After many years of inside-out strategies, most enterprises are now challenged to change focus to the Customer. Part of the challenge is to understand the customer perspective: what customers want and need from companies and their products. Interactions with customers take place in many venues, most of which are not controlled by the enterprise. The Customer Experience is now multi-channel and much more complex.
Another challenge is to provide one experience across all channels. Sophisticated use of processes, customer intelligence and analytics is helping enterprises enable a unified sense of connection across all interactions that the customer has with the enterprise. To create a single personalized experience continuously, enterprises need intelligence to be based on the right data that is accurate and trustworthy. This means data quality is a strategic piece for the Customer Experience, whether it takes place on a corporate marketing landing page or in customer service.
Just as engendering customer experience excellence requires cross-enterprise collaboration and orchestration of people, processes and practices, the same can be said of achieving the data quality needed to ensure that the right information and intelligence is at the heart of each and every customer interaction. Marketing, sales, customer service, products teams – all need strategies and goals in place to be able to understand how customer information and intelligence will be used, in order to make the right decisions about the quality of the data that will be needed.
Enterprises need real-time, targeted ways of messaging to each customer whether that customer is an individual consumer or another enterprise. Accurate behavioral and demographic data must constantly be available for dynamically-generated offers and messages, particularly for companies engaging in highly targeted marketing initiatives. As campaigns become more specifically directed, the importance of high quality and reliable data escalates. Feeding the right data into marketing automation processes or predictive modeling is not an easy task and will fail if the data is inappropriate or deficient.
Frequently the quality of the relationship between enterprises and their customers is greatly impacted by the quality of processes for collecting and managing customer information. The need for reliable information and data quality runs throughout the various touch points that comprise customer interactions, primarily to give the customer the experience that the customer wants. Real-time data quality processes are gaining importance for keeping up with the constant stream of customer data from many disparate sources.
The ability to support personalized customer experiences depends on timely, high quality data. Personalization is dynamic and only works well when fueled by the most current customer information and intelligence. Customers prefer interactions that encompass their individual preferences, history of purchases, and future needs (predictive analytics). Enterprises can further engender improved customer experiences by analyzing customer feedback about prior interactions, whether it is through corporate channels or social media sites.
All teams in the enterprise should now understand that customer experience excellence translates into quantifiable results: revenue growth, propensity to purchase again, brand loyalty, and brand advocacy. Customer brand advocacy is priceless and cannot be duplicated by any other efforts to attract and gain new customers. From customer intelligence, enterprises should also be making better customer-focused decisions, generating products and services that customers want, and forecasting future directions and new opportunities that tie well to their customers. How do enterprises know that their customer intelligence is giving them valuable insights? Data quality.