By Katie Eickhoff, Social Media Manager/Senior Analyst
One of the easiest ways to start your development of a measurement plan is to develop measurement goals. Whether you’re looking to find out whether a particular message reached a particular audience or whether you were perceived favorably over a particular period of time versus your competitors, pick something you’d like to learn from your data and then determine how you can gather it. Goal-setting is a vital means of gathering useful analytics and I’m always glad to see when people tout the benefits of goal-setting and consistently flabbergasted when people overlook targeting their goals.
One of the easiest ways to skip that goal-setting step and simply gather information to figure out what to do with later, is automated data collection. And no recent example screams “automated data crunch without a purpose” more than the revelation of the National Security Agency’s breadth of data collection. While collecting data for surveillance purposes and collecting data for the public relations world are certainly far apart in their respective subject matters, the NSA revelation provides a prime example of the questions that arise when a lack of stated goals results in a massive data crunch, with an astronomically-sized margin of error size.
NSA data collection is stated to have no goals, merely this “unsupervised learning” that is touted as being the best way to go about their business. From what’s been reported, they primarily rely on automated programs and algorithms pouring over data sets with no goal (or too general ones to be useful) in mind.
The NSA’s analytics provide a macro example of someone going to their boss and saying “I want to measure EVERYTHING said about us, our competitors, and our customers” and then not setting any goals from that point on. Not only does this waste a measurement budget but it robs the measurement organizer of the ability to set landmarks for success and alienates anyone looking at the measured data. To new team members, owners, bosses a lack of a measurement plan/goals is a scary useless thing and you’ll see them rebel against measurement & question whether it’s worth the time or budget.
Keeping your goals within sight of our data is also why it’s useful to have a visible human to check, sample, interpret data as well as modify data collection and analysis methods. Human analysts keep a real-time watch on whether data is meeting your goals and help you develop new goals as new crises arise.
The intelligence business may turn to “unsupervised learning,” but in doing so, they prove that lacking goals makes for data of questionable use both inside and outside of an agency. Setting measurement goals helps prove the worth of your campaign and ensures to parties inside of your company and out, why the data you’re looking at is useful.