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What Are Your Customers Saying about You? - RKG Summit May 2012
 

What Are Your Customers Saying about You? - RKG Summit May 2012

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Social media usage is exploding. All the cool kids are doing it. But how do you keep up with what people are saying about your brand? How do you know if they are positive or negative? Do you know how ...

Social media usage is exploding. All the cool kids are doing it. But how do you keep up with what people are saying about your brand? How do you know if they are positive or negative? Do you know how computers "listen" to social media? To answer those questions, and many more, you need a social media listening program. You could start with a basic tool, such as Google Alerts, but many businesses find that they need more. At this event, see real-world case studies that show social listening in action.

Discover what tools are available today (and what their shortcomings are) and learn how social media listening tools are evolving. Find out how to keep up with all the conversation about your company and their industry in social media, so that you can respond to protect your reputation.

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    What Are Your Customers Saying about You? - RKG Summit May 2012 What Are Your Customers Saying about You? - RKG Summit May 2012 Presentation Transcript

    • Mike MoranWhat Are Your Customers Saying About You? Mike Moran RKG Summit May 2012www.mikemoran.com © 2012 Mike Moran Group LLC
    • Mike MoranWhat are they saying about you? FORUMS/NEWSGROUPS MICROBLOGS VIDEO SHARING SOCIAL NETWORKS WIKIS The Conversation SOCIAL MEDIA NEWS PHOTO SHARING AGGREGATORS MAINSTREAM MEDIA BLOGS 22 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranMessage boards have long been complaint centers  Would you have spotted this comment?  Would someone know how to respond?  What if that is one my competitors?3 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran But now all you need is a phone  Your customers look at reviews before going into your restaurant  Or writing a review while they wait for the check4 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranSocial media now makes marketing a conversation  Readers comment on your blogs  They change your wikis  The create blogs of their own  They create “hate” sites if they don’t like you Web 1.0 users were consumers Web 2.0 users are participants5 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranMarketers do not have message control  We don’t control the message  Maybe we never did  The message is changed, rebutted, and misconstrued by our audience  We must modify what we say in response6 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranSocial media is exploding  164 million blogs (over 1M new posts each day) Source: Invesp, July 2011  250 million tweets per day (almost tripled in one year) Source: Twiter, October 2011  800 million Facebook users (half are daily users) Source: Facebook, December 2011  YouTube serves three billion videos per day Source: YouTube, December 2011  695,000 status updates on Facebook—every second! Source: Barry Ritholtz, December 20117 © 2012 Mike 7 © 2012 Mike©MoranMike Moran 2008 Group LLC
    • Mike MoranThe blueprint for social media marketing Listen Mobilize Engage Measure8 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranThe blueprint for social media marketing Listen Mobilize Engage Measure9 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranFord contains a potential disaster  A Ford fan site goes public with a Ford cease and desist order that goes viral on the Web  Scott Monty of Ford tweets “not good” when he first hears the story  Later, his legal team explains that the fan site was selling counterfeit Ford items and he tweets that  Within 24 hours, the story is dead, with Ford’s reputation intact10 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranGraco handles a recall online  Imagine the nightmare of baby strollers recalled for safety reasons  Graco jumped on Twitter and responded to every nervous tweet, requesting serial numbers and providing advice  Afterwards, as many stories praised Graco as slammed them11 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranComcast’s rude introduction  Comcast’s first exposure to social media came from a YouTube video of a service man sleeping on the customer’s couch  They later became one of the first companies to pioneer customer service on Twitter12 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran USAA has a social voice of the member program Executives Functional Leaders Regional Leaders Real-time Trend Reports Analysis Unified Information Access Product Websites Social Media Surveys and Focus Groups • Product/Service Ratings & Reviews Customer Comments on: • Customer Discussion Forums • Facebook Fan pages • Blogs • Customer Article/Blog Comments • Twitter • Flickr 1313 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranListening tells you the lay of the land  Learn what customers think  Decide what you’d like to change about that14 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran How do you start listening?  