How Poor Data Quality Leads to Wasted Marketing Dollars
Companies invest millions of dollars in agencies to control their media buying—not to mention the money spent on the actual buy itself. Yet a recent Forrester report revealed that 21 cents of every dollar of media spend is wasted because of insufficient data quality.
Whether or not marketers are aware of this financial loss, organizations continue to increase media spending year after year without putting measures in place to mitigate poor data quality. We’ve become numb to the waste and plow ahead hoping that campaigns will still yield positive returns.
The same Forrester study reported that a quarter of campaigns are harmed by poor data quality. But what exactly is poor data quality, and how can marketing leaders eliminate this issue? The answer lies in data integrity. Improving data standards saves teams time on manual process elimination and improves customer targeting, ultimately reducing wasted media spending.
Let’s take a closer look at what constitutes poor data quality and explore some tips on how to avoid it:
Insufficient Data Is a Result of Bad Data Management
Poor data quality is often created through poor data management and setup, which can be detrimental to campaigns long term.
When kicking off a media campaign, many marketers move forward with a multifaceted approach that defines, executes, and tracks the campaign across a variety of channels. The campaign often spans many agencies, creative teams, analysts, internal ad operations teams, and various other stakeholders. The flow is already complicated, and when you add the expansion of platforms and channels on top, it becomes a data nightmare. You can have numerous policies and divergent strategies in place, including separate disconnected data taxonomies.
Often, information from each stakeholder is uploaded in bulk, and each spreadsheet often houses data with entirely different taxonomies. This could mean data is put in the wrong place, or the resulting tracking code and landing page URL fail to capture data fields downstream accurately, tainting any insights pulled out of the dataset.
Implementing a unified taxonomy from the start can help solve poor data quality problems.
Data Taxonomy to the Rescue
Seeing wasted media spending doesn’t necessarily mean the whole campaign was a failure. Unfortunately, it often means there are inefficient processes within the execution of a media buy that are costing unnecessary time and money.
To optimize time and spending, it’s critical to apply lean principles and incorporate a unified data taxonomy from the start. Not optimizing these processes will lead to more costly media buys and ultimately hold a business back from its true ROI potential. Here’s where data integrity comes into play.
A few essential tips go along with improving data integrity. Let’s break each down.
1. Quit defining data in a vacuum.
There are typically numerous parties involved in a media campaign, both internal and external. This reality creates information silos and often forces data to be defined in a vacuum with limited transparency across agencies and teams. Having no unified taxonomy in use for tagging leads to insufficient data. Instead, marketing teams should pull all stakeholders together to review and approve what’s included in tagging options and define what each distinction means.
2. Unify data definitions.
You shouldn’t have to guess what your data means. Consult each stakeholder on what should be included in the data taxonomy and then provide a detailed guide that outlines the consensus. Across your team, ensure every member of the group who might touch the data is clear on your data taxonomy and its definitions so they can improve data application and interpretation accuracy. Everyone is much more likely to complete metadata fields correctly when they are confident in what a value means and its importance to the organization.
3. Integrate a single source of truth across disparate systems.
If data and content for a media buy are planned, edited, produced, and stored in different places or across various agencies, the systems must be closely integrated. Data should flow uninterrupted between the tech stack and be easily accessible to any stakeholder who may need it. This means that, ideally, any change to one system would trigger updates to every other system.
Data integrity must be actively prioritized and managed across stakeholders to ensure the true success of media buys. Too often poor data quality is detrimental to the success of a campaign when these costly issues could easily be avoided. Teams must mitigate this possibility up front by defining data outside of the vacuum, establishing a consistent data taxonomy and integrating a single source of truth across systems.
E.J. Freni is chief revenue officer at Claravine. An award-winning sales executive with over 15 years of progressive experience and leadership in preeminent marketing technology and SAAS companies, Freni builds emerging businesses and teams to accelerate market penetration, new business, and revenue growth. In his current role, Freni oversees Claravine's global Go To Market, working with many of the largest companies in the world to solve enterprise data integrity challenges. Prior to Claravine, Freni led sales, strategy, and execution at premier global marketing and advertising platforms including Adobe, Brand Networks, Quantcast, and Yahoo!