Data is the lifeblood of a company and a key driver in guiding business strategies and growth. But if data is invalid, incomplete, or otherwise inaccurate, things can get ugly quickly. In this article, we explore the good, the bad, and the ugly of one of the biggest assets a company has – its customer data – and what companies should be doing to ensure high quality customer data.
The ugly truth is that poor data quality is pervasive and costly. Harvard Business Review estimates only 3 percent of companies’ data meets basic quality standards. Gartner estimates the average financial impact of poor data quality on organizations to be $15 million per year. And IBM reports bad data costs the US economy around $3.1 trillion per year.
Certainly, there are dollar figures that can be directly connected to data errors. But when data quality issues go unresolved, they cost companies even more. Poor data quality leads to loss of operational efficiencies, campaign effectiveness, and competitive advantages, and can have far-reaching consequences when it’s used for strategic decision-making.
In fact, Gartner estimates poor data quality is the primary reason 40 percent of all business initiatives fail to achieve their targeted benefits. IBM reports 1 in 3 business leaders don’t trust the information they use to make decisions. And 84 percent of CEOs say they’re concerned about the quality of the data they base their decisions on. Clearly, there’s a need to assess and understand data quality issues and implement strategies and tools to remedy them.
On the flip side, there are tremendous, positive impacts when companies take data quality seriously and:
When companies champion data quality, and continuously assess the usefulness of their data, they reap the rewards. In fact, researchers at the University of Texas estimate increasing data usability by even 10% would boost revenue for Fortune 1000 companies by more than $2 billion per year.
With high-quality data, companies are more agile, productive, and competitive. Some of the many benefits of good data quality include:
The more you know about your customers, the better prepared you are to:
Many companies work in different data systems or platforms. They may acquire new customer lists when they merge or take on partners. They may receive leads automatically from outside sources and collect customer data across multiple platforms. For example, a company might collect data from web lead forms, tradeshows, and point-of-sale systems. Hotel properties and restaurants may receive leads from online reservation sites. Auto dealerships may have their database integrated with a dealer network.
All of these disparate systems and sources lead to inconsistency in data collection and the need to not just clean, but standardize data. Standardization can help companies avoid costly duplicates and other errors that frustrate sales teams and impact campaign costs.
Sales, marketing, and customer success teams talk a lot about getting a 360-degree view of customers. Why is this important? Customers, whether consumers for B2C or clients for B2B, expect personalization and great customer experiences. There’s no way to deliver that without solid, accurate data on those customers. Plus, the more you have a 360-degree view of customers, the better you’re able to identify your ideal customer and develop accurate buyer personas.
To build a larger picture of your customers and their interests, append your database with information collected internally and externally.
Depending on what you offer and who you target, you might find it valuable to add things like age, dwelling type, homeownership status, number of children in the home and their ages, number and type of pets in the home, automotive information, and marital status.
The more robust your data, the greater your ability to send the right message to the right audience. For example, a renter in a condominium where maintenance needs are covered isn’t the target audience for promoting your lawn mowers, pool installations, roofing services, and outdoor lighting products. But that renter may be the right target for promoting home loan offers, location-specific retail stores, other rental opportunities, and new home builds.
Additionally, actively enriching data helps you connect the dots. The more you know about your customers, the more insight you gain into what motivates them, what types of products they look for, and what communication channels they prefer. And when you can hone in on the attributes and interests of your best customers, you can use the information to find more of them.
High customer data quality also helps you reduce costs and improve efficiencies.
For example, ensuring you’re working with verified physical addresses for direct mail campaigns is a time and money saver. It’s not just about saving on overall postage costs. You can also lower your print costs for the campaign by using an accurate piece count and prevent resending packages that could have been delivered right the first time. Additionally, you can append address data to include full ZIP Codes and take advantage of the lowest bulk rates.
Of course, better campaign ROI isn’t the only goal. Verifying physical addresses supports better customer experiences, as speed and accuracy of shipping orders improves.
Likewise, you can verify email addresses in your database or at the point of capture to ensure email deliverability and improve overall campaign effectiveness. Achieving high deliverability supports higher open and conversion rates, and also helps you protect your sender reputation.
Finally, verifying the accuracy of phone numbers supports prompt lead follow-up, increases the efficiency of call centers, and distinguishes mobile and landline numbers so you can ensure your call strategies remain compliant with regulations.
To compete effectively, today’s data-driven businesses need to ensure their customer data quality is sitting pretty. Data quality is an ongoing challenge that can be overcome by leveraging regular data quality assessments, following best practices for keeping data clean, and using the right tools for sound data management.