June 23, 2010
Four Areas of Social Media Analytics in PR
By: Lauren Fernandez
We finally have it. A way for public relations professionals to not only prove qualitative metrics, but quantitative metrics as well. We chatted last week about basic reporting templates, which goes hand in hand with what you need to measure and best practices.
Social media has opened up a door for those in the PR world to prove value and worth to the C-Suite – when normally, it’s the first department to be cut when budgets are tight.
Traditionally, PR professionals focus on “impressions” which can be inaccurate when reporting to clients. Impressions are based on the opportunity to see, not the actual number of people who are reading and absorbing. Many use multipliers to define pass-along readership, but it varies by brand and agency. It’s dubious at best.
Brand and reputation are now up there with media relations since social has been on the scene. So how can you begin to break it down? Every agency and brand are specific to a few things: agency dynamic, brand objectives/goals and client expectations. There isn’t a magic button to tell you what to report in.
We now have online discussion, social media discussion and anything a consumer might say about your brand offline. The last one can be based on in-store promotions, WOM opinion or when experience is related. Makes it a bit tough, right?
There are four areas you can focus on, then make specific and applicable to your clients and/or brand through an analytics approach:
Presentation: What type of exposure has your content and message gained? Is it more so than competition?
Engagement: How are people interacting with the content? What platforms are they identifying with? Who is interacting?
Influencer: How has presentation and engagement altered perceptions and attitudes? Is it positive, negative or neutral? What’s the sentiment? What degree of influencer is this?
End Result: As a result of approach and campaign execution, what has your target demographic done? How did they respond?
What areas would you add? What questions would you focus on? What type of metrics is your C-Suite requesting? Let’s discuss in the comments section.
[...] This post was mentioned on Twitter by Lauren Fernandez, Lauren Vargas, Katie Morse, Alexandra Armeanca, Luana Santos and others. Luana Santos said: Four Areas of Social Media Analytics in PR: http://tinyurl.com/25xcjb3 (via @radian6) [...]
Great post. I think under "engagement" I would also add of those people interacting with the content, how many are posting negative messages or complaints and by that same token how is the organization/brand handling those issues. Are they turning a negative situation into a positive customer service experience? I think there is a lot of value to social media especially when you're able to use the platform to turn around a potentially bad situation and make an angry/dissatisfied customer/client a happier, more loyal one.
I LOVE this point, Ivette. So many brands use response as part of their customer relations model – and it's effective in many cases. How many times have you seen a complaint, where they then turn around and praise because a response was quick/problem solved?
That is definitely something to be considered. Thanks for posting!
Lauren
Lauren –
The multiplier question has been on my mind ever since I read a post on the Burrelles Luce blog http://www.burrellesluce.com/freshideas/2010/04/multipliers-a-way-to-establish-correlations-between-audited-circulation-and-readership-or-just-fluff/)” rel=”nofollow”> target="_blank" rel="nofollow"> <a href="http://(http://www.burrellesluce.com/freshideas/2010/04/multipliers-a-way-to-establish-correlations-between-audited-circulation-and-readership-or-just-fluff/)” target=”_blank”> <a href="http://(http://www.burrellesluce.com/freshideas/2010/04/multipliers-a-way-to-establish-correlations-between-audited-circulation-and-readership-or-just-fluff/)” target=”_blank”>(http://www.burrellesluce.com/freshideas/2010/04/multipliers-a-way-to-establish-correlations-between-audited-circulation-and-readership-or-just-fluff/) about the multiplier effect as it relates to traditional media.
Clearly, the multiplier effect in social media is a different animal altogether. How to measure it? You've given us a nice initial framework. What I'd love to see is how Radian6 can help with some of the analytics in this regard.
One thought related to something I wish one of the monitoring platforms did: Frankly, I've often wondered why a Radian6 or ScoutLabs does not offer Tealium analytics. Tealium social media (which is not a social media platform) provides a clever way (without comprimising personally identifiable info) to tell marketers which sources online are generating the most traffic to their website. Their approach is based on the idea that 70 – 80% of the time, when google analytics or omniture tells you that your web traffic came "directly" from someone typing your url into the address bar, that person was just previously exposed to a social media source mentioning your company. It seems to me, this information can help tremendously in computing the multiplier effect in the social media world, especially as it relates to the web traffic effect which is much more important to many businesses (obsessed as they are now with ROI) than mere buzz.
But the Tealium-type stuff is only one small part of the overall analytics pie. I for one am looking for a clearer picture of the "rest of the pie." Thanks for initiating this important conversation on the social media multiplier effect.
You really got me thinking, Hugh, with your comment!
Radian6 merges with WebTrends so one can overalay the social conversation to actions that matter on a brand's site.
You're right about it being a small piece of the pie – its really up to the brand on what they want to measure and monitor.
Lauren –
The multiplier question has been on my mind ever since I read a post on the Burrelles Luce blog <a href="http://(http://www.burrellesluce.com/freshideas/2010/04/multipliers-a-way-to-establish-correlations-between-audited-circulation-and-readership-or-just-fluff/)” target=”_blank”>(http://www.burrellesluce.com/freshideas/2010/04/multipliers-a-way-to-establish-correlations-between-audited-circulation-and-readership-or-just-fluff/) about the multiplier effect as it relates to traditional media.
Clearly, the multiplier effect in social media is a different animal altogether. How to measure it? You've given us a nice initial framework. What I'd love to see is how Radian6 can help with some of the analytics in this regard.
