November 22, 2017

Helping PR pros make smarter decisions

A New Measurement Metric

A New Measurement Metric

I recently attended the PR News Measurement Conference in Washington, D.C.; interesting conferences always set the mind to work. Of course, the main topic was measurement and how to incorporate appropriate and useful measurements and metrics into PR work.

Thankfully, Ad Value Equivalency (AVE) is dying, albeit a bit slowly for my taste. And even impressions–the “gold standard” of window-shopping eyeball counting–is falling on some disfavor. One of the more interesting sessions was on a measurement called “Weighted Media Costs.” While this measurement still ties costs to measurement, the charts and demonstration of correlation cannot be easily dismissed.

However, one area not addressed at all by the conference was the differences in time spent on online versus offline reading, and how much reading comprehension is gained or lost due to the length of an article. To that end, CustomScoop is developing a new metric that we think addresses this in a way that is tailored to our rushed lifestyles and limited attention spans.

How we developed our formula

Given Twitter’s rise and the limited ability of many to set aside time to read long-form news articles, blog posts, and even longer Facebook status updates, the core thought behind the formula is to value brevity. Twitter’s 140-character limit seemed a good place to start. We are calling this the Twitter Correlative Value. So, the length of an article or blog post in characters over the 140 limit ranks lower on our scale, under ranks higher. Think of it in terms of a golf score–par is even, but it’s better to be under than over. So, TCV=Total Character Count – 140.

Next, of course you have to consider the value of tonality. A short, negative post is more likely to be remembered by even the most attention-starved consumer, whereas a long form article with lots of words can meander all over the place. Not only does the tonality become more problematic to detect, but there’s a much higher likelihood that competitors get mentioned (either positively, negatively, or neutrally), which further muddles the message. Plus, who knows if anyone is even going to get to the end of a long-form piece. So, we need to take the proximity of the tone to the brand/message/issue into consideration. The closer the tonal word is to the keywords identified by the user, the more likely such tonality is to be absorbed by the end reader, so the number assigned to this is 100 for right next to the keyword. For example, if the keyword identified by our system is “Jen Zingsheim” a post that states “Jen Zingsheim rocks” would have an opinion proximity of 100. A number is subtracted for each word in between the keyword and the tonal word. So “Jen Zingsheim has a dog, lives in New Hampshire, and oh, BTW, she rocks” would have an opinion proximity of 89, and so on. We are calling this value the Net Opinion Proximity Evaluation.

So, the new metric takes into account the length of a piece and the proximity of the assessment of the keyword by the post/article’s author. So, in the first case (“Jen Zingsheim rocks”) the first score is 19-140=-121, then add 100 for a value of -21. Remember golf scores–this number (-21) clearly indicates the reader has a better chance of absorbing the keyword, message, meaning, and may even remember the entire post. The second example scores lower: 71-140=-69, then add 89 for a total value of 20.

So the Brevity Scoring feature will allow people to quickly determine what messages will get across to consumers, and if they are likely to retain the message.

What do you think about our new Net Opinion Proximity Evaluation (NOPE) and Brevity Scoring (BS)? Let us know in the comments.

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About The Author

Jennifer Zingsheim Phillips is the founder of 4L Strategies, and has worked in communications and public affairs for just over 20 years. Her background includes work in politics, government, lobbying, public affairs PR work, content creation, and digital and social communications and media analysis.

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5 Comments

  1. info@shonaliburke.com'
    Shonali Burke

    Jen, I've been trying to get my head around Weighted Media Cost as well and, as you (and my friend and measurement rock star @commAMMO aka Sean Williams says) the correlations can't easily be dismissed, but I still have trouble getting beyond the dollar-equivalent side of it… which is why I'm not adopting it with open arms… yet.

    Your new metric at CustomScoop (of which I'm admittedly a happy client and fan) is certainly interesting. It seems pretty easy to implement and adopt, which is a plus. What I always wonder about is what people will actually DO with such scores, and how this actually impacts behavior which will lead to the desired outcomes. As you know, that's what I want people to focus on; not just the numbers, or numbers without context, but what their activity and outputs are leading to in terms of outcomes.

    I also wonder if by the "reverse scoring," i.e. use of the – sign to indicate a higher score, people might get confused and associate the – with a negative score? Of course, you'd educate them, but it might be a bit of an uphill climb. Just my $0.02. Thanks for giving me something good to mull over this morning!

    1. jzingsheim@customscoop.com'
      Jen Zingsheim

      Shonali, first, our apologies it’s taken a while to post the comment. Something’s gone wonky and we now need to approve all comments manually–clearly we’ve inadvertently modified a setting.

      Next, you made my day with this comment. I must point out the date of the post (April 1) and the acronyms of the metrics–NOPE/BS. We were just April Foolin!

      Have a great weekend!

      Jen

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