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Embedded Analytics

A Framework for Packaging Analytics

By Michelle Gardner | January 25, 2018
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In a recent blog, we talked about creating packages of analytics enhancements—or tiered offerings—in order to gain the most value from your analytics capabilities. But how do you know what to include in each tier?

>> Related: Is the Price Right for Your Analytics? <<

The Packaging Decision Framework created by Software Pricing Partners (SPP) is a great place to start. It can help you separate your user base according to usage and perception of value, including application features and embedded analytics features.

Here’s what it looks like:

Plotting Features

To start working with the framework, make a list of all the features you can or want to offer, including embedded analytics capabilities. Place a dot for each feature in one of the quadrants based on how often people use it (x-axis) and how valuable they perceive it to be (y-axis).

To get the position right, you’ll need to research the main user classes that make up your customer mix. These are comprised of users who are similar in terms of how often they use (or will use) each feature in your application and how valuable they perceive these features to be.

Developing User Classes

To develop your user classes, interview a representative sample of users within your customer mix (about six to 12 conversations per market segment). Think in terms of monetizing use of your software via user classes rather than marketing buying personas. While companies may buy your software product, it’s the end users who ultimately use the features.

Try to understand the context of the business problem users are trying to solve with your solution. And don’t forget to reach out to internal experts on your executive team and in tech support, customer service, product development, and other customer-facing roles.

Determining Tiers

Next, group each of the features from your product roadmap on the framework. Here’s how the four quadrants break down:

  • High Usage/Low Value (upper left): These are your basic must-have features, the foundation of your offerings.
  • High Usage/High Value (upper right): These are candidates for premium tiers.
  • Low Usage/High Value (lower right): These are valuable to some of your user base (but not all), and therefore good candidates for extra-cost options or add-on products.
  • Low Usage/Low Value (lower left): These won’t add any value and won’t be used. Nix them.

And here’s what your framework will look like with these groupings:

In placing the dots on the framework, aim for the most accurate representation you can, but don’t get tangled in analysis paralysis. Remember that this exercise is not a one-shot deal. Your packaging should evolve as your customer mix, competitive landscape, and solution evolve. Adjust the position of the dots to reflect any new inputs.

To learn more about this framework and how to apply it to pricing your analytics, read our ebook in conjunction with SPP: How to Package and Price Embedded Analytics.

 

About the Author

Michelle Gardner is the Content Marketing Manager at Logi Analytics. She has over a decade of experience writing and editing content, with a specialty in software and technology.

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