Customer relationship management (CRM) is one of the most useful and widespread business tools on the market. And yet, a startling number of companies fail to realize full return-on-investment and even suffer direct losses from CRM initiatives. The culprit? Many signs point to bad data.According to some estimates, organizations can lose around $8.2 million per year as a result of inefficiencies and missed opportunities caused by poor data quality. Duplicate data, inaccurate data, invalid data, non-conforming data, dark data—it takes many forms, but they all share a common characteristic, which is the ability to disrupt business performance.
How it Happens to Your CRM
In CRM software, the building block of all data is the contact record. The contact record is also often the first piece of information to become inaccurate. A contact record can go obsolete in as little as one month, due to life changes such as marriage, divorce, relocation, or job changes. The frequent manipulation and high-volume entry of customer information also leave room for error. That margin of error is further multiplied in organizations with multiple customer engagement channels and points of entry.
Businesses commonly blame bad data on some of the following culprits:
Flawed interdepartmental communication
Lack of a formal data stewardship strategy
Lack of relevant data control technology
Lack of company oversight and management
A 2013 multi-national survey by Experian revealed that 91 percent of all companies “suffer from common data errors,” including incomplete, outdated, or inaccurate data. It’s important to understand that bad data isn’t just a series of micro-level flaws. As the errors multiply and travel up the hierarchy, the damage grows exponentially and affects many different areas.
Poor customer service and satisfaction: Your customers will directly witness and suffer the impact of poor data quality since it directly pertains to the products and services they receive.
Skewed business intelligence: Bad data will cause inaccurate forecasting and reporting, which means faulty intelligence. With the wrong information in their hands, your leaders are more apt to make poor business decisions, or draw the wrong conclusions from the information they have.
Crippled loyalty programs: If customer contact information is outdated and sales activities are not being recorded as they should, how can you drive true loyalty with rewards and incentives?
Failed marketing automation: Without a process in place for verifying and remediating new lead information, email and other campaigns will be plagued by delivery failures and content that gets sent based on faulty triggers.
Wasted time and money: Ultimately, the same bad data that causes underperformance and redundancy in these areas costs your business revenue (missed opportunities, failed ROI) and valuable time. The same Experian survey above showed that 77 percent of businesses say their bottom line has been affected by inaccurate and incomplete data.
It’s easy to play the blame game with data errors. After all, a human being, at some point, did type information into a set of fields. But the blame for data problems of a larger scale should be shared by every stakeholder connected to that data, which includes everyone from the sales rep through the CIO.
Ensuring consistently good data practices will require a top-down strategy that addresses both the content and form of your CRM data. Here are four tips to get you started:
Provide strong sponsorship and oversight from company leadership: In order for a stewardship policy to take root, it needs strong support from key decision-makers. Traditionally, the CIO has been in charge of maintaining data quality, but many larger companies choose to appoint specific data officers. Whoever takes the reigns will need to demonstrate and measure the benefits data stewardship will have on other strategic initiatives (marketing campaigns, for example) as well as the company’s bottom line.
2. Address human error within your teams: The biggest data liability within a company is usually not the CRM; it’s the personnel. Train your team members on best practices for data entry and teach them about the lifecycle of customer information. It’s also important to institute quality control measures that help track compliance.
3. Shore up built-in automations for data control: Most CRMs allow customization down to the field level. CIOs and administrators should take advantage of this by designing the system to police itself: define required fields for information capture; use default values and auto-population as much as possible; create helpful field dependencies; design access controls to only give account/contact/lead permissions to the right users; impose restrictions and parameters on web-to-lead data.
4. Institute a formal quality control process: First, identify the entry points for your CRM and prioritize them based on volume (a high-volume entry point might be batch-importing or web forms). Next, use a data cleansing tool to verify and enrich information as it enters the system at the point of capture. A surprising number of organizations (23 percent) still rely on manual data checking for entry points, but unless your business has a very small customer base, manual checking can let a lot of errors slip through the cracks.
It’s also a good idea to check existing data for omissions, obsolescence, and duplicate records by conducting batch cleanses with back-office programs (UnDupe, which integrates with Nimble, is a good example).
As CRM software expands its reach into more workflows and customer channels, the quality and accuracy of data will become even more critical to business success. The stakes are high, which is why it’s important to implement a great solution. By guiding your team with strong central leadership, minimizing human error, and using effective data management tools, you can protect your bottom line and ensure your CRM data is always up-to-date.