How would you grade the accuracy of all the data in your CRM? If you’re like most CMOs, you’d give it a D. If you’re lucky, you might say a C. CRM data is notoriously dirty; it’s outdated, disorganized, or flat out wrong. Marketing organizations are throwing down millions to solve the problem of dirty data, but what if they’re looking at the problem the wrong way? What if the problem isn’t about dirty data, but rather, the way you use data to fuel marketing and sales campaigns?
Dirty data is the culprit lurking behind a lot of bad marketing, and it’s the reason marketers allocate tons of valuable resources to data cleansing. Up until recently, data cleansing has been the only option available to marketers whose success hinges on the accuracy of the data that fuels their campaigns.
There are typically three main causes for Dirty data:
- Static data decay over time
- Inaccurate data enter CRM
- Duplicate data enter CRM
Netprospex reports that data decay at a rate of 2% per year.
When your campaigns rely on bad data, every email campaign, phone call, and direct mail send is at risk of turning up a closed business or a key contact who’s moved on. To avoid wasting time following dead ends, marketers embark on lengthy and costly data cleansing processes that promise to keep the data in their CRM squeaky clean for a limited amount of time.
Read more about The Dangers of Dirty, Duplicate Data.
When it comes to data cleansing, we think marketers are looking at the wrong problem. Rather than scouring a dataset with a bristly software that scrubs the good with the bad, we think marketers should focus less on data and more on software.
CRM data is rife with hidden insights, and all the scrubbing in the world won’t magically make them pop. Keeping your CRM data clean enough to unearth these insights requires tons of legwork–especially when you don’t know which data move the needle on your marketing campaigns. To unlock those insights, marketers need better technology, not better data.
Dirty data can paralyze a marketing team, but when the problem of bad data is eliminated, marketers are free to focus on what they do best: discovering, engaging, and converting customers. If marketers could just eliminate the problem of unreliable data, they would be free to build effective customer engines.
Thankfully, a new breed of data science tools, powered by technology like Apache Spark, have been built entirely to help marketers take advantage of their data–without the hassle of racing the data decay rate. Technology is helping marketers rethink the way they look at data by comparing the data in a CRM system (internal customer data) to a tightly maintained, real-time dataset that contains a broader view of accounts (external proprietary data).
These tools operate by matching a customer’s CRM data to their data through a complex process called data integration. Once data have been matched, the software solution will analyze data to reveal insights.
Marketing technology analysts call these tools Customer Data Platforms.
Customer data platforms render CRM data cleanliness insignificant because they focus on the only data that matter. Most customer data platforms look at the won, lost, and open records in a CRM, layer machine learning algorithms on top of this information, and reveal insights about which market and buyer behaviors drive decisions. Thanks to customer data platforms, marketers no longer have to undergo expensive data cleansing processes to apply advanced data analysis to their CRMs. This new approach only requires that marketers keep track of basic firmographics and account status.
This new approach means that marketers don’t need to wait for a CRM cleanse to run a TAM analysis on their data or build a target audience for a social advertising campaign.
Keeping data clean is one of the biggest data science challenges of our day and age, but it’s not likely to be one that marketers solve. As the roles of CMOs and data scientists collide throughout the next decade, we’ll see an uptick in the development of new tools that make it possible for marketers to apply data science without becoming data scientists themselves.