Question: How do you make data collection easier for AR measurement programs?
This question gets to the heart of the measurement challenge-if it is too difficult to do, it will not get done. Making data collection practical involves selecting the right mix of metrics, leveraging outside resources, and automating many tasks.
[This post focuses on the data collection aspects of an effective measurement program. Therefore, the following related topics will not be addressed 1) picking and prioritizing the right metrics, 2) distinguishing between operational and performance metrics, and 3) using metrics to track performance against pre-defined goals. For discussion of these topics, please see Online SageContentTM Library series “Metrics and Program Measurement.”]
- Selecting the Right Mix of Metrics. First, to make data collection practical, you must pick metrics that meet measurement program goals (e.g. track what you want to measure) and are easy and cost-effective to generate (e.g. data collection requires moderate/minimal effort). Be clear on what you want to measure and collect only that data so you can encourage AR team participation. However, do not reject metrics that initially appear difficult to collect. New options to out-task and automate may make collection easier than you think.
- Leveraging Outside Resources (Out-Tasking). Out-tasking is a variation of out-sourcing, but instead of contracting out a significant part of your AR program (which SageCircle rarely recommends) this technique refers to contracting out an activity or task. Out-tasking is especially appropriate for activities that are […]
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