Find your friends  Use search  Follow your favorite bloggers Approach 1: Listen by person 1515 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranInfluencers of the conversation can be scored Reach » Site traffic • Influencers vary by » Followers/Friends conversation » Number of social media venues • Quantitative scoring Engagement Authority can reveal the people » # of relevant » Online rank who make the messages » Number of back- » Frequency of links conversation conversation » Respect or standing within community • Human analysts can Connectivity » Bloggers blogroll provide qualitative » Cross-topic scoring, also connectivity » Influence flow16 © 2012 Mike Moran © 2008 Mike
    • Mike MoranInfluencers have relationships with each other17 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Suppose you don’t know who to follow?  See what’s happening now with Twitter search  Use hashtags Approach 2: Listen by topic 1818 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranEngagement tools do simple listening  Choose people to follow  Enter simple keywords or hashtags for subjects  Only finds Twitter data, with possibly some blogs or Facebook  Hootsuite is a prime example  Seesmic is also popular  Tweetdeck being purchased by Twitter 1919 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Google Alerts are comprehensive, but slower  Google Alerts are free and easy, but not realtime  Set up a search and follow the e-mails or an RSS feed  Perfect for small businesses and unique search keywords 2020 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranBut other companies fail with search algorithms  “T-Mobile” will be found quite easily  “Sprint” not so much  “Verizon Wireless” is also not easy to isolate21 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranHow can you compare volumes?  You want to say, “Our volume of conversation is bigger than our competitors”  But…  You can’t ensure the accuracy of your dataset  The volume goes up all the time anyway22 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran And no technology sees all the data, anyway  Public: Twitter, blogs, YouTube, most message boards  Private: Most Facebook, most LinkedIn, some message boards  In between: Reviews 2323 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranAnd all mentions of your brand are not good Top Five Tech Pundits in Smart Phone Conversation Blog URL Net Sentiment Rank Traffic (toward Brand) Venue falls in the top 1% of highest Engadget A trafficked, most influential sites Venue falls in the top 1% of highest Gizmodo A trafficked, most influential sites Venue falls in the top 10% of high Electronista B trafficked sites Venue falls in the top 10% of high UberGizmo B trafficked sites Venue falls in the bottom 90% of Switched C trafficked sites24 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranDo you want to measure postings or mentions? [Negative for Treximet Cardio Side Effects] “I did a little research of my own, and was a bit alarmed to come upon a forum of migraine sufferers who had tried the drug and reported tightness in the chest and other indications of cardiac disturbance. I decided to leave the Treximet in the desk, untouched. “ Blog.lazyharpy.com, published on 18-02-2009 [Positive for Cardio Side Effects] “For some reason, today I picked it up. I did a quick web search for adverse side effects of Imitrex, which I have used for years, and felt surprised to see the very same descriptions as those accompanying the Treximet. Since Imitrex has never bothered me,..” Blog.lazyharpy.com, published on 18-02-2009• Do you care if the whole post is positive or negative? [Positive for Treximet Effectiveness] “…I popped a Treximet, slanted my shades, closed and locked my office door, and put• Or what the specifics are for my head down for fifteen minutes. When my alarm went off, my each issue? head was perfectly clear. That was four hours ago.” Blog.lazyharpy.com, published on 18-02-2009 2525 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranWhat metrics matter? Trends Sentiment Share of Voice26 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranWhat do you need to use listening for?  