The Analytics Lifecycle Allows an Iterative Approach to Customer Analytics

Posted on: May 14th, 2013 by Bryan Truex No Comments

In a recent web seminar, Christine Richards, Director of Knowledge Services at the Utility Analytics Institute shared three recommendations for utilities looking toward implementing customer analytics.

  •  Meter data is only one source. While Richards agreed that meter data is important, she also urged utilities to consider other data sources and systems that support customer operations analytics from across the enterprise.
  • Automate for efficiency. Due to the volume of data, it’s critical to determine how to filter which information needs human intervention and which analysis can be automated to reduce the time commitment, freeing employees to focus on more complex customer issues.
  • Create a cross-functional team. Putting the right team in place now and assigning dedicated resources will serve utilities well as customer operations analytics efforts expand.

Finally, Richards urged utilities to view analytics as a tool to reach a strategic goal, not the goal in and of itself. We couldn’t agree more. The value in analytics is in the ability to gain insights that enable utilities to improve services, customer relationships, and reliability across the energy value chain.

Brian Jore, Director of Utility Business Consultant at Teradata followed Richard’s presentation to discuss an iterative approach to customer operations analytics that will make it much easier for utilities to get started. Jore began by defining integrated data analytics as, “combining and correlating disparate data to uncover new business insights and optimize processes.”

The reason utilities need integrated data to perform analytics is to overcome the need for data acrobatics. Jore cited examples of manual processes, spreadsheets, skunk works and disparate applications that make it difficult to pull all the data together and use it to move the needle on business operations.

To combat this status quo, Jore introduced the Analytics Lifecycle.

Applying this construct to customer analytics, a utility starts with smart meter data and surrounds it with other customer data to build the repository of integrated data upon which they can apply segmentation. This segmentation approach quickly enables the targeting of customers that fit the profile for the services you’re creating. And it also proves the value of integrated data to gain buy-in from your utility’s leaders for expanding the reach of your analytics program.

This approach shows how interactions with data can work. For example, integrated data makes it easy to tie into a multi-channel marketing approach and simplify processes because it reuses the same, consistent business rules in the data warehouse to identify these customers while enabling the development of customized marketing messages for use in the call center.

The Analytics Lifecycle gives utilities a bigger return on their investment by creating a framework that’s a foundation for a business process to create new insights that allow business users to interject themselves when a process is not performing as expected. This is much different—and more insightful—than a set of dashboards and reports at month end.

The three-step wheel is meant to continually circle in a clocklike fashion. Each step provides value, but the center is the integrated data concept that continually evolves as the cycle repeats over time. The Analytics Framework effectively removes the manual aspect of gathering and assembling data for each analysis.

Step 1: Analyze & Explore:
This step is not about generating a report. Many utilities don’t know what the output requirement is when they start. In other words, they’ll know it when they see it. Utilities need an environment that allows them to access data, apply different hypotheses and dynamic segmentation that leads to discovery.

Jore advocates leveraging all touch points with a customer to understand where opportunities lie. This process helps to facilitate insights for regular business users, but also for more advanced analysts in the way of providing correlations and path analysis; the primary steps that lead to a certain customer behavior. Once discovered, the utility can actively monitor that behavior and with each iteration better predict what the customer behavior might lead to.

Step 2: Align & Optimize:
Take the insights and customer segments identified in the first step to work toward discovering the ideal combination of products and services for each segment. The utility can also learn to what extent those profiles have been penetrated and maximized.

Additionally, utility marketers can begin to determine marketing channel effectiveness by identifying how customers respond to offerings and communications placed in different channels, such as web, call center, email, and customer portal.

With these insights in hand, channels can now be optimized to take advantage of opportunities. Examples include the ability to increase the adoption of demand response programs for regulated utilities or to scale lead generation for retail energy providers.

Step 3: Production & Tracking:
Through the work done in the first two steps, utilities will develop a number of business rules. These serve as parameters that can be used consistently across channels to produce the output you’re looking for. With this process automated, utilities will begin to not have to interject and manually kick off reports. Instead, they’ll simply run.

With automated reporting, business users can start taking action, understand trends, learn about new opportunities, and identify areas where customer behavior is not moving in a direction the utility wants—credit and collections issues, for example.

Once specific behaviors have been identified, modifications can always be made for refinement. But you’ll also want to track by these parameters to understand the different transitions. This leads you back to Step 1: Analyze & Explore to continue the evolution of the insights you’re gaining from integrated data analytics.

Essentially, the Analytics Lifecycle allows utilities to act on data rather than spending all of their time recreating the data and analysis.

To learn more, watch the on-demand webcast.

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