How much of the money your company spends on advertising and marketing goes untracked? Or sub-optimally tracked? Do you know? Does anyone in your organization actually know?

The digital marketing industry is suddenly awash in new technologies that allow companies to optimize their marketing spend by refining attribution, to the point where each step along the customer journey (in some cases including analog or offline touches) can be measured and assessed for their impact on revenue.

But the best optimization and attribution tools in the world can’t help you derive real insight if the initial tracking data you used is messed up. Likewise, if the tracking data you’re using can’t be looked at holistically — for example, if you can’t directly compare performance from one platform to another because your Facebook campaigns don’t use the same tracking conventions as your DCM campaigns…your ability to derive real insights for your organization is limited, at best.

To take full advantage of optimization and attribution tools on the market, It’s critically important for large-scale marketers do two things: sync up tracking data from the first instance of data collection, and ensure that all subsequent marketing data is tracked consistently over time. This is no simple task. It is far too easy for data quality to become compromised. Whether it’s the issue of managing tracking data consistently across platforms or capturing and measuring deviations from the original tracking codes as they happen in real time, your organization’s ability to derive full value from the marketing automation tech stack can be distorted without anyone realizing it.

When compromised data is discovered, a few things usually happen: first, an enormous expenditure of manpower might be sent to “fix” the data. In cases where the data can’t be cleaned to the point of trustworthiness, it may require essentially starting from scratch with new tracking conventions. Or, the idea of data-driven decision making may be called into question altogether, with the reasoning “if we are spending all this time and money, and we still can’t get to a greater than 80% confidence interval, is it worth the effort?”

In the window of time before your data collection mistakes were uncovered, business decisions may have been made, based on faulty data, determining tens of thousands of dollars of ad expenditure. When mistakes are uncovered, the effort that goes into data triage inevitably takes an analytics team’s time and attention away from current business priorities in an attempt to salvage campaigns that have already passed.

All of this is why IBM estimates that bad data costs US corporations $3 trillion per year.

Wouldn’t it be ideal to ensure consistency in your campaigns from the first instance of data collection? We certainly think so. Tracking First is one a handful of companies trying to meet this challenge, by helping organizations build their entire network of marketing data analysis on a solid foundation of data collection. If your campaign tracking is structured the right way from the beginning, you can continue to build upon it indefinitely without fear of the whole thing falling apart. You can also derive full value — and real insight — from subsequent efforts to optimize your campaigns and define attribution. There is a better way!

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