Global Trade recently highlighted an annual 3PL trend study that indicates one of the biggest goals for logistics companies in 2019 is to prioritize customer relationships.

Developing strong customer relationships relies on effective customer engagement and communications. Most logistics companies are familiar with the technology and tools that help them manage fleets, track inventory, and improve operations. But, a growing number are leveraging customer relationship management (CRM) technology to improve customer engagement and support sales and marketing initiatives. The timing couldn’t be better, because if prioritizing customer relationships is a key focus in 2019, prioritizing the data quality in CRMs is an essential part of the mix.

The Impact of Data Quality

To prioritize CRM data quality, the ultimate goal is a CRM database free of duplicate records, missing or wrong details, and non-standardized entries (e.g., entering Corporation when Corp is preferred). But bad data is added to the system through list imports, manual entry, and typos on web forms when data quality tools aren’t in place to catch duplicate information or invalid data. In the absence of data cleansing routines, even good data begins to decay as contact information changes and companies merge or close.

Without a data management protocol in place, it is impossible to realize the full potential of CRM data to guide business activities. In the case of the logistics industry, muddying reports with user data errors leads to misdirected marketing and sales growth efforts. This can create frustrating interactions with the company, poor customer experiences, acquisition and retention challenges, and ultimately, lost revenue.

The High Cost of Bad Data

The logistics industry is no stranger to the importance of maintenance. Left unchecked, a small problem with a fleet can become a big problem with domino effects that bottleneck the entire supply chain. There’s a similar impact with a lack of data maintenance.

According to the 1-10-100 quality principle, the relative cost of fixing a problem increases exponentially over time. So if the cost of preventing bad data from entering the CRM is $1, then the cost of correcting existing problems is $10, and the cost of fixing a problem after it causes a failure is $100. The issues and costs are compounded as that bad data begins to pollute marketing and sales initiatives, decreasing campaign ROI and reducing customer engagement.

The Two-Step Data Cleansing Process

To stop the cycle, a cyclical approach to data quality and maintenance is needed. The following two-step data cleansing process is a great place to start.

Prevention is the first step. The company must ensure those who use the CRM system leverage best practices for entering and updating data without introducing errors. Examples of clean data best practices include completing all data entry fields required for a record, following a standard naming convention, checking for duplicate records before entering new information, and ensuring the validity and deliverability of email addresses. It’s also wise to consider creating a data governance policy that formalizes these practices and embeds data quality in the company culture.

Remediation is the second step. This involves keeping data accurate with regular data cleansing routines that include steps to remove or merge duplicates, standardize content, and verify email addresses. It should also include checking data against credible outside sources occasionally to determine if it’s up-to-date or stale.

With either step, some areas of data quality and entry are challenging even for the most detail-oriented data users or administrators. This makes the availability of third-party data quality tools that go beyond the native functionality of CRMs an important option. Companies can choose solutions that are compatible with their CRM and should look for those that are particularly effective at supporting data integrity during mass imports, streamlining and automating data quality processes, and customizing how duplicate records are managed. Email verification tools can also be leveraged to verify the email addresses in lists before importing them, directly in Salesforce to support lead follow-up, and at the point of capture (for example, adding an API to web lead forms to verify email addresses as they are entered).

Data Quality Is Logical

There’s a growing trend in viewing quality data as a high-value business asset. Studies show senior leadership is increasingly acknowledging the need to support data quality and 85 percent of corporations indicate they are trying to incorporate data into their business strategies. Likewise, the value of using CRM data to get to know customers better and improve customer experiences is widely recognized.

To achieve significant growth in their customer base and revenue, it’s time for logistics companies to give importance to marketing and sales data the way they’ve given importance to distribution, warehousing, and fleet data. People and human-error, not technology, hold data back. As noted in an article from Salesforce.com, “The value of CRM isn’t in the product; it’s how you use it.”

Implementing a data management protocol is the only way to navigate human error and get the most value from the CRM. The resulting higher quality data will bolster marketing and sales activities, and help logistics companies better understand, reach, engage, and retain their customers. Once the previously mentioned two-step process is in place, companies can revise and refine data quality processes as they learn more about what clean data means and how to deliver great customer experiences using quality CRM data.

Ashley Sierant is a data quality management expert, overseeing successful implementation of Validity tools for clients. Validity is a leading global provider of data integrity and compliance offerings that tens of thousands of organizations worldwide rely on to trust their data.