Running similar metrics at scale, leveraging benchmarks, and starting to develop something more than a one-off campaign-based spreadsheet is beneficial to all parties, including agencies, publishers, and marketers. Perhaps it’s time to move away from talking about data and instead focus on metrics.
In today’s world, analytics is the single most significant part of digital marketing. As a marketer, having data that reveals how the campaign succeeded is a must-have that will guide the decisions around advertising for the foreseeable future.
But what if the data is ineffective in and of itself? Many campaigns produce a large amount of data but provide little insight; they focus on “delivery” data. While knowing the number of views a campaign received is crucial, brands are looking for the knock-on effect – are consumers thinking or going to buy their product as a result of viewing the ad?
Some publishers go even further, measuring the brand lift generated by some campaigns on their sites, allowing them to get a better understanding of their genuine impact. However, they may have challenges here as well.
Every publisher who opts to track a campaign this way is doing so for the noble goal of better understanding and optimizing its performance. They also have the opportunity to present themselves in the best way possible, focusing on the most impressive figures that support what they do. But, while that may appear to be a good short-term plan for that campaign and publisher, they are actually helping no one, least of all themselves.
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This ad-hoc method is costly, time-consuming, and not transparent, especially if the measurement is performed using an in-house solution. Meanwhile, the marketer is likely to receive a variety of reports and spreadsheets from the many publishers on their schedule, which they must piece together in order to develop a holistic picture of how the “premium publishers” portion of their campaign has provided campaign effect.
It’s easy to see why publishers would revert to such tried and tested approaches — they’re simply relying on data that’s easily available and the same measurement process that they have always used. However, this is of little use to the marketer.
Because different methods of measuring ad effectiveness exist, marketers may see a plethora of very impressive-looking statistics from various sources, but they will not be able to draw truly effective conclusions that will inform the next stage of their advertising because they will not be able to compare like-for-like measurements.
As a result, it’s time to stop talking about one-dimensional “data” and start talking about “metrics.” Metrics provide a universal language of performance, allowing for comparison across publishers and markets, which is critical for a true understanding of how an ad campaign worked.
Because many businesses operate globally, they should, in an ideal world, speak the same language when it comes to metrics, regardless of where they are physically located. That’s when companies start to generate data that’s priceless, allowing buyers to compare their performance to that of other publishers in the same sector, and publishers to see how they’re doing in different parts of the world, all using the same straightforward, easy-to-understand set of numbers.
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The potential to function more successfully at scale and plan more effectively for the future is the most exciting aspect of adopting global metrics. A big publisher operating in multiple markets may find that their campaigns perform better in one country but not the others, and with that knowledge, they can start assessing what factors are leading to that success and how they can apply what they have learned to their other areas.
In turn, they can share their combined knowledge with their advertisers to help them get a better return on their future campaigns. They can help their customers optimize their behavior while also attracting new customers with the promise of this accessible, standardized data and analytics by establishing a body of knowledge around what works and what doesn’t.
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