A Comparison Of Marketing Measurement Approaches In A Cookieless World

Founder and CEO of the company SegmentStreama conversion modeling platform that solves marketing analytics in a cookie-free world.

There are many different approaches to marketing measurement that help businesses understand which of their campaigns have the most value and how to allocate budgets to maximize ROAS. The most known of them are:

• Multi-touch assignment.

• Incremental testing.

• Marketing mix modeling.

• Transformation modeling.

Each approach is great in its own way, but there are certain limitations that prevent it from providing adequate results. Before we examine marketing measurement approaches, let’s look at the current context in which they must operate—a world without cookies.

What is a world without cookies?

A cookie-less world is a term that describes the current state of marketing measurement approaches that will only get worse in the future. These include the following restrictions:

Restrictions on the use of cookies

The cookie is the holy grail of marketing measurement. With the help of cookies, it is possible to observe user journeys and understand which channels led them to conversion.

However, the lifetime of cookies is currently limited. For example, on safari, they only last up to seven days in some cases. This means that if a sale occurs after seven days, it will not be attributed to the initial cookie and upper funnel channels will not receive credit.

Furthermore, cross-device and cross-browser tracking is impossible. Oftentimes, opening channels is mistakenly given no value.

Tracking limits

The internet is moving towards a privacy-focused place, so there are certain tracking limits that ensure users have a better and safer experience on the web.

Also Read :  Gavin Williamson, UK minister, resigns from post following bullying allegations

For example, some browsers block third-party cookies by default, while some do not allow cross-site tracking.

I compare common data-driven methods for marketing measurement and examine whether they are capable of delivering adequate results in the current context.

Multi-touch documents

Multi-touch attribution (MTA) models are supposed to determine and assign value to each touchpoint in the conversion journey. MTA models require a single key to wrap all touchpoints in a trip—a cookie or a login ID.

However, MTA models show similar results to single-touch models. The entire customer journey is not visible and only lower funnel channels receive credit. Therefore, marketers do not get enough accurate data to make their strategic decisions.

Incremental test

Simply put, incremental testing is an A/B testing method that measures the incremental value of a marketing strategy. The main difference is that it divides the target audience into two experimental and control groups. The control group is exposed to the marketing activities that need to be measured and the experimental group is not exposed to anything.

Incremental calculation involves counting the conversions that happened thanks to the marketing campaign against those that would have happened anyway.

There are cookie-based and cookie-less incremental testing tools. Cookie-based ones are more common. They divide the audience randomly, which is a good way to get fair measurement results. However, this method requires a conversion to occur in the same cookie.

Cookie-free approaches segment viewers based on geography, demographics and other parameters that are tracked without cookies. Such branches are less random. Therefore, experimental and control groups can be influenced by many factors such as sales periods or other promotional channels. As a result, testing can be difficult to run and may not provide marketers with sufficient results.

Also Read :  Socceroos coach Graham Arnold signs contract extension through 2026 World Cup

Marketing mix modeling

Marketing mix modeling (MMM) is a complete statistical analysis of sales and the factors that affect those sales over a long period of time. Macro-evaluates all budget allocations to digital and offline marketing channels (eg, TV/radio advertising, print advertising, digital marketing, etc.).

It considers several factors from market conditions to product price and seasonality. MMM can then identify spikes in sales, determine whether they are caused by the aforementioned factors, and calculate the potential impact of further activity in specific macro-channels.

MMM tools are good to run once in a long period of time (for example, once a year) for a fundamental review of all marketing activities – both digital and offline. MMM can help make data-driven budget allocation decisions across channels, markets or brands. It is a good tool for retrospective analysis, but its applications are limited to only a few situations. MMM cannot help evaluate the impact of marketing activities on the fly or at the micro level.

Transformation modeling

Conversion modeling uses machine learning algorithms to evaluate the impact of all marketing activities when it is impossible to observe actual conversions. It can help when other attribution models and marketing measurement tools fail—when conversion paths involve cross-device interactions, when first-party cookies have expired, or when in-place analytics are needed.

However, the shift to conversion modeling requires certain changes in marketers’ processes and mindsets. First, it’s important to come to terms with the fact that cookies that are still alive now don’t allow marketers to track customer journeys as accurately as they could a few years ago. Marketers should stop relying on cookies and start looking for alternatives before it’s too late.

Also Read :  Kickstarter campaign launched for open-world life simulation game The Witch of Fern Island

Second, given the aforementioned limitations, marketers should stop trying to calculate ROAS for each channel. Calculating this metric at the channel or campaign level is nearly impossible and will become more complex in the future. Instead, start calculating ROAS for the entire marketing mix to avoid improperly optimizing the marketing mix itself.

It’s important to understand that using machine learning is an important part of adapting to a future that feels like a black box due to privacy laws. Marketing measurement tools used to rely heavily on cookies, but data collection is no longer available. Realizing that machine learning can turn small pieces of still-available data into actionable insights is an important step toward becoming competitive even in a world without cookie-cutter measurement.

Beat the world without cookies

The fact that tracking restrictions and cookie restrictions are becoming more and more strict every year, and the whole Internet browsing experience is moving towards providing more private experiences, we can expect to live in a completely cookie-free world one day.

With this in mind, marketers should look for better alternatives to conventional marketing measurement tools and approaches.

The Forbes Technology Council is an invitation-only forum for CTOs, CTOs and CTOs globally. Am I eligible?


Leave a Reply

Your email address will not be published.

Related Articles

Back to top button