How Marketers Can Avoid Bad Data

While most companies will never have perfect data quality, it’s important for marketers to approach their data with a consistent and careful eye. 

Written by David Finkelstein
Published on Dec. 15, 2021
How Marketers Can Avoid Bad Data
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Consumer data is a highly valued commodity for marketers, but unfortunately bad data is growing increasingly more prevalent in the adtech industry. 

In 2020, BDEX analyzed more than 1 billion device identifiers and found 25 percent of them to be invalid. Whether it comes from human error, fraud or outdated information, bad data costs organizations an average of $12.9 million each year. Besides the major impact bad data has on return on advertising spend (ROAS), poor data quality can affect a brand’s strategy and decision-making abilities. 

Utilizing industry rates of usable consumer data and average ROAS, BDEX found that marketers can increase ROAS by as much as 43 percent by taking steps to eliminate bad data. 

With these considerations in mind, here are three ways marketers can improve their data quality.  

3 Ways Marketers Can Improve Their Data Quality

  1. Evaluate your data sets.
  2. Build strong identity resolution tools.
  3. Utilize machine learning.

 

Evaluate Your Data Sets 

Consistently analyzing data sets is key to decreasing bad data and identifying common signs of data fraud.

When evaluating data sets, it’s important for marketers to be aware of the following statistics: 95 percent of all IP addresses have six or fewer email addresses associated with them, and 95 percent of all email addresses are associated with four or fewer IP addresses. That means that companies should be cautious of any email address or IP address that is linked to more than four IP addresses or six emails. While these signs are not a guarantee of fraudulent data, they’re highly likely indicators.  

In addition, marketers should be aware of other common signs of fraudulent identifiers, including unnaturally fast traffic on web pages or excessive traffic for extended periods of time. 

Read More From David Finkelstein3 Marketing Strategies for Adjusting to the Post-Pandemic World

 

Build Strong Identity Resolution Tools 

Once marketers understand the telltale signs of bad data, they can focus on internal systems that function as valuable data management tools, such as identity resolution.

Identity resolution tools combine multiple identifiers across devices and touchpoints to create a unified view of each customer. Brands depend on identity resolution to draw a link between all consumer touchpoints, both online and offline. In this sense, identity resolution helps minimize erroneous device identifier data as a result of user error, because it works so effectively to accurately link customer data. 

Strengthening your identity resolution capability is a must for any company developing data-driven marketing strategies, and it helps ensure that there aren’t gaps or inaccuracies in customer data that could lead to inconsistencies — and therefore a loss of ROAS. 

 

Utilize Machine Learning 

Another key tool for improving data quality is machine learning because it helps companies detect data anomalies and duplicates. 

As suggested by its name, machine learning enables machines to scan billions of data points for accuracy and make automatic decisions without human input. By employing machine learning to create a systematized method for data entry and verification, marketers can significantly decrease the amount of bad data that is created as a result of user error and outdated information. 

More importantly, machine learning allows brands to shift the resources used for time-consuming data collection tasks. According to research from Forrester Consulting, marketing teams spend as much as 32 percent of their time managing data quality, so it’s imperative that marketers learn to make the most of their data efforts. 

 

The Takeaway

While most companies will never have perfect data quality, it’s important for marketers to approach their data with a consistent and careful eye. 

This means taking the time to evaluate data sets for signs of potential fraud, strengthening their identity resolution capabilities and leaning on machine learning to streamline the data entry and vetting process. Doing so will help create the strong data foundation necessary to run highly effective advertising and marketing campaigns. 

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