6 Training & Adoption

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Training and adoption go hand in hand in ensuring you are getting value from your analytics application investment. Both are an integral part of the validation loop that measures value.

Jump to Adoption


Users understanding how to use an application is the best way to ensure adoption of a new system, and training is an important part of that process. End user training is best done when it is timed with deployment of new releases, particularly if a release contains a new system entirely.

We approach training in multiple ways.  There is traditional user role based in-person classroom training, which is the most expensive and least scalable, especially for global users, but most effective in driving adoption. Classroom sessions can be video recorded for use on demand later.  An alternative to in-person training is video conference training, which is less effective than classroom, but more effective than training with no human presence at all. Another option is pre-recorded online screen capture shorts that can be accessed anytime as needed. This is a scalable approach for a dispersed user community. Online shorts should be long enough to encapsulate a feature area but short enough to hold users’ attention for the duration. A quick guide is 3 to 10 minutes long for each segment.

There are other training options, like chat bots, quick reference guides or user supported offerings, such as online user groups where users can ask questions of other users. Documentation is also an effective tool for explaining how an application works and what the features are.


Adoption is the measurement of how many users are using the system to complete a use case for which the application was built. Measuring adoption can be both quantitative and qualitative.

Quantitative Feedback

Defining benchmarks of what adoption should be enables you to use quantitative measurements to determine if you are on target. If you are not on target, you have to decompose the gaps to understand why adoption is off. Quantitative benchmarks are data driven and measured by usage statistics. For example, how many users are on the system and for how long, and which data sets/dashboards they are accessing. You can also measure statistics on system uptime and data loading performance.

If you cannot easily discern from the initial layer of usage statistics why adoption is less than target, you can decompose the numbers until you find root cause. Root cause may be due to a number of reasons: difficulty using the system, lack of right features, login issues, or other areas. These are application level features, but there also may be a problem with the underlying data. If users do not have high confidence the data is correct or if data delivery is not timely, usage may drop. Data quality checks and data architecture are part of end-to-end development. Ensuring good architecture that is aligned with the way your business metricizes data, as well as data quality testing, helps ensure data issues are minimized.

Qualitative Feedback

Qualitative feedback is gathered through methods like training reviews, user groups, facilitated workshops, and one-on-one meetings. Using these methods, users may explain a desired workflow that doesn’t already exist or they might articulate a problem they are having that cannot be easily quantified using data driven methods. Collecting qualitative feedback is a great way to establish relationships with the user base and gives them an outlet that is more personal and meaningful than contributing quantitatively.

While training is a great way to drive adoption, another means is to use one or more ambassadors embedded within target user groups to promote the application and be a touch point for training, feedback, new feature requests, and ongoing maintenance requirements. The use of ambassadors is part of our program framework that builds cohesive programs across all stakeholder groups.

Whatever your environment, we can design and implement a training and adoption model that fits your needs and budget.

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