Tapping Operational Data to Reduce Costs and Build Customer Loyalty
Given greater competitive pressures, increasing customer expectations, and more touch points to manage than ever before, improving customer satisfaction while simultaneously reducing operational cost remains a significant challenge. Many companies implement business intelligence (BI) tools to monitor and improve performance, but these projects, while requiring significant time and capital investment to complete, often provide little more than visibility into performance and produce no tangible bottom line improvement.
A new approach, operational performance management (OPM), promises to dramatically improve customer service performance where BI, generic data warehouse projects, and use of operational system reports have failed. The reason is simple: OPM has emerged to specifically address the varied and specialized requirements of operations managers. OPM at its best moves beyond general purpose reporting and analytics to provide the right data, best practice analytics, and workflow to understand not only what is going on across an organization's operational landscape, but also why it's happening and how to take action that results in continuous improvement. Three top requirements define OPM, and are critical to the new class of processes and technologies that have been created to support it:
1. Granular, low-level data and metrics: Rather than relying on aggregated data and batch reporting, operations managers need to have daily or intraday visibility into individual transactions, agent- and customer-level performance to ensure they can manage their people, process, and systems areas effectively.
2. Optimize the entire customer experience: Instead of focusing solely on the agent interaction, operations managers need to understand if a customer contact should be handled via self-service, by an agent, or prevented if it is a repeat or unnecessary call. An end-to-end view of customer experience--encompassing all channels and systems--is key.
3. Analytics plus action: Operations managers need more than analytics that simply provide insights--they need a plan of action. True OPM not only generates specific analytics, it also provides workflows that help managers quickly size up the situation and then do something like improve coaching techniques or fix broken processes or systems.
Requirement for granular, low-level data and metrics
Aggregate metrics often keep the true drivers of underperformance from critical view. While aggregate metrics are useful guideposts for top-level managers to assess performance trends, they are ill suited for helping operations managers improve performance driven by people, processes, or systems that they control. Take first call resolution (FCR) as an example. Poor FCR is driven by three causes--agent mistakes, self-service gaps, or process/policy gaps. A manager trying to improve FCR first needs to answer the following question: Are particular agents, self-service failures or policy/ process changes causing low FCR? An aggregate FCR metric can only reveal that call resolution performance is low--it's not linked to the underlying agent, contact sequence, or product data, and thus would not be able to give the manager the required insight to systematically improve FCR. OPM focuses on detailed, operationally linked metrics so that managers can assess the root cause of underperformance in a few clicks versus being forced to guess.
Understand the true customer experience
Typically, organizations looking to improve agent performance and customer satisfaction rely on post-contact surveys or an audit of a small sample of interactions using quality monitoring tools. While these approaches provide valuable insight, they put too much focus on the individual call as a measure of an optimized customer experience. In most contact centers today, the single agent call is NOT the most appropriate barometer of the customer experience--30 to 60 percent of customer contacts are automated via self-service, online transactions continue to grow, and 30 to 50 percent of interactions are actually repeat calls. Linking customer call data to self-service interactions, prior call interactions, and account event data like plan changes provides the required rich context for companies to understand and optimize the entire, end-to-end customer experience. Without this deep context, call center managers are left managing what they can easily measure--efficiency metrics such as AHT (average handle time), ASA, et cetera. With true customer experience data, a manager can work to optimize self-service interactions without sacrificing satisfaction, improve agent handling of calls to reduce repeats and lower AHT, and improve upstream activities that may be causing unwanted calls in the first place.
Let's look at the case of a telecommunications provider that experienced an increase in customer contacts. While BI tools would have simply reported the increase in call volume, OPM was able to identify the customer experience driving the increased number of calls: an increase in repeat calls due to an overflow routing issue. The wrong-specialty overflow-routing issue was causing customers to be transferred, conferenced, and repeat called at a very high rate. The short-term fix was to change the overflow routing rules. In the longer term, the company trained the high repeat call agents on an additional specialty. The added customer experience context of knowing the number of customers with repeat calls coupled with the profiles of the agents with the highest repeat call rate resulted in the difference between solving the problem in less than one day and weeks of dissatisfied and frustrated customers.
Requirement for analytics plus action workflow
Corrective action, and not insights, is the goal of OPM. Stated another way, OPM's objective is to provide a manager with the fact-based conviction to change a person, process, or technology to improve results. Given that OPM is grounded in facilitating action, the analytics offered by solutions that address OPM must be specifically integrated with workflows to facilitate next steps, such as agent coaching and training, IVR rule changes, policy changes, et cetera. As an example, a manager faced with an FCR problem and using an OPM system would automatically monitor teams to identify the best and worst performers. This performance ranking would take account of tenure, call reason, and specialty-mix differences, and indicate which underperforming agents should be trained/coached versus not. The manager would then be able to set an FCR performance threshold and set up an automatic coaching schedule for agents who were below the FCR threshold. This type of focus on operational improvement is only possible through analytics plus action.
The answers to improving customer satisfaction and lowering contact center costs lie within the enterprise. The amount of data generated by customer contact operations is staggering but has sadly produced little actionable information. Far from replacing today's BI tools, warehouses, and operational systems, OPM provides the long missing last mile to tangible operational action by linking disparate source system data to the analytics and action-oriented workflows needed by managers to take corrective action on the people, processes, and technologies impeding performance.
About the Author
Ronald Hildebrandt is cofounder and president of Enkata. He holds a BS in marketing from Saint Joseph's University, and an MBA from Harvard Business School. He can be reached at firstname.lastname@example.org. Please visit www.enkata.com
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