When data surpassed oil last year as the world’s most valuable resource, it marked a dramatic milestone in the history of human commercial activity. The “information economy” is firmly upon us, and it affects us all regardless of industry. Nobody knows that better than marketing and media professionals, who rely on targeted advertising to fuel their business.
Targeted advertising is effective in driving conversion, and better data leads to better targeting.The problem is that the data available to most marketers is like the raw crude oil that comes out of the ground. It’s dirty, dispersed across different locations, and needs to be refined in order to be useful.
Marketers need accurate, easy-to-understand data in order to make effective decisions. This is where the issue of data cleanliness comes into play. Broadly speaking, data cleanliness refers to the removal or correction of inaccurate, incomplete, or irrelevant data that corrupts otherwise useful data sets. Not only does marketing data need to be cleaned in order to be useful, it needs to be refined and consolidated into organized, unified formats.
According to Forrester, less than 0.5% of all data collected by companies is ever analyzed and used. And yet, “just a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company.” For those of us in the digital marketing world, those numbers seem shocking. Data is our stock in trade. Why, then, do so many companies make such inefficient use of their data?
As with so many business decisions, the question comes down to cost. Most companies feel that cleaning and consolidating their data simply takes too long and costs too much to justify the effort. A recent study by the CMO Council found that nearly 25% of marketing, commerce and supply chain executives believe that they don’t have the time and/or resources to clean and process all of their data, and 51% of marketers said that inaccessible data trapped in individual platforms was a major impediment to getting the most value out of their assets.
Unclean data results from marketers working across disparate platforms, buying data from different sources, compiling different user IDs, etc. Some firms can charge hundreds of dollars per hour to clean data sets. While such an outlay may seem steep, the cost of cleaning the data will pale in comparison to the operational cost of inaccurate or misleading information.
Analysts and marketers need to be able to look at a data set and easily take inferences away from it. Moreover, a sophisticated marketer shouldn’t have to possess the skills of a computer scientist in order to make sense of its data. The promise of digital marketing lies in its nimbleness and adaptability. By knowing what messages and channels are driving conversion (thanks to clean, accurate attribution data), marketers can experiment on the fly and adjust their tactics accordingly. The result is a streamlined, efficient marketing funnel that increases ROI.
Better data leads to greater marketing efficiency, which more than makes up for the modest initial investment in data cleaning. If you have a better picture of your ideal customer, you can better understand the value of each customer relationship. You can know which messaging tactics deliver the most predictable results and plan accordingly for maximum return. The result is a healthier digital marketing ecosystem that attracts further investment. When all the elements of an efficient data stream work in harmony, we can stake a much greater claim to the true potential of digital marketing.