Marketing Measurement for 2022 and Beyond

Kelly Sanderson
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There has been a lot, and I mean a lot, of discussion in the advertising industry over the past three or four years about tracking and measurement. Due to increased customer privacy concerns from global governments and policy changes coming from large tech organizations (looking at you Apple), many of the digital tracking methods we’ve all come to love and loathe are becoming obsolete. These changes are forcing marketers and ad agencies to re-think their evaluation of media and how they attribute their efforts.

Currently the industry is evaluating two approaches.

Let’s look at each of these approaches in a bit more detail.

cookie-based online tracking
First Approach: Keeping it one-to-one

One of the main reasons digital advertising channels were embraced so quickly by advertisers, particularly social media and programmatically bought display/video, was that it allowed marketers to do what they had long been dreaming of; reach the right user, at the right time, with the right message… then track them from site to site and back to their own until they converted to a sale. And for a while the dream was alive and well. The level of data that publishers, ad tech providers, brands and ad agencies were able to collect from online users was unlike anything they’d seen before. Performance marketing thrived (often at the expense of branding), cookies were dropped into browsers at will and mobile SDKs were stuffed with location trackers.

However online users and lawmakers started to realize how much online activity was being tracked and monitored leading to tough privacy laws like GDPR and CCPA being introduced. Some browsers stopped allowing third-party cookies and Apple introduced its App Tracking Transparency update which required iPhone and iPad users to expressly consent being tracked by app makers. All these changes have been a major disruption to the ad industry and seem to signal the end of an era for user tracking, but the ad industry isn’t ready to give just yet. The industry has gotten used to having user level insights and that’s hard to give up. So, in the face of losing third-party cookies, there has been a major push to come up with an alternative user tracking method.

In the quest for user level data, several identity-based tracking solutions have emerged. These solutions include:

  1. The Trade Desk’s UID 2.0. At a high level, UID 2.0 works by changing an online users email address into an alphanumeric identifier (a universal ID) which can then be used to connect their activities online as they go from site to site.
    • The program is reliant on having a neutral organization host the program. It looked like IAB Tech Lab would take it on but they have recently declined.
  2. LiveRamp’s ATS. LiveRamp’s approach is to create a user identity graph that utilizes a mix of user identifiers such as email,  phone #, username etc. to create an encrypted identifier that can be used to target online users as they surf the web.

These solutions are promising, and are getting quite a lot of adoption but as we see companies like Apple giving its users the option to hide their email address when registering with a website or making a purchase, it brings up questions as to how sustainable an email-based identity tracking program could be.

Seems like a lot of work is going into this. Why is having user identity important?

Two reasons. First, user ids are the foundation of programmatically bought digital advertising. To show an ad to the right person, at the right time… well you get it, you need to know who that person is and what they are uniquely interested in. This requires identity tracking in some shape or form.

The second major use case is multi-touch attribution (MTA).

multi-touch attributionMTA is the process of giving each touchpoint someone has with your brand credit for the ultimate sale or conversion. Even if it’s only a fraction of the credit. There are many different types of models a brand may use (linear, time decay, U or W shaped, etc.) but the goal is generally the same, understand which online marketing efforts the user was exposed to before converting.

For the last decade ad agencies have looked to MTA to help gain better insights into the online user’s path to conversion and provide guidance when optimizing campaigns. Unfortunately, the data used in MTA is incomplete and can lead to incorrect assumptions about which channels are generating conversions.

Why incomplete?

So, while MTA is still useful for providing media buyers with direction on which channels to buy or targeting methods to deploy, they hardly give a complete look at the user journey. For that, marketers would need to turn to a marketing measurement that encompasses all the factors that got into a purchase. It’s time to go old school but with an updated twist.

Second Approach: Updating Tried and True Methods

To truly understand how each marketing tactic a brand has in market is contributing to its overall sales, advertisers need to utilize a measurement methodology that includes both online and offline media but also considers the impact of non-media factors like COVID, weather, sales, competitive activity, etc. This is where Marketing Mix Modelling (MMM) comes in.

What is MMM?

MMM has been around since the 60’s and since then its main purpose has been the same, to help advertisers understand how different factors in their marketing mix impact sales. Technically speaking, MMMs are ‘a statistical analysis that uses multivariate regressions on sales and marketing time series data to estimate the impact of various marketing tactics on sales then forecast the impact of future sales on a set of tactics’ (thanks Wikipedia!). In other words, MMMs look at how various media channels and external factors have affected sales for that specific brand or product in the past and then it uses those insights to forecast future revenue.

The equations used in MMMs go far beyond a few simple ROI calculations. MMMs include things like ad stock, impression volume, ad saturation/diminishing return levels, ad frequency and more. MMMs can also be used to compare the return on ad spend (ROAS) of each media channel at a high level or break it down by targeting method, creative execution or by ‘walled-garden’.

Media Mix Modelling Channel Attribution

MMM’s are also naturally privacy compliant since no unique customer data is required to create one. Also, if an advertiser or agency doesn’t have sales data success proxies such as brand lift, website traffic, footfall traffic or even search volume can be used.

Not your Grandfather’s MMM

Although marketers have been using MMMs for decades they have evolved dramatically over the years, particularly in the past 5. In the past, MMMs were expensive to develop and were a static analysis of a moment in time. This meant only the largest brands were able to commission them and, while the insights were valuable, they quickly became obsolete. Thanks to advancements in computing technologies and APIs, tech providers, agencies and advertisers now have the capability to create MMMs that generate ROAS insights in near real-time at a  much reduced price point..

Sounds great, right? But there are a couple things to note:

  1. Marketing mix modeling is a backward-looking analysis so if an advertiser is looking to add a new media channel to the campaign mix it won’t be able to predict its impact on sales. If a brand wants to see how a new channel will impact sales, they will need to run a campaign on it first then (generally for a month or so) to test its effectiveness.
  2. MMMs can tell advertisers a lot about the impact each media channel is having on its bottom line but deep media knowledge or an experienced ad agency partner is still required to make tactical recommendations on media placement, targeting parameters, execution and more.

The aim of an MMM isn’t to replace MTA but to augment it. Using the combined power of both approaches, brands can create a unified marketing strategy with greater insight into the most effective ways to reach their customers.

In Summary

Weathering the many challenges that come with a ‘digital only’ approach to campaign analysis (ad blocking, fraud, a patchwork of local and global privacy legislations, third-party cookie loss and a series of social media measurement inconsistencies, etc.) has left many marketers looking for an alternative, future-proof way to measure the returns of their marketing efforts.

As we move forward we are likely to see advertisers, like AMEX, using modern, actionable MMMs as the foundation of their marketing analysis and campaign planning.



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