Need For Good Data Management In Customer Service

Businesses today understand that data is an important enterprise asset, relied on by customer-facing employees to deliver on their customers’ needs, among other uses. Yet too few sales, marketing, and customer service professionals realize that addressing data quality is necessary to move the needle on customer satisfaction. A recent Forrester survey shows that fewer than 20 per cent of companies see data management as a factor in improving customer relationships.

Inattention to data quality can have a big impact both on companies and the customers they serve. Following are just two examples:

Garbage in/garbage out erodes customer satisfaction. Customer service agents need to have the right data about their customers, their purchases, and prior service history presented to them at the right point in the service cycle to deliver answers. When their tool sets pull data from low-quality data sources, decision quality takes a hit, leading to significant rework and customer frustration.

Lack of trust in data has a negative impact on employee productivity. Customer-facing employees begin to question the validity of underlying data when data inconsistencies and quality issues are left unchecked. This means employees will often ask a customer to validate product, service, and customer data during an interaction—which makes the interaction less personal, increases call times, and instils in the customer a lack of trust in the company.

The bottom line: High-quality customer data is required to support every point in the customer journey and ultimately deliver the best possible customer experience to increase loyalty and revenue. So how can firms most effectively manage their data quality?

While CRM applications can play a role in this process, they can’t solve the data-quality issue by themselves. A common challenge heard from our clients, as they rethink their existing CRM applications, is the inability to obtain a complete trusted view of the customer. This is where third-party data-quality solutions must come into play. To get started on the data-quality journey, application developers should consider a five-step process:

  1. Don’t view poor data quality as a disease. Instead, it is often a symptom of  broken processes. Using data-quality solutions to fix data without addressing   changes in a CRM application will yield limited results. CRM users will   find a work-around and create other data-quality issues. Balance new  data-quality services with user experience testing to stem any business   processes that are causing data-quality issues.
  2. Be specific about dirty data’s impact on business effectiveness. Business   stakeholders are flush with data-quality frustrations. Often, they will  describe poor data as “missing,” “inaccurate,” or  “duplicate” data. Step beyond these adjectives to find out why   these data-quality issues affect business processes and engagement with   customers. These stories provide the foundation for business cases,  highlight what data to focus on, and show how to prioritize data-quality  efforts.
  3. Scope the data-quality problem. Many a data-quality program begins with a   broad profiling of data conditions. Get ahead of bottom-up approaches that   are disconnected from CRM processes. Assess data conditions in the context  of business processes to determine the size of the issue in terms of dirty   data and its impact at each decision point or step in a business process.  This links data closely to business-process efficiency and effectiveness,   often measured through key performance indicators in operations and at   executive levels.
  4. Pick the business process to support. For every business process supported   by CRM, different data and customer views can be created and used. Use the   scoping analysis to educate CRM and customer stakeholders on business   processes most affected and the dependencies between processes on commonly   used data. Include line-of-business executives in the discussion as a way to get commitment and a decision on where to start.
  5. Define recognizable success by improving data quality. Data-quality efforts   are a key component of data governance that should be treated as a   sustainable program, not a technology project. The goal is always to   achieve better business outcomes. Identify qualitative and quantitative  factors that demonstrate business success and operational success. Take a   snapshot of today’s CRM and data-quality conditions and continuously  monitor and assess them over time. This will validate efforts as effective   and create a platform to expand data-quality programs and maintain on-going   support from business stakeholders and executives.
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