectively, attribution brings a myriad of benefits including:

Optimized Marketing Spend

Attribution models give marketers insights into how marketing dollars are best spent by showing touchpoints that earn the most engagements. This allows marketing teams to adjust the budget and media spend accordingly.

Increased ROI

Effective attribution enables marketers to reach the right consumer, at the right time, with the right message – leading to increased conversions and higher marketing ROI.

Improved Personalization

Marketers can use attribution data to understand the messaging and channels preferred by individual customers for more effective targeting throughout the customer journey.

Improve Product Development

Person-level attribution allows marketers to better understand the needs of their consumers. These insights can then be referenced when making updates to the product to target the functionality consumers want.

Optimized Creative

Attribution models that can evaluate the creative elements of a campaign allow marketers to hone messaging and visual elements in addition to better understanding how and when to communicate with users.

Common Marketing Attribution Challenges and Mistakes

While marketing attribution can offer many benefits, there are a host of common mistakes that can result in misattribution, obscuring the success of campaigns for marketers.

To ensure they are getting the most accurate data that reflects their users’ customer journey, marketers should avoid:

Correlation-Based Bias

Attribution models can be subject to correlation-based biases when analyzing the customer journey, causing it to look like one event causes another, when it may not have.

In-Market Bias

This refers to consumers who may have been in the market to buy the product and would have purchased it whether they had seen the ad or not. However, the ad gets the attribution for converting this user.

Cheap Inventory Bias

This gives an inaccurate view of how media is performing, making lower-cost media appear to perform better due to the natural conversion rate for the targeted consumers, when the ads may not have played a role.

Each of the biases threatens to have marketers make optimizations in favor the less effective messaging, causing immense damage to ROI.

Digital Signal Bias

This occurs when attribution models do not factor in the relationship between online activity and offline sales. For marketers who make sales both online and offline, they must make optimization decisions based on both online and offline data, not only what they can trace digitally.

Brand & Behavior

Attribution models can often overlook the relationship between brand perception and consumer behavior, or will only look at them at a trend regression level.

Marketers must ensure their attribution models are able to detect relationships between brand-building initiatives and conversions. Not understanding how their attribution model measures branding impact is a common and detrimental mistake, leading marketers to make decisions based on incomplete recommendations that devalue brand building.

Missing Message Signal

Creative and messaging are just as important to consumers as the medium on which they see your ad. One common attribution mistake is evaluating creativity in aggregate and determining that one message is ineffective when in reality it would be effective for a smaller, more targeted audience. This emphasizes the importance of person-level analytics.

Pranav Thapak – You can read the full article also here.

Can you trust the results your advertising platforms are reporting?

Can you trust the results your advertising platforms are reporting?

This article is a contribution from Trevor Testwuide, CEO Masured

For the past few years, an endless stream of policy updates from tech giants like Apple and Google have held DTC marketers in an advertising undertow. Just as they gain a semblance of stability, having adapted their strategies to the latest requirements, another headline hits and pulls them back into a sea of chaos. 

At Measured, we are committed to providing brands with ongoing insights that can always be trusted. Our experts are continuously analyzing the potential impact of inevitable industry changes on the ability of advertisers to measure the performance of their ad campaigns.

Based on data from across our portfolio of DTC brands, running thousands of media incrementality experiments on multiple channels, there is only one viable option to escape the breaking waves for good. Marketers need a reliable system of measurement, based on their own source of truth transaction data, that is independent of platform bias and not susceptible to damage from rapidly degrading identity resolution and user tracking capabilities.

We’ve come full circle: why are we talking about last-click again? 

If we’re being honest (and that’s just who we are), relying solely on attribution and conversion lift reports delivered by the platforms you are paying for advertising was never a comfortable approach for anyone. It’s understandable to be concerned that platforms reporting on their own performance might inflate their results. By relying on last-click attribution, platforms have lived up to that skepticism. When every platform claims 100% of the credit for every conversion it was in the path of, the numbers will not add up.

Multi-touch attribution (MTA) promised to reconcile conflicting last-click performance reports with user click-paths, built by following individuals around the internet and assigning a percentage of credit for each touchpoint on the way to a conversion. It sounded like a data-driven utopia for marketers, but ended up being complex, cumbersome, expensive, and really not all that effective.

We’ve been shouting about this at Measured for years; With more and more governments, companies, and platforms putting the kibosh on user-level tracking, MTA is dead

Marketers are now challenged with reverting back to platform reporting, unifying the data they can collect from all available disparate sources, and extrapolating actionable insights. But who should they trust when sales transactions, site-side analytics, and the platforms themselves inevitably report wildly different results?