One thought related to something I wish one of the monitoring platforms did: Frankly, I've often wondered why a Radian6 or ScoutLabs does not offer Tealium analytics. Tealium social media (which is not a social media platform) provides a clever way (without comprimising personally identifiable info) to tell marketers which sources online are generating the most traffic to their website. Their approach is based on the idea that 70 – 80% of the time, when google analytics or omniture tells you that your web traffic came "directly" from someone typing your url into the address bar, that person was just previously exposed to a social media source mentioning your company. It seems to me, this information can help tremendously in computing the multiplier effect in the social media world, especially as it relates to the web traffic effect which is much more important to many businesses (obsessed as they are now with ROI) than mere buzz.
But the Tealium-type stuff is only one small part of the overall analytics pie. I for one am looking for a clearer picture of the "rest of the pie." Thanks for initiating this important conversation on the social media multiplier effect.
In assessing and using the intelligence gained, I am evolving four key metrics or dimensions to help clients understand and use whatever we turn up.
VOLUME. Count the total number of conversations and the relative size of the conversations to calculate baseline awareness. By comparing this number relative to conversations about competitors or about the business vertical, you can understand the relative positioning of your brand. By analyzing the volume by audience segment or over time we can plot the impact of marketing campaigns, promotions, social media activity or news coverage. With a clear sense of awareness and positioning, all kinds of marketing strategy and tactics can be brought to bear.
SENTIMENT. Do they care and are they for you or against you. This vector seeks to understand receptivity to and perceptions of brands. Most of the free tools use baked-in business rules about word proximity and phraseology to determine sentiment. This is useful but not necessarily accurate since it depends on tables of words and phrases pre-determined to be positive or negative.
As a result most tools return bell curve results with the hump in the middle labeled “neutral” which means that the data scanned doesn’t have a preponderance of good or bad words associated with your brand. And while its true that most consumers are ambivalent about most brands; it would be a mistake to accept a “neutral” rating on its face. Sifting thru conversations is required to separate “machine neutrality” from genuine neutrality.
INTENSITY. Borrowing technique from signals intelligence analysis, this dimension seeks to understand where the conversation originates, who responds and if there are changes at specific times or over defined time periods. Gathering this data can suggest seasonality; trigger-events or begin to identify opinion-leaders and market-makers.
IOLs. Informal opinion leaders are bloggers, tweeters, videographers, uploaders, commentators, friends or frequent site visitors who direct, distract, side track, explain or enrich the online conversation. Understanding who they are, what opinions or perspectives they represent and gauging their reach, their relative circle of influence and the consistency of their POVs guides marketers in shaping media and PR outreach. IOLs are the poor man’s focus group to test new initiatives, float trial balloons or drive instant feedback.
These four vectors shape the utility of social media mining and transform raw data into useful intelligence that can be nimbly applied to messaging, marketing and media. All four are evolving as the tool sets and the analysts using them become more familiar with the social universe and how it impacts brands and business categories. Over time, norms and best practices will emerge. But for the moment, these four pathways provide the best approach to understand and utilize what customers are saying about our brands and us.
In assessing and using the intelligence gained, I am evolving four key metrics or dimensions to help clients understand and use whatever we turn up.
VOLUME. Count the total number of conversations and the relative size of the conversations to calculate baseline awareness. By comparing this number relative to conversations about competitors or about the business vertical, you can understand the relative positioning of your brand. By analyzing the volume by audience segment or over time we can plot the impact of marketing campaigns, promotions, social media activity or news coverage. With a clear sense of awareness and positioning, all kinds of marketing strategy and tactics can be brought to bear.
SENTIMENT. Do they care and are they for you or against you. This vector seeks to understand receptivity to and perceptions of brands. Most of the free tools use baked-in business rules about word proximity and phraseology to determine sentiment. This is useful but not necessarily accurate since it depends on tables of words and phrases pre-determined to be positive or negative.
As a result most tools return bell curve results with the hump in the middle labeled “neutral” which means that the data scanned doesn’t have a preponderance of good or bad words associated with your brand. And while its true that most consumers are ambivalent about most brands; it would be a mistake to accept a “neutral” rating on its face. Sifting thru conversations is required to separate “machine neutrality” from genuine neutrality.
INTENSITY. Borrowing technique from signals intelligence analysis, this dimension seeks to understand where the conversation originates, who responds and if there are changes at specific times or over defined time periods. Gathering this data can suggest seasonality; trigger-events or begin to identify opinion-leaders and market-makers.
IOLs. Informal opinion leaders are bloggers, tweeters, videographers, uploaders, commentators, friends or frequent site visitors who direct, distract, side track, explain or enrich the online conversation. Understanding who they are, what opinions or perspectives they represent and gauging their reach, their relative circle of influence and the consistency of their POVs guides marketers in shaping media and PR outreach. IOLs are the poor man’s focus group to test new initiatives, float trial balloons or drive instant feedback.
These four vectors shape the utility of social media mining and transform raw data into useful intelligence that can be nimbly applied to messaging, marketing and media. All four are evolving as the tool sets and the analysts using them become more familiar with the social universe and how it impacts brands and business categories. Over time, norms and best practices will emerge. But for the moment, these four pathways provide the best approach to understand and utilize what customers are saying about our brands and us.
This is great, Danny! Thanks for sharing.
[...] This post was mentioned on Twitter by Solvis Consulting. Solvis Consulting said: RT @rangelie: Nice post by @cubanalaf (on @radian6 blog) re: the four areas of social media analytics in PR: http://bit.ly/9Qxqhy. [...]
I LOVE this point, Ivette. So many brands use response as part of their customer relations model – and it's effective in many cases. How many times have you seen a complaint, where they then turn around and praise because a response was quick/problem solved?
That is definitely something to be considered. Thanks for posting!
Lauren