Market Thoughts

Still wondering how Operational Intelligence delivers value?  Then, check out the below examples and see how they add up to $2 million:

• Customer Care: A series of seemingly minor problematic customer interactions that, taken in aggregate, cause your customer to leave for another service provider. Imagine a customer abandoning an on-line order, submitting two calls into a service centrer, and perhaps frustrated with an unresolved billing question. Operational Intelligence allows you to connect these events to identify a likely churn candidate and then take action to save this customer. Each saved customer could be worth $1,750 or more to your bottom line.
• Service Assurance: Multiple network and process alarms that indicate an SLA is about to be missed that will require a costly manual escalation. With Operational Intelligence you can correlate alarms with customer and inventory data to identify automated responses. In each case where a manual intervention is avoided, you could save from $25 to $1000 or more in labor costs.
• Operational Improvement: Reactive analysis of what went wrong, but too late to do anything about it since all you have are views of historical information. With Operational Intelligence, you can avoid the cost of this kind of analysis, between $1,000 and $5,000 per instance in analyst time, while delivering more timely insights.

Add these individual events up and you quickly have a significant ROI. For example, you can generate $2 Million in annual operational improvements by saving 15 customers per day, avoiding 110 in bound calls per day and by removing one weekly analysis.

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In my conversations with contacts at large organizations I have repeatedly heard how metrics and KPI initiatives have fallen short, not because the data weren’t aggregated and provided to decision makers quickly and accurately, but because the individuals constituting the teams responsible for running the business on a day-to-day-basis did not fully understand the business context of a metric or KPI; did not have confidence in a given measure or its sources; and never fully appreciated how their teams contributed to the metrics or the resultant initiatives to drive improvement. And the executives, while they had their metrics and visibility, lacked the tools to track consistently the improvement programs they put in place as a result of their interpretation of those metrics.

Visibility is frequently mentioned as a goal of projects aiming to aggregate and roll up metrics and KPIs to executive dashboards.  It usually refers to providing those executives and decision makers improved insight into the underlying measures of the health and performance of the business and to make decisions accordingly. Aiming for visibility and understanding for top decision makers is absolutely critical and obviously a sound goal for these types of initiatives, however, in doing so we risk defining the problem narrowly, ignoring the perspective of a much broader audience charged with improving the underlying business processes and functions that the measures reflect.

The ultimate solution must provide every decision maker and contributor in the chain the visibility and tools necessary to grasp the big picture, their place in it, and their role in shaping and improving it. To achieve these goals, and to ensure a closed loop of optimization, the solution must provide a clear view into three primary attributes of any given metric, namely: Definition and Context; Integrity; and Organizational Accountability and Alignment.

Definition and Context

The Metric Definition and Context provides the metadata necessary for a metric consumer to understand the implications what he sees and the wherewithal to take appropriate action. These metadata should include:

• Description
• Objective
• Goal/Threshold/SLA
• Domain/Organization/Dimension
• Owner
• Time/Space Context

Integrity

At best a metric of dubious integrity is ignored and rendered pointless; at worse it is interpreted on faith and sets in motion decisions and results based on faulty data and assumptions. Providing metric consumers objective indications of a metric’s veracity and accuracy is critical. To do this effectively it is necessary to provide the following:

• Data Source
• Frequency and method of update / Chain of approval
• Owner
• Quality rating

Accountability and Alignment

There will be some overlap here with the previous two attributes, but it’s important to call Accountability and Alignment out separately because ultimately nothing will improve if the functional teams responsible for the business’s performance don’t understand how the work they do fits into the picture composed by and for the executive decision makers. Those in the line of business must know that if they push lever x metric y will respond in fashion z.

• Domain/Organization/Dimension
• Causal chain
• Resolution and program improvement traceability
• Business Function Owner

What we have learned is that while the definition and production of the metric lie at the core of performance improvement initiatives, it is really governance and proper metadata definition and publication that drive success or failure. To ensure success one must employ technology tools and methodology to provide complete visibility and insight into the key metrics as well as the mechanisms in the form of executable and auditable processes and policies to ensure that any improvement initiatives undertaken as a result of the insight and analysis are properly executed.  Without this fully articulated loop these initiatives are bound to succeed only halfway, which is failure by another name.

This question came up in a recent briefing I had with the CIO of a mid-sized European service provider.  Here is the gist of our conversation …

Well, the first thing you do is congratulate the team for a job well done. If all has gone right, the users are happy and the system is efficiently chugging through orders, managing customer care events and generating bills to pay for it all. The team is tired and deserves a little break — at least for a week!

However, after the euphoria wears off there is perhaps something gnawing at you as you watch your the logic of the systems and its processes collide with the reality.   What about all those “low priority” use cases that were cut at the last minute and the highly complex interactions that your highest value customers are demanding which need to be manually managed?

After some investigation, you might find that the sum total of all these “non-automated events” are impacting customer satisfaction metrics more than expected and keeping overall costs from going down as fast as you hoped.  What can you do?

This is where Operational Intelligence can help.  After reviewing the capabilities in M3O Ops Book and M3O BPMS, it became clear how the visibility to see which of these events are causing issues, the insight into how these events might be linked to customers, root causes etc.,  and the ability to act before cost is incurred or, even worse, the customer leaves is so critical.

We’ll be working together over the next month or so to build the case and bring the value of Operational Intelligence to these ‘below the line’ cases that are now front and center.

Drop me a line if you want to talk this through for your own customer care implementations.  I can be reached at lehrhardt@vitria.com.