3 Steps to Analyzing Your PR Measurement Data
Data is essential to successful public relations now and in the future, but 53 percent of professionals still find measurement to be their biggest skills gap.
Will you adapt to the evolving landscape or let competitors lead the way?
If you don’t have accurate data or know how to analyze it, you’ll fail to grasp which campaigns have the most impact on your audience. But with a multi-touch attribution model, you’ll set your measurement strategy up for success by better understanding the roles each campaign and touch plays along the sales journey.
With the resulting data, you can properly analyze why your efforts are or aren’t working and adapt your plan based on these insights. To get an in-depth understanding of your analytics, follow these three steps:
1. Monitor for Shifts
As social media channels alter their algorithms and new competitors emerge, your audience’s needs evolve, which means your PR strategy should too. What worked last month may not work next month, or even next week.
Improve your efforts and reach your PR goals by monitoring for shifts in your outreach activities. To do this, you’ll need media intelligence software, such as Cision PR Edition, to investigate and analyze sudden shifts or stagnant plateaus in your communication history.
Once you’ve detected an out of the ordinary circumstance, you can research new ways of leveraging your campaigns and channels to better reach your audience.
Before you start testing new ideas, identify why previous campaigns or communication flopped or succeeded. Without this vital information, you won’t know, for example, whether your influencer campaigns or webinar topics caused an increase in positive sentiment.
Customer surveys will help you find the answers. Whether you include a one-question survey at the end of your webinar asking what they liked best or a Likert survey, asking customers to rate their experience keeps surveys simple.
On a quarterly or semi-annual basis, engage in more in-depth surveys to gain insights on the entire customer journey, rather than a particular piece of content or event.
3. Start A/B Testing
Data resulting from customer surveys and media intelligence software, will help you develop hypotheses. A/B testing will validate if your conjectures are right.
For example, let’s pretend you received overwhelmingly positive feedback from a recent webinar, and those who attended were more likely to sign up for another event or piece of similarly themed content than previous webinar attendees. What complicates matters is that this particular webinar had fewer sign-ups than past webinars. You might conclude that sending your email invitation later in the day led to a greater likelihood of it going ignored.
In this scenario, you could test your hypothesis by sending two email announcements for your next webinar: one half in the morning and the other half in the afternoon. Making changes to your campaigns without testing out other options would make it much more difficult to determine if this shift was actually a result of bad timing.
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