Platform reporting will lead you down a dangerous path

The same issues that moved us away from last-click attribution in the first place still exist, but today’s environment is even more precarious for platforms trying to prove their worth to data-hungry advertisers. Apple’s new privacy policies, Google’s decision to eventually kill browser cookies, and all the privacy dominoes falling in the wake of these titans are slashing visibility for walled gardens like Facebook and causing a breakdown in their ability to provide usable attribution and lift reports to advertisers.

Facebook recently indicated that the platform’s ad measurement and reporting systems are suffering accuracy issues related to Apple’s iOS updates. Facebook believes that “real-world conversions, like sales and app installs, are higher than what is being reported for many advertisers.”

The incrementality experiments we’ve been running prove that Facebook’s theory is correct. While Facebook’s attribution reporting has gone down significantly since the rollout of iOS 14.5, by reconciling experiment results with source-of-truth transaction data from the brand, Measured has revealed that the incremental contribution of their Facebook campaigns has remained consistent.

Performance isn’t suffering. Reporting is.

This is not an isolated incident

While Apple was the most recent to implement (and enforce) new policies and Facebook, as the second-largest online advertising platform in the world, has received the most attention about collateral damage for advertisers, this is the new reality for all advertisers – on all platforms.

As an example, we just reviewed the results of OTT incrementality experiments for a large fashion brand advertising on the Roku platform. The results indicated that Roku was significantly underreporting the performance of their ads and the value they contributed to the brand’s business. If the brand had relied on the reports from Roku alone, they may have made decisions about very large advertising investments based on false information.

The variety and pace of change happening in this space are having some level of impact on measurement and reporting for every advertising platform that exists. There is no future where data privacy policies become less restricted. Marketers need to prepare now.
To avoid the repercussions of wasting money and failing to meet KPIs, marketers need an independent system of measurement, tied to their own business transaction data (which the platforms do not have) to rescue them from the undertow and guide them towards smart investment decisions. – You can read the full article also here.

(VIDEO) Campaign Evaluation based on IHC Attribution Insights

(VIDEO) Campaign Evaluation based on IHC Attribution Insights

Video webinar title card: Campaign Evaluation How IHC helps evaluate the performance.
Play Video

Take a look at Campaign Evaluation based on IHC Attribution Insights

This video focuses on the IHC attribution model, to find out how to evaluate marketing campaigns based on IHC results: along with some examples, let’s find outdifferent perspectives for  campaign performance evaluations.

You can also take a look at the IHC attribution introduction video and the results analysis and basic insights.

Attribution is changing-How can you prepare?

Attribution is changing - How can you prepare?

This article is a contribution from Gabriel Hughes PhD, CEO Metageni


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?

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 Visitor 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.

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? 

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. To get in touch with us: or click here – You can read the full article also here.

(VIDEO) IHC Attribution

(VIDEO) IHC Attribution

Video webinar title card: IHC Attribution - The new gold standard in multi-touch attribution
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First contact with the IHC Attribution model

We want to introduce you to the core concepts of the IHC attribution model, which is going to push multi-touch attribution modelling to a new standard. See what makes IHC a customer interaction phase attribution model and what are its main advantages.

You can also find out how to analyze results and basic IHC insights in this previous post, or even see them in action checking this case study.


(VIDEO) Case Study

(VIDEO) Case Study

Video Case Study IHC Attribution
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Check the IHC Insights in action

The IHC attribution model has demonstrated to improve marketing decision-making and ultimately performance. This case study shows how marketeers decided to reduce the budget for certain channels following Google Analytics results. But IHC attribution analysis drove them to different conclusions. How did IHC help them and what were the results?

You can also check a previous post about IHC Attribution results analysis and basic insights.


(VIDEO) ITP and how the Server-side Tracking can help us

(VIDEO) ITP and how the Server-Side Tracking can help us

ITP server side tracking help thumbnail
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Take a look at ITP and how Server-side Tracking can help us

In our new video we’ll talk about ITP and find out which is the best event tracking option for an optimal data collection. For us, building good customer journeys is essential for a smart attribution. Client-side or server-side tracking? Find our winner here, and let us know if you agree!

You can also take a look at the IHC attribution introduction video and the results analysis and basic insights.

(VIDEO) Basic IHC insights

(VIDEO) Basic IHC Insights

Video webinar title card: IHC Attribution- Results analysis and basic insights
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Take a look at IHC Attribution Basic Insights

This time we analyze the IHC attribution results and which information we can obtain from the IHC view on attribution and customer journey analytics.

You can also check our previous post introducing the IHC model clicking here, or even see them in action checking this case study. 

(VIDEO) Event Data Tracking

(VIDEO) Event Data Tracking

Video webinar title card: Event Data Tracking - How to store and collect data, then benefit from the intel
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Take a look at the Event Data Tracking video

Here’s a quick intro into event data tracking for ecommerce sites: How to collect, store, and ultimately benefit from the intel. There’s lots to consider when building  a good and reliable data system.

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