Text analytics expert Seth Grimes is conducting another of his valuable industry surveys among practitioners, adopters and those thinking about using text analytics. Seth’s insights and guidance have informed my thinking and been cited in my books Listen First! and Digital Metrics Field Guide. A few minutes of your time and the free report are well worth it.
His invitation …
If you are a current text-analytics user — or if you’re exploring ways to tame text — please take part in a survey I am conducting, at http://altaplana.com/TAsurvey. I am researching how organizations apply text analytics, at the information they’re analyzing, what they look for when selecting solutions, and return on investment. I plan to release findings via a free report that will benchmark text-analytics usage and report perceptions about the technology and solutions. (The report from my 2011 study is online.)
The survey has 21 questions. Your response is anonymous (unless you provide contact info) and should take 5-10 minutes. Consultant and researcher responses are quite welcome. If you’re not a direct text-analytics user — but your customer experience, survey analysis, social-media analytics, or other solution relies on text analysis — please take the survey.
Reading a history of the English Coffeehouse reveals striking parallels between its rise and impacts in the 17th and 18th centuries and 21st century social media. They remind us that social media enables, helping give voice to our humanity, expanding and extending our ideas and our selves, connecting and communicating with one another in ways simple and profound, even creating new industries, social organizations and economies.
Most important, the parallels teach us that many of us have asked the wrong question about social media analysis. It is not “Which social media are consumers using, when and what are they doing?” but rather, “How are people expressing their fundamental human nature through social media?”
Please join me in New York at the Sentiment Analysis Symposium Customer Insight Analytics Workshop on March 5 where I will be presenting “The Insider’s Guide to Social Media Measurement”
My morning workshop will explore the world of “humetrics” — the big shift from our industry’s age-old preoccupation with media measurement to understanding people by gauging and interpreting their digital lives. I’ll be sharing my “Digital Metrics Field Guide” to help you recognize data points not merely as impersonal dots on a trend line, percentage changes, or ratios. They are, in fact, personal — capturing what people say, do and feel in real time. Once we view digital metrics as reflecting individuals, they become characters we employ to craft compelling narratives about people and brands that we later share with our colleagues in and outside of our areas. Those narratives fortify brands with a common understanding that increases the potential to act in the best interests of customers and prospects, and to create and execute successful marketing strategies.
The Insider’s Guide to Social Media Measurement will have four sections:
introduction and overview of social media measurement
case studies illustrating the use of social media measures (with audience participation and comments)
hands-on workshop. We will present three typical business scenarios and have the attendees work through them, including developing an analytic strategy, measurement plan, etc.
wrap-up and final Q&A
I will also be joined in presenting the workshop by Vincent Santino, Associate Director of Digital Insights & Analytics at Kaplan Test Prep.
After the workshop, continue to learn from other leaders in the industry, including Amazon, American Express, Huffington Post, IBM and more at the symposium on March 6. To learn more, please visit http://www.sentimentsymposium.com/workshops.html and use the codeFOAF to save 10% if you register before January 25.
Ratings we assign are influenced by other ratings that we read. New research conducted by Sinan Aral and colleagues at MIT’s Sloan School found that:
“When it comes to online ratings, our herd instincts combine with our susceptibility to positive ‘social influence.’ When we see that other people have appreciated a certain book, enjoyed a hotel or restaurant or liked a particular doctor — and rewarded them with a high online rating — this can cause us to feel the same positive feelings about the book, hotel, restaurant or doctor and to likewise provide a similarly high online rating.
This important finding was discovered too late to be included in the Field Guide’s entry on “Influencers.” That discussion pointed out the importance of the herd model and urged that it be considered along with the widely adopted Influencer-Follower (two-step) model. The influencer two-step is very popular because: a) it conforms to the conventional mental models we evoke to explain how advertising works (authority, message, persuasion), b) because measures of influence, such as Klout scores, are computed in line with the two-step model — using social media counts such as posts/updates, number of friends/followers/contacts, and sharing, and c) herd influence has been under-recognized.
Despite books and articles on herd instincts in marketing, knowledge about herd instincts and its applicability to the work we do is not yet generally known by practitioners. Adding to herd instincts’ invisibility: Herd instincts measures are not reported by measurement services, so most of us are unaware of the herd notion. Herd measures are not easily derived from social media metrics. Methods for researching herd instincts scientifically in marketing and advertising are not in the market research tookit. This study changes that at last … and to our benefit.
Key Implication: Brands should oversee their ratings sections to minimize fraudulent positive ratings. Those “false positives” can create unrealistic expectations. Instead, encourage people to record authentic ratings that minimize bandwagon effects and foster realistic expectations about the brand. Ratings may then generate better guidance to other readers and to the brand itself.