Affordable cloud computing has created increased demand for Customer Relationship Management (CRM) services. CRM in the cloud not only delivers cost savings benefits, but enables access to client information from anywhere at any time.
And yet, companies need to be able to trust the underlying customer data before they use it to fuel processes, support CRM services, or guide management and sales decisions. In light of the interest in Big Data and analytics, it’s more important than ever that any data-dependent process can rely on the quality of the data in order to deliver actual business value.
CRM as a service
We’re in the midst of a CRM for cloud services boom. Companies such as Microsoft, Sage, SAP, and SugarCRM along with hundreds of other providers have been rushing to offer cloud-based and on-demand or hosted CRM solutions to satisfy this thriving market. According to a study by Nucleus Research, 63 percent of enterprises with more than 1,000 employees have adopted on-demand CRM technology, while according to Gartner, around 35 percent of all CRM applications use SaaS—and this number is likely to increase to 50 percent by 2020. At the same time, companies are also expanding their use of CRM-related marketing automation tools and customer data analytics solutions to power better business decisions.
All of this interest is based on the premise that CRM solutions 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 often more difficult than it seems.
Data challenges
A recent Economist Intelligence Unit report commissioned by Capgemini found that 75 percent of business leaders surveyed believe their organizations to be data-driven, and yet a majority of that data is not high quality. 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 the 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.
Good data means better customer relationships
The reality is that the better the data, the greater the opportunity to succeed, and avoid such customer pitfalls. Good customer data, including relationship history and preferences, has the potential to improve the effectiveness of marketing campaigns by catering to the individual through the most appropriate channel. It can also reduce waste from poorly targeted mailings and protect customer relationships from the brand damage associated with “marketing fatigue” which occurs when customers are over-burdened with frequent off-target messages.
For example, Iceland Frozen Foods is one of the top 10 grocery chains in the UK. To further extend their presence in the market, the organization wanted to introduce a new customer card. To do so they needed to build a detailed and accurate single view of the customer for more than three million individuals. By incorporating data quality into their customer data solution, the organization was able to identify and eliminate duplicate customer records, while more effectively capturing home delivery and shopper details at the point of sale. As a result, the grocery chain gained accurate customer intelligence for decision-making, enabled personalized direct marketing communications and generated higher customer response rates.
Quality is the key
Good data enables strong loyalty programs and the identification of new customers for special treatment or nurturing. From a service perspective, good data also means accurate and fast customer recognition, recent transaction and engagement identification, and expedited issue resolution. Reliable data allows for easier unified billing, improved invoicing, and better cash flow as well as compliance with customer privacy data protection, opt-outs, suppression, do-not-mail, and other exclusions. The benefits of high-quality data seem endless.
Oki Data is one such company that has experienced the benefits of data quality beyond CRM. Initially the company deployed data quality in order to increase the value of its data management solution which handled customer shipping information. The company was able to immediately decrease shipping and mailing errors due to verified and corrected addresses and improve customer service using data that gave them a more complete view of the customer. However, the company soon realized they could further expand the benefit of good data across their organization and began using online data quality processing in conjunction with their e-commerce website as well.
Three steps to achieving CRM-compliant data
The key to ensuring good data for CRM is to create a data quality compliance process. Any data that is entered into a corporate system for use in a customer related business process needs to meet required standards for cleanliness, relevance, and timeliness. There are three main data compliance steps:
- Determine quality of existing data and its degree of reliability and consistency for CRM processing
There is no point in embarking on an expensive CRM implementation only to find that your data doesn’t reconcile and cannot provide a reliable customer view. Get quantified insight into the quality of your data before you begin to move it to your CRM platform. Data profiling enables organizations to understand any issues with the data and determine which steps need to be taken to remedy them. Dedicated data quality software automates this process, allowing companies to incorporate their own specifications, so the data is not only validated for quality, but also for relevant to specific CRM needs. - Data quality software should convert customized business rules into a standardized process that transforms and corrects the data.
For example, a standardized and corrected customer record will match associated data coming through other channels and from legacy systems. This ensures that associated customer, financial, product, and historical data is linked to the correct person as well as any external data sources. - Embed customized business rules into your CRM, SCV, and other relevant offline and online customer-centric systems to automate the validation and correction of data at point of capture.
CRM users and supporting teams will all have a higher level of data consistency, quality, and reliability serving their specific business requirements without the delays and cost problems commonly associated with post-CRM data reconciliation.
Proceed with care
Embrace the customer relationship opportunities presented by data, by Big Data, and by the ease of access to on-demand CRM. But take heed: For all the value insight into data can bring, inaccurate data can frustrate and undermine even the best-intentioned customer relationship efforts. By leveraging the capabilities of data quality software, organizations and their customers can enjoy the benefits of a reliable customer view and improved customer relationships.
