BETA - Building metrics - First steps and best practices

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Want to build metrics but not sure where to start? This article includes some introductory information and data design suggestions to help you on your metric 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 Metrics?

With Metrics, 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 metric

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 metrics.

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 metric.

Note: The sky’s the limit when it comes to creating metrics in Klipfolio®. Looking for some inspiration? Metrics (Beta) includes many pre-built and defined metrics 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 metric, 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 metric 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 metric at each refresh.

Consider how you could refine your raw data to optimize it for metrics. 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 metrics. To fully appreciate this feature, when trying out metrics, 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 metric values are automatically aggregated as you switch between time periods in metrics.
  • 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 metric later.
  • Not all data columns make optimal dimensions. Only include the columns that represent the dimensions you want to see in your metric.
  • Rename columns (optional, depending on the metric'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 metric'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 metric. For example, grouping large sets of individual numbers into ranges of numbers will simplify your metric 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 metrics aren’t necessarily built on the first try. Creating meaningful metrics is often an iterative process. The process of creating a metric is fast and easy so, if you create and explore a metric 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 metric. If you build a metric 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 metric and starting over is if the metric includes stored history that’s not available anywhere else.

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