Data is hailed as the marketer’s Holy Grail for a good reason -- providing marketers with the insight needed to tailor advertising campaigns helps them maximize engagement among target audiences and return on investment (ROI).
However, harnessing data effectively isn’t always straightforward. For many marketers, the abundance of data produced by disparate sources has made the task of identifying and unifying relevant insight seem colossal. Machine learning, which can take control of data and use it to adjust activity, often in real time, has come to be known as the solution to the industry’s analytical woes.
Machine learning alone, however, cannot offer accurate intelligence for all marketing efforts -- data discrepancies have been plaguing the industry for years and we still don't seem to be any further forward. No matter how sophisticated the technology is, quality insight and results depend on the quality of what is input initially. Research we carried out for a specific advertiser revealed that two critical elements of campaign insight — attribution and performance data — only matched half of the time. The standard of data fed into the machines was therefore only around 50% accurate, meaning the information driving marketing decisions was likely to be equally imprecise. What’s more, this is not a one-off -- it’s a widespread anomaly in digital advertising.
The question is: What causes this fragmentation and how can marketers ensure intelligent tools provide a view of the truth that is clear and concise, rather than clouded and contradictory?
The Chief Cause Of Discrepancies