Empowering customer service agents with relevant, complete, and accurate answers to customer questions remains one of the major challenges in contact centers today. The past 10 years have seen efficiency and productivity gains squeezed out of the mechanics of routing and queueing a call to the right agent pool, screen-popping the customer information to the agent’s desktop, case management, and workforce optimization. Less attention has been placed on allowing agents to access information and informally collaborate with one another. Its no wonder that more than 70% of the time of an average call is spent locating the right information for the customer.
In many contact centers, content is created by groups of authors who are disconnected from the day-to-day conversations that agents are having with customers and who are unfamiliar with the language and terms that customers use. All content follows the same basic create-edit-publish cycle, irrespective of its usefulness in answering customer questions.
Customer service leaders know that a good customer experience has a quantifiable impact on revenue, as measured by increased rates of repurchase, increased recommendations, and decreased willingness to defect from a brand. They also conceptually understand that clean data is important, but many can’t make the connection between how master data management and data quality investments directly improve customer service metrics. This means that IT initiates data projects more than two-thirds of the time, while data projects that directly affect customer service processes rarely get funded.
What needs to happen is that customer service leaders have to partner with data management pros — often working within IT — to reframe the conversation. Historically, IT organizations would attempt to drive technology investments with the ambiguous goal of “cleaning dirty customer data” within CRM, customer service, and other applications. Instead of this approach, this team must articulate the impact that poor-quality data has on critical business and customer-facing processes.
To do this, start by taking an inventory of the quality of data that is currently available:
Chart the customer service processes that are followed by customer service agents. 80% of customer calls can be attributed to 20% of the issues handled.
Understand what customer, product, order, and past customer interaction data are needed to support these processes.
Eighty-six percent of customer service decision-makers say that a good customer experience is one of their top strategic priorities. Sixty-three percent say that they want their customer experience to be the best in their industry. Yet few companies deliver a good customer experience.
In our recent survey, just over one-third of the 160 large North American brands questioned were found to provide a positive customer experience — a number that hasn’t significantly moved for the past five years.
We know that a bad service experience has quantifiable negative impacts, as measured by monitoring the wallet share of each customer over their engagement lifetime with a brand. But when is a service experience good enough? A recent Harvard Business Review blog says that delighting your customers is a waste of time and energy, and exceeding customer expectations has a negligible impact on customer loyalty — that customers just want simple, quick solutions to their problems.
What customers also want is a consistent, reproducible experience across all touchpoints.
What this means is that a customer wants to receive the same data, the same information, over any voice, electronic, or social communication channel used. Customer service agents supporting customers across these channels should follow the same business processes. And channels should be linked — either from a technology perspective or a business process perspective — so that customers can start a conversation on one channel and move it to the next without having to restart the conversation.