In today’s economic environment, IT organizations know they have to make smart decisions on spending amid the ongoing financial uncertainty. Some companies are drastically slashing IT spending and waiting for better times to return. But other forward-looking companies have chosen to maintain or even increase spending in areas that can help increase revenue and cut costs. Some of these companies are examining ways to more effectively manage and define customer data through master data management (MDM).
The IT decision-makers at these companies know that even small enhancements to their MDM strategy can go a long way toward improving an organization’s overall data quality. Better data quality will – in turn – lead to increased sales and marketing effectiveness and business efficiency, decreased customer management costs and ultimately, improved customer satisfaction levels. In fact, effectively defining and managing customer data is at the root of the success of any program that relies on customer data. And it is particularly important during an economic downturn, when organizations must do more with existing resources.
There are two dilemmas that businesses often confront when developing an MDM plan: The first is that different functions within a company have their own definitions of a customer and their own customer hierarchy, and each function has different needs when it comes to customer information. The second is that many organizations do not spend enough time breaking down their data requirements before defining their customer data model, and then do not sufficiently scrub existing data or make ongoing data quality a priority. This can result in complex data models, increased costs, and – over time – decreased sales, marketing, service and IT efficiency and customer satisfaction levels. To successfully navigate these challenges and make better use of customer data, organizations should begin by gaining an understanding of the customer data lifecycle and how different divisions of the business use data. They should then come to an agreement on how customer data will be modeled across the company; identify the appropriate technology and any gaps between business needs and current technology; determine who owns, oversees and can make changes to customer data; and develop a plan for ensuring long-term data quality.
After addressing these common pitfalls, it is best for an organization to take a phased approach to enhancing their customer data management and modeling and making investments in the underlying technology. This will help the company simplify its customer data model, reduce IT costs, augment their customer view with third-party data feeds, maintain a single, complete view of every customer and improve sales efficiency and customer segmentation. This way all functions will be talking about the same customer and they will be able to map all relevant activity correctly to this customer.
The CRM consulting firm Innoveer Solutions recently worked with a high-technology company that wanted to improve the reliability and quality of its customer data. The organization wanted to increase customer satisfaction levels, proactively track lucrative support contract renewals and ultimately, maintain a single, 360-degree view of each customer. The high-tech company was also facing a hiring freeze and needed to boost the efficiency of its sales force. Although the organization’s sales and service divisions had differing needs, the majority of their requirements overlapped and could be attained with just one CRM system, if customer data was correctly modeled from the outset. It was also evident that by spending time creating a more clearly defined customer data model that minimized exceptions, the company could streamline many business practices, simplify the existing infrastructure and decrease long-term costs.
Employing a phased approach, the organization recognized how different business groups use customer data to come to a consensus on how customer data should be defined and managed – and who would have permission to make changes to the data. In addition, the company scrubbed its existing data and consolidated duplicate information, linked child records correctly, integrated third-party customer data feeds and trained a new data quality team that was devoted to converting sales data into business intelligence. Since the company rolled out its new customer data model and MDM business process changes, it has improved account coordination, and the company now has a more in-depth understanding of its customers. Because of its improved MDM strategy – and resulting improved data quality levels – the company is also able to more quickly identify and sell to decision-makers, generate more accurate sales projections and more effectively resolve customer complaints.
This example demonstrates that through improved customer data modeling and more effective MDM business processes, companies can reduce data management costs, improve sales and marketing effectiveness and create a single trusted view of customers. Companies will also find that this effort lays the groundwork to support future customer data-centric projects. This can lead to improved customer retention and reduced costs today during the economic downturn, and will position companies for growth as the economy begins to rebound.
Bernard Drost is the chief technology officer at Innoveer Solutions.
