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Knowledge is power: The more intelligence a business has about its customers, the better it can customize and personalize engagements with them, resulting in higher CX ratings.
The competitive edge that businesses glean from earning top customer ratings is driving many to design 360-degree customer views using AI. Various software applications and cloud-based services using AI can develop a complete picture of each customer.
The information contained in a 360-degree view also supports customer health scores, which 43.6% of organizations now calculate, according the Nemertes Research's "Intelligent Customer Engagement 2019-20" study of 518 organizations.
Customer health score
Customer health scores are numeric scores -- 1 to 10 or a traditional 100% grading system -- or letter grades (A, B, C, D, F) that indicate problems or opportunities with customers. Companies can focus on a few key metrics or use machine learning to track and evaluate hundreds of them. The goal is to show -- through component or roll-up scores -- whether customers are about to leave, whether they may be ready for a purchase or whether they are satisfied with a company, among other details.
Achieving accuracy in those scores depends on a well-thought-out set of metrics and associated scores, accurate data collection, and insightful analysis. Depending on the size of a company, AI and automation may be the only way such scores can happen due to the volume of customer activities. Data gathered for the 360-degree customer view using AI can feed into the customer health score and also help agents know where to focus during interactions.
360-degree view defined
Customer data helps agents deliver a better interaction. It also helps CX leaders analyzing broader customer trends, feedback and characteristics to shift messaging, product development or go-to-market strategies. However, the more practical, day-to-day use of 360-degree customer data resides with contact center agents.
Organizations may have a single definition of their 360-degree customer view that they use companywide, or they may customize each view, depending on products, global regions or type of service they're addressing.
Here are some typical components to the 360-degree view:
Customer history. Using CRM tools -- and some supporting apps, such as support ticket apps and loyalty program management apps -- agents can view all outreach to customers and notes from actual interactions. For example, this data provides context when a customer is aggravated at calling for the fifth time with no resolution to the same problem. Or, it can show something as simple as the customer's credit limit prior to suggesting they buy a new product line.
Customer feedback. This is a large part of the 360-degree customer view and the customer health score. Companies use survey tools, customer engagement apps, natural language processing (NLP) and analytics tools to gather and interpret structured and unstructured feedback. After any customer interaction, 67.6% of organizations send out surveys that include net promoter scores, customer effort scores and internally developed surveys. Businesses may also ask for open-ended comments.
At the same time, customers may rate companies on third-party sites -- such as Hotels.com, OpenTable, Yelp and TripAdvisor -- with both structured data and unstructured, open-ended comments. Business can combine all of this feedback into a 360-degree customer view using AI-enabled analytics tools, including NLP, to translate and interpret unstructured feedback. Agents can see how individual customers rated past interactions for a specific call. Or, CX leaders can regularly review trends and details behind structured ratings, while drilling down into open-ended comment summaries with sample comments that showcase common complaints or compliments.
Customer loyalty. By understanding customers' loyalty to the company, agents can offer extra benefits or offer membership into their loyalty programs. The agent can review profiles in CRM history or customer engagement management software to determine factors such as length of time as a customer, responsiveness to proactive discounts, loyalty program membership or reference history.
Customer background. It's also helpful to know something about each customer's background. For example, socioeconomic status informs agents what clients can afford or for what programs they qualify. Moreover, social software that tracks customers' digital footprints is useful. For example, AI-enabled intelligent routing may send customers with more social media followers to specifically trained agents, or they may simply get priority over others.
Inventory visibility. By having visibility into real-time inventory status, agents can recommend products without the embarrassment of having a customer agree to a purchase, only to discover the product isn't in stock. In addition to inventory software, organizations are starting to use predictive analytics tools. They consider the customer's purchase history, other similar customers' purchasing patterns and myriad external factors that could influence a product recommendation.
Gathering customer data is vital to delivering consistently excellent customer service. But data alone won't do the trick. Analysis of that data, along with authority to act upon that analysis, is what successful companies are doing to continuously improve the customer experience.