Customer data come into your organization from every touch point, both physical and virtual—numbers, names, order histories, website preferences. These data are the lifeblood of most organizations today. Like real blood, however, data can become contaminated; when that happens, your organization suffers.
Dirty Data Are Pandemic
Inaccurate customer data probably already exist in your system. Experts across industry estimate that inaccurate data affect as many as 22 to 35 percent of businesses in the United States today. Think about the credit card industry. How many times have you received multiple credit card offers from the same company? Do you already have a card with that company? Another example is when a customer calls an organization for support on a product or service. How many times must the customer tell the same story? How many times does the customer service representative on the other end of the line recognize the problem, know what’s been done and what hasn’t, and take the next step without making the customer repeat any of the previous steps?
The problem with these two scenarios is that the customer data are inaccurate in some way. That could mean that they’ve been duplicated across departments that have soloed information that they don’t share. Or maybe the data are incomplete, wrong, or mislabeled. Syntax errors and even typos are common problems with “dirty data.”
Are you’re Data good or Bad?
The problem with bad data is that they can go unnoticed for so long that the damage is done before an organization even realizes that the error has affected sales or customer confidence. Fortunately, one question will help you determine whether you have good or bad data: Do you have data? If you do, then you have bad data. It happens when sales teams key in orders, when a new application is implemented, or when form fields are mislabeled. Bad data happen, and when they do, they can obscure your 360-degree view of your customer.
What you see instead is a fragmented story from which pieces missing. They may even be the wrong pieces altogether. The result can lead to poor customer experiences, which in turn lead to loss of loyalty and confidence. So, yes: Bad data can destroy your customer relationships.
Cleanliness Is Next to Good Customer Relationships
Many organization still use manual cleansing processes to strip out syntax errors, typos, and record fragments in the data they collect, but this is an expensive, time-consuming way to do it. Fortunately, a variety of tools is available to help organizations clean their data and create a holistic view of the customer across the organization. These tools can help organizations build more consistent data sets across all systems, which helps them better understand their customers. Clean, consistent data also make sales teams and customer service representatives more responsive, which means that the whole organization is more agile, and its customer relationships have more depth and meaning.
Don’t misunderstand: Cleaning your data won’t be easy, but it will be worth it. The alternative is a fragmented view of your customer that leads to poor customer experiences, lost sales, and lower profits.