In Data Quality Management

Dirty data costs companies billions every year in wasted resources and lost productivity. This is true whether the data is purchased, gathered via download offers or stored in a company’s internal database.

This problem is driven by several factors. Today’s mobile workforce is changing jobs faster than ever before. According to Gartner, 30 million of the 138 million workers in the US will switch jobs in the next 12 months. Now add that to the number of businesses that move or get acquired every month. It’s easy to see how they dirty data piles up and piles up fast.

To make matters even worse, feeding dirty contact data into a marketing automation or CRM system has a multiplier effect. This can quickly derail success by:

  • Delivering multiple wrong messages to the wrong person or persons.
  • Annoying customers and prospects with redundant messages.
  • Losing credibility due to botched attempts at personalized communications.
  • Failing to leverage multi-modal marketing capabilities.
  • Misinterpreting campaign success metrics.
  • Creating sales inefficiencies.

So how can a company address this problem? It’s not easy. Most marketers are overwhelmed by hundreds of thousands of duplicate entries, old data, inaccurate contact details and countless records in different states of completion. This existing data has likely been gathered by many different individuals over multiple years.

The best way to start cleaning data is by targeting the right companies, along with the decision makers who actually determine the buying process.  Develop a profile of what your ideal customer would look like, working from there you should be able to weed out less-than ideal candidates or at least give some kind of prioritization to companies in your database.