Making Data Collection for Measurement Practical [AR Practitioner Question]

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 SageContent^TM Library series “Metrics and Program Measurement.”]

SageCircle Technique:

• 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 non-strategic, repetitive and routine. Economies of scale permit third-party AR providers to invest in the tools, processes, and databases required to accomplish these tasks more efficiently. Data collection activities that are good candidates for out-tasking include: 1) analyst AR effectiveness surveys, 2) press quote collection, 3) written research analysis, and 4) basic analyst information tracking and storage.
• Automating Some Tasks. Automation can make data collection, especially for operational metrics, as simple as creating a query or running a standard report. For example, a common operational metric is the AR team’s number of analyst interactions. Traditionally, collecting this data meant searching calendars, finding post-its, and combing spreadsheets. AR teams can automate this task by using an integrated program management application to enter analyst interactions as they occur. Then, they can generate the number of interactions metric in a minute with a couple of mouse clicks.

Bottom Line: To collect and analyze the data for an effective measurement program, AR needs to be practical about the effort required to gather and report on metrics. You can increase the success of your measurement program through carefully selecting your metrics and out-tasking or automating their collection and reporting. 

Question: Which of these data collection techniques do you use?