No matter how good the machine you build it is, it’s not going to run well if your fuel is poor quality. Data is the fuel the runs all marketing. The problem is, data error is commonplace. Data providers are all trying to give their clients the best data possible but often the data they are providing doesn’t match up.
Harvard Business Review did a study in May 2020 to test the accuracy of consumer data. They found that the accuracy of Demographic data was particularly disappointing. Most were only around 50% accurate. For example, the average accuracy of gender segments classifying males was only 42.5%.
“Half the money I spend on– department-store magnate John Wanamaker (1838-1922)
advertising is wasted; the trouble is,
I don’t know which half.”
What do those errors mean for your campaign? A study by Forrester Consulting on behalf of Marketing Evolution in fall 2019 found that “marketers estimate that 21 cents of every media dollar spent by their organization in the last year was wasted due to poor data quality, which translates to a $1.2 million and $16.5 million average annual loss for mid-size and enterprise organizations, respectively.”
How do a few errors on a record level equal so much wasted money? Record level data is the spring from which everything else flows. For a small example, if a few records in your CRM have mistakes in them, what is going to happen when you use them to send an email campaign? The opens and clicks from your email campaign may inform your audience for your social campaign. In every step and trigger along your campaign, the small errors effect more and more information downstream.
The company Truth Set is looking to minimize error across all data. How? By providing a third – party consumer report type service for data providers and marketers. Truth Set goes through every record available and assigns a percentage rating to all the attributes, such as ‘percentage that this record is actually male.’ That way, marketers won’t waste money on inaccurate data and data providers can keep their data clean.
The hope is that as more time goes on, third party quality checks and ratings will become commonplace and shrink margin for error unilaterally.
What can you do make sure your data is accurate? There are a few easy identifiers you can check for. How old is the data? How transparent is the provider with where the data came from? How consistent and relevant is it?
What changes do you think would make it easier for marketers to trust their data?