Few would argue that the customer is king; however, these kings are even more powerful as providers focus on market share to ride out the recession, then ARPU gains to achieve better-than average market growth in the recovery period. Throughout this transition, providers are making strategic investments to improve the customer experience and therefore better position themselves to achieve short-term and long-term market and financial performance gains. These gains include reducing operational costs and adding customers in near-saturated markets, avoiding churn--particularly for high customer lifetime value customers, accelerating penetration across product lines, driving increased revenue per customer and customer lifetime value.
In an attempt to support these goals, providers have focused on two areas of investment: business intelligence software to provide stakeholders and executives analyses, dashboards, and reports that show the baseline realities and subsequent progress on customer KPI’s; and predictive analytics that use complex algorithms to predict customer behavior to help guide marketing and other programs. Both of these investments have proven their value over time and can provide a meaningful analytic backbone.
However, these capabilities miss a critical factor in what truly shapes the customer experience and actions in the first place. Organizations must first understand how customers transact and traverse across the large, complex order-to-cash process, from marketing programs through to billing, customer care and field service. This is where the customer lives, and where the strength – or lack of strength – of the provider’s entire order-to-cash process can either enhance or erode the customer experience.
Process analytics, in this case customer-based process analytics, deliver transaction-by-transaction, root-cause based analytics to determine if the sub-processes and systems that comprise the order-to-cash process are operating optimally and whether they are supporting or inhibiting the over-arching customer experience goals. They can also provide a more complete view of the implications and value of upstream marketing programs. More specifically, customer-based process analytics focus on three core sets of issues:
- Errors, delays or confusion in the order-to-cash process (e.g., order-to-activation delays, billing errors, or unclear marketing programs) that can frustrate customers and cause a negative impact to customer experience.
- Avoidable calls to the call center and sub-optimal call handling processes that can unnecessarily drive up costs while further frustrating customers.
- Sub-optimal revenue management in terms of errors that delay revenue and/or collections, call handling issues prompting excessive customer credits, and marketing programs that negatively impact margins by driving up call volumes without the associated ARPU gains.
For example, plenty of attention is given to customer service center benchmarks, such as time-to-answer and time-on-call, as they are proven determinants of operational costs and proxies for measuring customer satisfaction. But why are these customers calling in the first place? Beyond the desirable reasons (adding services), is it also for avoidable reasons such as billing disputes or complaints for late or incorrect services. The calls could be related to marketing inquiries, because customers cannot evaluate a new program or their eligibility. Or, are the follow-up calls because the first call didn’t resolve or perhaps even compounded their initial issue increased their frustration? So while managing service level agreements (SLAs) are vital and good, identifying and attacking the avoidable call volumes and the sources of process inefficiency are what will ultimately enable providers to reduce costs, reduce churn, and enhance the customer experience.
Clearly, there is a powerful business case for conducting customer-based process analytics in complement to existing BI and predictive analytic investments, so why has investment in this area paled in comparison and lagged in terms of time? There are probably several reasons. First, until recently, the ability to capture data and the associated business logic has been relegated to a few very specialized areas of analysis, such as revenue assurance and fraud detection. Secondly, for some providers, solving the challenge to acquire, enrich, correlate, and analyze the customer data across all of the operational sources is simply too challenging.