Building PowerMetrics - First steps and best practices

Want to build PowerMetrics but not sure where to start? This article includes some introductory information and data design suggestions to help you on your PowerMetric journey.

Let’s begin at the beginning .... What is a metric? Metrics help you determine the health of your business. A metric depicts a single numerical value that’s used to track the status of a business process. Some examples of metrics are ‘Average Revenue per Account’, ‘Customer Lifetime Value’, and ‘Website Sessions’.

When you create a metric, you add dimensions, so you can see your performance from different perspectives. Dimensions provide context for the numerical value in your metric and are used to break down, filter, or group your data. Every metric includes at least one time dimension. Some examples of dimensions are country and product type.

Why PowerMetrics?

With PowerMetrics, you can:

  • Keep track of your performance over time and compare current to historical data. We retain your historical data by storing the latest records every time your data is refreshed, gradually building up your data history.
  • Gain insights into your data through interactive exploration. Dig into your data by trying out different visualization types, applying various date ranges, and choosing unique ways to segment and filter your data. It’s quick and easy and you can see the impact of your choices as you experiment and explore.

Before building a PowerMetric

Ask yourself:

  • What business questions do you want to answer?

Having a clear vision of the answers you’re looking for will drive your preliminary data preparation and help you make the right choices when building and exploring your PowerMetrics.

For example:

  • Do you want to know how much it costs your business to gain new customers? The “Customer Acquisition Cost” metric measures the cost of acquiring new customers by adding up sales and marketing costs for a given period, and dividing by the number of customers in that period.
  • Do you want to gauge the popularity of your company’s posts on social media platforms? Use the “Social Media Likes” metric to measure the number of likes on your Twitter or Facebook posts.

No matter what metric you're interested in, make sure you select a data source and dimensions (columns) that include the data you’ll need for your PowerMetric.

Note: The sky’s the limit when it comes to creating PowerMetrics in Klipfolio®. Looking for some inspiration? Klipfolio includes many pre-built and defined PowerMetrics and we are adding more all the time!

Once you know the questions you want to answer, then:

  • Consider your data source.

Think about the question you want answered. Does your data source contain the right information? Does it include the numerical values you want to track or records that can be counted? Does it include all the dimensions you want to use in your PowerMetric, so you can see your data from several perspectives?

Take a few minutes to identify the columns from your data source that you’ll use for the PowerMetric value, the dimensions to segment the data by, and the time dimension. These are the columns from the data source that are stored in the PowerMetric at each refresh.

Consider how you could refine your raw data to optimize it for PowerMetrics. Before modelling your data source, take a look at the data design tips described below.

Data design tips

Here are a few data design best practices:

  • Time and history are at the heart of the value of PowerMetrics. To fully appreciate this feature, when trying out PowerMetrics, choose a data source that includes historical data.
  • Using more granular data, like transactions and hourly data (versus data that’s summarized over longer periods, like by week or by month) enables you to leverage how PowerMetric values are automatically aggregated as you switch between time periods.
  • When modelling a data source:
  • Choose a rich data source that includes columns that can be segmented in multiple ways. Doing so ensures you have lots of options when exploring your PowerMetric later.
  • Not all data columns make optimal dimensions. Only include the columns that represent the dimensions you want to see in your PowerMetric.
  • Rename columns (optional, depending on the PowerMetric's purpose and proposed audience).
  • Don’t “over-prepare” your data. For example, unnecessary grouping or aggregation of your modelled data prevents you from accessing a PowerMetric's flexible ability to display varying levels of granularity.
  • Use formulas to manipulate and optimize your data. Using a formula to group your data into categories (or buckets) can bring simplicity and clarity to your PowerMetric. For example, grouping large sets of individual numbers into ranges of numbers will simplify your PowerMetric presentation. A clearer message is a more powerful message.
  • Transform multiple columns into smart, combined dimensions, for example, concatenate a first name column and a last name column into a name column.

Don't be afraid to start over!

Great PowerMetrics aren’t necessarily built on the first try. Creating meaningful PowerMetrics is often an iterative process. The process of creating a PowerMetric is fast and easy so, if you create and explore a PowerMetric and it’s not what you want it to be, don’t be afraid to delete it. Return to your data source and either edit it further or choose a new data source before creating your next version of the PowerMetric. If you build a PowerMetric and a dimension isn’t useful, then remove it. Less is usually more and there’s almost always a different dimension you can add that will have greater value.

The only exception to deleting a PowerMetric and starting over is if the PowerMetric includes stored history that’s not available anywhere else.

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