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.

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. 

Measured.com – You can read the full article also here.

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: hello@metageni.com or click here. 

Metageni.com – You can read the full article also here.