Contact center leaders are aware of these changes and are trying to determine how to use the available data assets to do a better job in serving customers quickly and efficiently. As such, forward-looking companies are implementing more advanced analytics tools, along with richer data sets to better understand their customers’ needs and make faster decisions. To make sure your company doesn’t get left behind, we’ve outlined a strategy to begin transforming contact centers into a modern insights center.
Identify your objective
The value of information is the cost of making the wrong decision. In this context, any analytics that can improve decision making, and produce better business results (e.g., increase revenue, reduce costs, higher NPS scores), is worth doing.
The challenge is to identify the right analytics tools to best help your team meet its goals. The key stakeholders must therefore agree on what the primary objective is that they are focused on. For example, is the goal to increase operational efficiency or customer satisfaction? Answering this question will help determine the scope and direction of your team’s analytics strategy.
Aim for actionable insights
Analytical insights are important provided they affect decision making. This implies the analytics are “actionable” and sufficiently timely (perhaps even in the moment) to make a difference.
For instance, journey analytics are uncovering insights regarding how customers interact with brands and where there may be opportunities to reduce unwanted friction. Not surprisingly, there are often key differences in these journeys depending on what the care issue is and who the customer is.
Contact centers are also using new speech and text analytics tools to understand emerging customer care issues, identify potentially explosive legal issues, and assess customer sentiment, all in the name of improving customer satisfaction. These same tools can also be used to evaluate associate performance and assorted processes that are integral to operational efficiency goals. Other analytics are being used to assess, evaluate, and streamline operational processes, as well as measure associate performance, knowledge management systems, and learning programs.
Regardless of the type of analytics your team chooses to implement, contact center analytic efforts should be aimed at understanding three questions:
- What is happening with respect to activities within a customer case?
- Which customers (e.g, first-time, returning, etc.) are impacted?
- How do customers and associates feel about their experiences?
Create a closed-loop” insights process
It is also important to create a sustainable system for collecting data, analyzing it, testing it, and deriving actionable insights from the data. There are several critical steps to creating a closed-loop process for doing this.
Step 1: Map the existing data flows into and out of the contact center, and, based on preferred, pre-defined customer experience journeys, identify gaps in customer knowledge that could be filled by integrating data from other internal or external sources.
Step 2: Build processes to export the contact center data to a centralized customer data repository on an ongoing basis so that everything that is known about customers’ attributes, attitudes, behaviors, and value can be leveraged from one location.
Step 3: Analyze and model the consolidated data, and orchestrate specialized actions aimed, say, to maximize customer satisfaction while also achieving designated SLAs (service level agreements). These orchestrated actions can (and should) include a wide assortment of champion/challenger tests to support continuous improvements.
Step 4: Develop automated reports and dashboards that will display key metrics pertaining to the various interactions customers have with the brand, as well as the downstream behaviors that will determine the impacts on the business. These downstream metrics could include customer satisfaction and Net Promoter Scores, customer retention rates, product purchase rates, revenue, and profitability.
Step 2: Build processes to export the contact center data to a centralized customer data repository on an ongoing basis so that everything that is known about customers’ attributes, attitudes, behaviors, and value can be leveraged from one location.
Step 3: Analyze and model the consolidated data, and orchestrate specialized actions aimed, say, to maximize customer satisfaction while also achieving designated SLAs (service level agreements). These orchestrated actions can (and should) include a wide assortment of champion/challenger tests to support continuous improvements.
Step 4: Develop automated reports and dashboards that will display key metrics pertaining to the various interactions customers have with the brand, as well as the downstream behaviors that will determine the impacts on the business. These downstream metrics could include customer satisfaction and Net Promoter Scores, customer retention rates, product purchase rates, revenue, and profitability.
Think of the steps as part of a continuous cycle in which data flows into a centralized customer data repository where it can be analyzed and tested to provide insights that further enrich the contact center and the rest of the organization’s understanding of its customers.
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