Learning Measurement: Don’t Overpromise and Under Deliver!

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‘We can prove the learning impacted results!’ Let’s dial this back a bit and not overpromise and under deliver. Even if you have ‘proof’, the stakeholder may be skeptical. So position your data as more evidence rather than proof. Position your data as more reasonable versus perfect. You’ll set expectations better by positioning your data better.

Learning measurement is not just about positioning your data, but about creating a healthy conversation about the story of your impact and then acting on it. A constructive conversation is about reasonable data that may show how learning was associated to the results, versus how the learning caused the results. These are very different statements. The latter is much harder to argue without significant statistical data.

Next, in telling the story of impact, use what is at your disposal to do this. Don’t promise you’ll get actual results, because L&D doesn’t control that. When that promise is made, it is promising something you neither have nor control. Instead, collect reasonable outcome indicators using evaluations, observations, and interviews, and supplement this information with results data you may be given, provided it is valid and reliable.

Next, if you have actual results data, you can’t claim credit for the result, as learning is only a piece of the puzzle. Unless you do the statistical analysis or a deep ROI Impact Study, talk about the correlation you have to the data. Discuss where and what, specifically, L&D contributed during the periods of change. That is a more meaningful conversation.

Finally, discuss how this intelligence can be used to foster a better learning culture going forward, which can result in optimized performance. Using measurement as a tool to manage and improve, in addition to conveying value, is healthy and a best practice.

Do you want to learn more about learning measurement and making it a practical yet effective process? Contact us and we’ll talk!

Thank you,

The Performitv Team

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