In the last post, we reviewed the necessary ingredients to make successful predictions: good data. We defined good data as clean, compatible, and chosen well. Let’s next look at a predictive analytics example every PR practitioner will benefit from.
Matching customer behavior to public relations
The purpose of public relations is to generate awareness and trust. We help connect our companies with audiences who matter most to the company, from investors to activists to customers.
However, public relations operates on a calendar largely dictated by either internal operational mandates – “it’s end of quarter, we need more website traffic!” – or editorial calendars for publications. We don’t investigate often enough the way our audiences – the public in public relations – behaves and time our efforts to what the audience wants most.
The PR industry operates this way mainly because we’ve not had access to predictive software or good data. That’s changed in recent years as the data floodgates have opened and predictive software has been democratized. Services like IBM Watson Analytics allow us to do powerful statistics and analytics without being statisticians.
Example: matching search intent to PR
How might we understand customer behavior to inform our public relations efforts? Suppose we looked at a very common data source: search engine data. Using services like Google AdWords™, Google Search Console™, Google Analytics™, or even Google Trends™, we’ll find clean, compatible, well-chosen audience intent data to predict customer behavior.
Let’s take the organic traffic from my personal blog as an example. Suppose I extract five years’ data from my blog of traffic from search:
This traffic data tells me when my audience is searching for topics of interest that my blog answers. If I knew when my audience was searching for topics the most intensely, I’d incorporate this information into my public relations program so that I’d be in the news right at the time when interest was highest.
Using machine learning software such as timekit, scikit-learn, etc. we take this data series and project it forward, using the existing 5 years’ data as the training data. This is what my 2018 looks like:
What do we see above? We see the future: a prediction of what my blog’s likely traffic over the next 12 months will be, based on machine learning analysis of the past 5 years. Let’s look at some key points:
- A: The fall is a good time for my blog – and unlike other B2B marketing sites, I don’t see a Thanksgiving slowdown, just a Christmas one. This I know from other research is because a significant portion of my traffic is from outside the United States.
- B: The first quarter look strong until right around the middle of March. I receive a ton of search traffic in January. This is due in part because I have lots of content about marketing plans and strategies, which are hot search topics in January.
- C: I don’t see much of a summer slowdown. The winter holidays are when I see the biggest search traffic volume drop; otherwise, the summer months are good to keep publishing.
What would PR do?
I personally don’t have a PR agency for my blog, but if I did, I would provide them this data. What would a data-driven PR agency like SHIFT do with it?
First, my PR team would check major editorial calendars to see what topics are on deck in the months to come that coincide with key peaks of interest in my website based on the chart above. For example, here’s the remainder of 2017’s calendar for AdAge magazine:
My PR team would advise me to craft stories, topics, articles, bylines, etc. to match these themes in the months when my search volume is highest, so that I’m in print when people are already finding my website. Using the above as an example, my PR team would advise me to write content they can pitch for precision marketing.
Second, my PR team would help me plan for 2018’s marketing search season. They’d refer me to a Creative Services team to prepare video, audio, and infographics so that they’re in publication – with links to my website – in January.
Third, my PR team would hit up events planning calendars for prominent weeks, like the week of November 26, based on my search traffic. What current events that week could I participate in, even remotely?
Amazon Re:Invent is that week, as is HPE Discover. If I’ve any content that would be appropriate for those events, the team would help me prep it and pitch it now, so that I’m talked about during that week.
Fourth, my PR team would inventory the greatest hits I’ve received over the past few months that have faded into legacy, and prepare paid syndication campaigns to bring them back to life at peak times. Paid syndications make old news fresh again, and having great coverage (as long as it’s still relevant) resurfaced helps ensure I’ve got mindshare when I need it most.
Finally, at the inevitable lulls and dips throughout the year, my PR team would help me craft bylines and other content pieces for a regular drumbeat of coverage at times when my audience’s interest in me is lowest, like in section C in the chart above.
The power to predict
The power to predict is neat in and of itself, but combined with great PR strategy and tactics, the power to predict makes our PR efforts a force to be reckoned with. Instead of guessing when to conduct our most aggressive PR campaigns, or scrambling to react to internal needs, predictive analytics enables PR professionals to bring real strategy to the table.
Predictive analytics is the epitome of data-driven PR. We know, based on data and careful analysis, combined with insight and the most advanced technology, what is likely to happen. Once we know what’s likely to happen, we’re able to build thoughtful strategies, relevant tactics, and timely execution to maximize the impact PR has on our overall business goals.
Next: predictive analytics pitfalls
In the next post, we’ll look at ways predictive analytics goes wrong. Stay tuned!
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