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 KPIs, 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
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 overarching customer experience goals. They
can also provide a more complete view of the implications and value of upstream
More specifically, customer-based process analytics focus on three core sets
1. 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.
2. Avoidable calls to the call center and sub-optimal call handling processes
that can unnecessarily drive up costs while further frustrating customers.
3. 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.
Some providers -- for many good reasons -- will seek to leverage prior efforts
that relied on enterprise data warehouses to create a portfolio of customer
analytics. Unfortunately, the "leverage" opportunity is limited as
BI and predictive solutions were not designed for this type of analytics.
As such, the provider can report on results that are delivered through formal
BI queries, or understand patterns and likely outcomes or behaviors from the
reported results. But they still cannot effectively determine root cause, drill
down to atomic-level data, and systematically detect and eliminate the defects
Nor will they have the flexibly to tap into the different operational systems
as they learn about their business. On top of these limitations, the leverage
opportunity is hampered by the inherent waterfall development process that typically
requires 6-9 months to move from curiosity or need to a workable query that
can produce initial results.
So what is possible? Process analytics enables providers to acquire and analyze
data and the associated process logic in the relevant order-to-cash systems
and sources. It also establishes and analyzes causal relationships by understanding
how the customer transacts and traverses across the process, and identifies
atomic-level defects or find new relationships among the data to enable providers
to attack root cause issues that impact the customer experience.
For example, providers investigating customer complaints will likely encounter
a practical reality: the complaint data is only as good as the customer service
representative inputting the data and often is different than the operational
reality. To understand and address complaints, the actual operational data is
the only reliable "system" of record and is uniquely able to show
what is actually happening to the customer (e.g., errors in the order, system-driven
delays to activation, provisioning errors, multiple on-site visits, etc). Without
process analytics, the provider can determine that something is happening, but
not know why it is happening or how to resolve it.
One example clearly showcases the power and focus of customer-based process
analytics. As part of a critical product launch that was central to their Triple
Play strategy, a leading North American telecommunications provider was experiencing
a significantly faster rate of growth in truck rolls and on-site times than
actual customer adds.
This was creating immediate risk to customer acquisition and churn in their
business, as well as risking the full portfolio of services offered to those
customers. It was also significantly driving up call volumes and overall cost.
The provider attempted unsuccessfully, using existing tools and manual efforts,
to correlate and analyze the range of data and rules that resided in order management,
billing, CRM, dispatch, and trouble management systems, among others.
The operational complexity outstripped the capabilities of the tools, which
could not help identify root cause or enable a continuous improvement program.
The customer then applied customer-based process analytics and was able to rapidly
identify and correct the systemic and individual issues related to products,
delivery models, technical skills, and customer segments that were causing excessive
truck rolls, lengthy time on-site and diminished customer satisfaction.
Winning the customer battle secures revenue in today's economic climate and
is a central lever to driving higher-than-market growth as economies recover.
Providers can quickly and cost-effectively add process analytics to identify
and resolve defects before they erode the customer experience and impact market
share or customer lifetime value.
More to the point, the absence of these types of analytics leaves providers
unnecessarily vulnerable. Process analytics leverages and complements existing
customer service and analytic investments and provides significant operational
visibility, customer value, and financial return.
About the Author
Victor Milligan is Chief Strategy Officer at Martin Dawes Analytics, a leading global process analytics software provider. For more information, contact the author at firstname.lastname@example.org or visit www.mda-data.com.