As the old adage goes, it's much easier to keep an existing customer than it is acquire a new one. In today's environment of customer empowerment, understanding what makes your customer tick has never been more important. Whether they're launching more competitive products and services or building new strategies for customer loyalty, businesses need better insight into each and every customer. This has driven an increase in the demand for CRM services as companies are expanding their use of CRM-related marketing automation tools and customer data analytics solutions.
However, all of this interest is based on the promise of CRM to provide comprehensive, accurate, and timely customer information in order to gain clear visibility into customer behavior and improve engagement, marketing, service, and loyalty. With the evolution of cloud-based applications, organizations are hoping to reap those rewards faster than ever, but many are finding that capitalizing on that promise is more difficult than it seems.
The impact of inaccurate data
Independent analyst firm Ovum suggests that poor-quality data is costing U.S. businesses around $700 billion a year, or 30 percent of the average company's revenue. So how can a company expect to achieve great results from processes that are data-driven, such as CRM services, when customer data can't be trusted?
Inaccurate data undermines CRM efforts. Actions and engagements based on incorrect insight frustrate customers. Customers who entrust a brand with their personal data expect that information to be used to improve the relevance of their interactions with that brand—to develop a more personalized relationship—and can be upset when their expectations are not met.
One of the biggest challenges to ensuring data is of high quality is that it now enters organizations in ever-increasing quantities and channels. In this age of big data, firms are seeing data volume grow at a breakneck pace. In many cases, the increase in volume is averaging some 50 percent a year, and it's double that in industries such as media, healthcare, and insurance. On top of that, customers are interacting through an increasing number of contact points, via ever more devices and in ever more formats. It's a maddening mess of information to sift through!
Before this flood of data can be woven by CRM services or other processes into meaningful intelligence to drive customer relationship actions, it must be cleansed, then matched (standardized) with other relevant data from inbound channels, as well as with existing legacy data for that customer. Given the volume, velocity, and variety at which data is arriving, the challenge is one of latency. How can an organization cleanse and match the data to support interactions that are happening right now? A 2012 report by The Economist Intelligence Unit suggests that there is a real need to overcome this challenge, as 85 percent of those surveyed stated that they were experiencing issues due to their inability to analyze and act on data in real time.
Achieving data compliance
The key to ensuring good data for CRM applications is to create a data quality compliance process. This means ensuring that the data entering corporate systems and processes meets required standards for cleanliness, relevance, and timeliness. There are three main data compliance steps:
1. Assessing the quality of existing data and its degree of reliability and consistency for CRM processing. There is no point in embarking on an expensive implementation only to find that your data isn't of good quality, doesn't reconcile, and doesn't provide a reliable customer view. Data profiling enables you to fully understand the issues in your data and determine what steps need to be taken to remedy them. Specialist data quality software automates this process, enabling you to incorporate your own rules, so the data is not only validated for quality, but also for relevance to your specific CRM needs.
2. Converting these rules into processes that transform and correct the data into a common format. A standardized and corrected customer record ensures it will match associated data coming through other channels and legacy systems of data collection. This ensures that associated customer, financial, product, and historical data is linked to the correct person, and that any external data appends.
3. Finally, the same process created for step 2 can also be embedded into your CRM system and other relevant customer-centric systems to automate the validation and correction of data at the point of capture. CRM users and supporting teams will all have a high level of data consistency, quality, and reliability serving their specific business requirements without the latency and cost problems commonly associated with post-CRM data reconciliation.
Embrace the customer relationship opportunities presented by data, big data, and the ease of access to on-demand CRM. But exercise caution, and heed the CRM data quality lessons of the past. For all the value data insight can bring, inaccurate data can frustrate and undermine even the best-willed customer relationship efforts. By aligning your data quality goals with your CRM goals, you'll be in the best position to drive return from your customer experience investments and avoid CRM failure.
Nigel Turner is vice president of information management strategy at Trillium Software.