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Effectiveness Media Measurement Brand Strategy

Effective measurement should propel brands forward – not hold them back

By Liam Wade, Performance Director

Impression

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February 7, 2024 | 7 min read

The world of digital metrics is fraught with misunderstanding, says Liam Wade at Impression. Brands should measure effectiveness, as well as efficiency, for the best results.

People point at a laptop screen

The best decisions are grounded in good data, says Liam Wade of agency Impression / John Schnobrich via Unsplash

We’ve been deluded for too long in thinking we fully understand the impact of our digital activity, when the truth is that walled-gardens and limitations on web analytics made sure that we never really did. The cookie wasn’t all-powerful, even if it was ever-present. Now we’re being presented with an explosion of digital measurement techniques - some new, most old, and a huge proportion of them presented as perfect “proprietary” solutions.

But used in the wrong way, these solutions can be extremely detrimental. Whether we’re talking about econometrics, incrementality, attribution – or better, a combination of all three – the data they generate can lead to decision-making that drastically impacts business performance. And while measurement should be an enabler, all too often it leads decision-makers down the wrong path, blocks innovation and ultimately prevents growth.

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Good measurement is about more than just efficiency

The discussion about the competing merits of effectiveness and efficiency is an old one. However, renewed interest in the concept of marketing effectiveness is bringing the debate to new people. Too often measurement is considered analogous with efficiency: ask how your marketing team are measuring the effectiveness of their work and all often you will hear only efficiency metrics used in their reply. But this is just one lens through which metrics can be viewed.

Don’t get me wrong, this lens is useful. It provide immediate, quantifiable data. Using metrics such as cost-per-click (CPC), conversion rate (CVR) and return on ad spending (ROAS) can help to reduce costs. But these all rely on data generated from cookies, which are flawed and have never been holistic.

In comparison, effectiveness metrics are designed to maximize the outcomes in the first place. They are typically more long-term, including measures such as brand equity and long-term revenue growth. The truth is that not all outcomes are equal, despite the fact they may seem so. Focusing purely on efficiency metrics can negate from viewing the long-term outcomes of a particular activity.

To get the best measurements, experiment

Typically, campaigns are measured retrospectively so measurement is seen as what happens after the thing that you do. But, you can derive stronger insights by proactively designing your activity with insight generation in mind.

While attribution and econometric modeling have their place, there is no substitute for real-world experimentation data. A culture of marketing experimentation can help to assess new activity, understand consumer behavior and to thrive in an evolving market.

For marketers from performance backgrounds, testing has been used to uncover efficiency opportunities; reducing cost-per-thousand (CPM) or cost-per-click (CPC). But, fast-learning brands test digital activity in ways beyond just in-platform metrics, measuring incremental impact on tangible business and commercial outcomes too.

Collecting first-party data can give insights into key consumer attributes while protecting the data-dense, AI-driven items that advertising platforms thrive on. Robust testing through measuring incrementality can boost the reliability of future modeling, making media mix models (MMM) confident and effective. Put simply, the more you experiment, the better your predictions and decisions will become.

Data should inform clear decision-making

Even highly productive measurement strategies can be let down by the incorrect use of the insights derived. It’s incredibly important to set the correct decision cadences, based on the measurement data you use.

Media mix models can guide you in making broad budget decisions, on an annual or quarterly basis. Experiments, and particularly incrementality tests, can help you to measure effectiveness and make faster strategic decisions. And though it isn’t what it was, digital attribution data – real-time modeled or otherwise – provides an uncomplicated, quick model for daily in-platform optimizations.

Bringing these tools together, you can start to understand the short-term signals that can predict long-term brand growth. These signals have flaws, but can help you make smarter decisions before your competitors.

Prioritizing effectiveness measures in your decision-making will naturally lend itself to long-term activities that lead to increased brand growth and customer loyalty. It can also encourage organizations to be brave and seek to demonstrate impact in their choices of experiments, while being patient to measure the results. Consider, for example, two stagnant brands competing over saturated platform CPCs, while an emerging competitor launches a radical creative-testing strategy across lesser-used audio inventory, eg music steaming and podcasts.

Effective measurement, when leveraged wisely, can unlock a world of opportunity for brands. It's about setting the stage, and using a measurement ecosystem to guide strategic choice. Brands that master this art will not just keep pace but can leap forward – turning insights into actions that drive real, enduring growth.

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Effectiveness Media Measurement Brand Strategy

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Impression

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