Market Research  Crisis Management  Product Development  Sales Leads  Reputation Management  Recruiting27 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranWhat do you need to use listening for? Need High Relevance Less Relevance OK  Market Research  Crisis Management  Product Development  Sales Leads  Reputation Management  Recruiting Aggregated data OR Individual postings28 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Algorithmic sentiment analysis misses sarcasm “Oh, the iPhone is a beautiful girl, no doubt.” The automated sentiment analysis failed to identify the sarcasm and coded the entry as positive for iPhone, while failing to understand the author was actually saying there was no value for the iPhone beneath its flashy exterior29 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranAlgorithmic sentiment analysis misses nuance “I waited on line for  You know that my entire lunch hour these are negative, at my Wells Fargo but there is no word branch today.” to tell the algorithm  These would be marked neutral by “State Farm never most algorithms told me I had no flood coverage.” “Amazon wouldn’t refund my money.”30 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranAlgorithmic sentiment misses context  The same words mean different things  An “unpredictable” movie is good, but “unpredictable” food quality, not so much  We like “small” cell phones but not “small” hotel rooms  “Faded” jeans are good, but not “faded” photos  “Frozen” computers are bad, but “frozen” margaritas are good31 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranThe problem: Algorithms alone fall short  70% accuracy on relevance  70% accuracy on sentiment  70% times 70% = 49% “Oh, the iPhone is a beautiful girl, no doubt.” The best algorithms seem to fail half the time32 © 2012 Mike©2010 Mike Moran © MoranMike LLC 2012 Group 2008
    • Mike Moran Human analysts can correct the algorithms  If you need the data to be right, you need people to check the machines  The machines collect the data and make the easy calls, and they suggest the answers for the tough ones, but humans make the final decision 3333 © 2012 Mike©2010 Mike Moran © MoranMike LLC 2012 Group 2008
    • Mike MoranBut that can be expensive  You can’t afford to have people look at everything  What do you instead?  Sampling  Machine learning34 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Machines can learn from the humans  Machine learning algorithms can detect patterns where human analysts corrected the machines  That feedback can then be used to update the computer algorithms so the computers are more accurate on the first try  But…you need accurate training data—sometimes lots of training data 3535 © 2012 Mike©2010 Mike Moran © MoranMike LLC 2012 Group 2008
    • Mike MoranHow does machine learning work?  Supervised learning corrects the machine  Unsupervised finds unknown patterns  Semi-supervised corrects when the machine is unsure36 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranStill don’t care what people are saying?  You can take this approach  No one can force you to listen  But you might be interested in knowing who is listening…37 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranYep, the big G…  Google has is listening ears on  No one knows everything Google might be listening to, but…  …two we have evidence for:  Sentiment of links  Human ratings38 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranGoogle was embarrassed by negative links  Making customers irate yields links  Google said sentiment analysis wouldn’t work  But many believe they use it now39 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranGoogle Panda listens to human raters  Human beings rate a small subset of search results:  Nice design?  Speedy response?  Quality content?  Would you return?  Sites that people like get bumped higher in ranking  The sites they don’t like are shoved down40 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranMachine learning scales the human ratings  Even Google can’t afford human ratings for every page for every search  So, it looks for patterns—common features  If your site looks like the low-rated sites, your site gets ranked lower41 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranWhat is the practical effect of Panda?  Sites that ranked highly with the old algorithm have been affected  If your site was great for search engines, but not for actual people, time to up your game  Who seemed to get hit?  “Content farms” and screen-scrapers  Older content  Sites loaded with ads  Vertical search sites—but not Google sites!42 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Next: Listen to non-text objects  Audio  Video  Images 4343 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Next: Cross national languages  Those who need to know can’t speak every language  Machine translation crosses the gap  Automation will be augmented with human beings at first 4444 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike Moran Next: Predictive modeling Sales Brand Tracking Conversion/Analytics Traffic Conversation Mining 4545 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC
    • Mike MoranRead all about it  “Buy this book, read it, and then The read it again.” search --Chris Sherman, Search Engine Watch marketing  Updated at each printing best seller Miami Herald: A Top Biz Book of 2007  “Great book.” --Robert Scoble, Scoblizer blogWeb: mikemoran.com  “Act now and readTwitter: @mikemoran it.” --Bryan Eisenberg, Author of #1 best seller Waiting for Your Cat to Bark?Blog: biznology.com46 © 2012 Mike©MoranMike Moran © 2012 Mike 2008 Group LLC