A recent article from the adexchanger sparked an interesting debate regarding the rising inconsistencies in language, knowledge base and expectations from cross-device graph providers and the buyer side. More specially, it points out that as cross-device graph providers start to scale and match rates grow, more problems are generated.
While all the arguments discussed in the article are indeed very valid, I would like to point out that yet again there is blatant ambiguity staring back at us. Take the title for instance. What is a ‘match rate’? How is it defined? Who defines it? When did ‘match rate’ become a valid cross-device metric?
If I am asking these questions, it’s no wonder that the market feels frustrated and rather skeptical of the technology and its capabilities. It clearly indicates that the evolving cross-device market is going through a period of growing pains like many others have experienced before us. However, now that this issue has been brought to light, it is time for cross-device graph providers globally to step up and clean up their act.
The solution is simple: educate the market. We need to speak the same language. It’s by far the easiest way to overcome miscommunication and or confusion. Even more importantly, it combats unrealistic expectations over results.
If by ‘match rate’ the adexchanger article meant accuracy, I would argue that this metric is meaningless. It only indicates the map’s correctly identified matches, plus the correctly identified non-matches, out of all possible matches. In simple terms it will always be close to 100% regardless of how many correct matches are identified.
The two metrics you need to know are precision and recall. Precision is a fraction of predicted matches between devices that are indeed the actual/true ones, whereas recall is the fraction of total actual matches that are correctly identified. Achieving the ideal balance between precision and recall is one of the main goals behind device matching.
However, the market needs to understand why only asking for precision or recall scores can be more counterproductive than not. It’s not a simple one-size-fits-all solution. For some use cases, precision (correctness) is more important, whereas in others, recall (market coverage) is what counts.
Why is this? Well, cross-device graph providers can optimize the metrics in order to help you achieve your business goals. For example, performance focused agencies may prefer a more precision optimized graph, which has the highest level of precision, to ensure that they are communicating to the same user. Brand advertisers on the other hand may prefer a branding campaign to focus on more exposure and reach, which is why they might opt for a recall optimized graph where the precision can be slightly decreased.
By proactively educating the market you can close this knowledge gap and better communicate what is actually possible in order to prevent dissatisfaction. Now is the pivotal point to take action and continue to move forward with the mantra: More matches, more clarity.