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

© 2012 Mike Moran Group LLC

Mike Moran

www.mikemoran.com

What Are Your Customers Saying About You?

Mike MoranRKG SummitMay 2012

Mike Moran

© 2008 Mike Moran© 2012 Mike Moran© 2012 Mike Moran Group LLC

What are they saying about you?

TheConversation

SOCIAL NETWORKSWIKIS

PHOTO SHARING

BLOGSMAINSTREAM MEDIA

MICROBLOGS

FORUMS/NEWSGROUPS

VIDEO SHARING

SOCIAL MEDIA NEWS

AGGREGATORS

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Mike Moran

© 2008 Mike Moran© 2012 Mike Moran3 © 2012 Mike Moran Group LLC

Message 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?

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 check

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Mike Moran

© 2008 Mike Moran© 2012 Mike Moran© 2012 Mike Moran Group LLC5

Social 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 theydon’t like you

Web 1.0 users were consumers

Web 2.0 users are participants

Mike Moran


Marketers do not have message control

We don’t control the message

Maybe we never did

The message is changed, rebutted, and misconstrued by ouraudience

We must modify whatwe say in response

Mike Moran


Social 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 Facebookusers (half are daily users)Source: Facebook, December 2011

YouTube serves threebillion videos per daySource: YouTube, December 2011

695,000 status updates onFacebook—every second!Source: Barry Ritholtz, December 2011

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Mike Moran


The blueprint for social media marketing

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Listen

Mobilize

Engage

Measure

Mike Moran


The blueprint for social media marketing

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Listen

Mobilize

Engage

Measure

Mike Moran


Ford 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 intact

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Mike Moran


Graco handles a recall online

Imagine the nightmare of babystrollers 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 them

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Mike Moran


Comcast’s rude introduction

Comcast’s first exposure tosocial media came from aYouTube video of a service mansleeping on the customer’s couch

They later became one of the first companies to pioneer customer service on Twitter

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Mike Moran


USAA has a social voice of the member program

Product Websites

• Product/Service Ratings & Reviews

• Customer Discussion Forums

• Customer Article/Blog Comments

Social Media

Customer Comments on:

• Facebook Fan pages

• Twitter

Unified Information Access

• Blogs

• Flickr

Surveys and Focus Groups

Real-time Reports

Executives Functional Leaders Regional Leaders

Trend Analysis

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Mike Moran


Listening tells you the lay of the land

Learn what customers think

Decide what you’d like to change about that

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Mike Moran


How do you start listening?

Find your friends

Use search

Follow your favorite bloggers

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Approach 1:

Listen by person

Mike Moran

© 2008 Mike Moran© 2012 Mike Moran

Influencers of the conversation can be scored

Reach

» Site traffic

» Followers/Friends

» Number of social media venues

Authority

» Online rank

» Number of back-links

» Respect or standing within community

Engagement

» # of relevant messages

» Frequency of conversation

Connectivity

» Bloggers blogroll

» Cross-topic connectivity

» Influence flow

• Influencers vary by conversation

• Quantitative scoring can reveal the people who make the conversation

• Human analysts can provide qualitative scoring, also

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Mike Moran


Influencers have relationships with each other

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Mike Moran


Suppose you don’t know who to follow?

See what’s happening now with Twitter search

Use hashtags

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Approach 2:

Listen by topic

Mike Moran


Engagement 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

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Mike Moran


Google Alerts are comprehensive, but slower

Google Alerts are free and easy, but not realtime

Set up a searchand follow thee-mails or anRSS feed

Perfect for smallbusinesses andunique searchkeywords

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But other companies fail with search algorithms

“T-Mobile” will be found quite easily

“Sprint” not so much

“Verizon Wireless” is also not easy to isolate

Mike Moran


How can you compare volumes?

You want to say, “Our volume of conversation is bigger than our competitors”

But…

You can’t ensure theaccuracy of your dataset

The volume goes up allthe time anyway

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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

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Mike Moran


And all mentions of your brand are not good

Top Five Tech Pundits in Smart Phone Conversation

Blog URL Net Sentiment (toward Brand)

Rank Traffic

Engadget AVenue falls in the top 1% of highest

trafficked, most influential sites

GizmodoA

Venue falls in the top 1% of highest trafficked, most influential sites

ElectronistaB

Venue falls in the top 10% of high trafficked sites

UberGizmoB

Venue falls in the top 10% of high trafficked sites

SwitchedC

Venue falls in the bottom 90% of trafficked sites

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Mike Moran


Do 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 Treximet Effectiveness] “…I popped a Treximet, slanted my shades, closed and locked my office door, and put my head down for fifteen minutes. When my alarm went off, my

head was perfectly clear. That was four hours ago.”


[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,..”


• Do you care if the whole post is positive or negative?

• Or what the specifics are for each issue?

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http://blog.lazyharpy.com/2009/02/18/wt.aspx?ref=rss



Mike Moran


What metrics matter?

Trends

Share of Voice

Sentiment

Mike Moran


What do you need to use listening for?

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Market Research

Product Development

Reputation Management

Crisis Management

Sales Leads

Recruiting

Mike Moran


What do you need to use listening for?

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Market Research

Product Development

Reputation Management

Crisis Management

Sales Leads

Recruiting

Need High Relevance Less Relevance OK

Aggregated data OR Individual postings

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 exterior

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Mike Moran


Algorithmic sentiment analysis misses nuance

You know that these are negative, but there is no word to tell the algorithm

These would be marked neutral by most algorithms

“I waited on line for my entire lunch hour

at my Wells Fargo branch today.”

“State Farm never told me I had no flood coverage.” “Amazon wouldn’t

refund my money.”

Mike Moran


Algorithmic 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, butnot “faded” photos

“Frozen” computers arebad, but “frozen”margaritas are good

Mike Moran

© 2008 Mike Moran© 2012 Mike Moran32 © 2010 Mike Moran© 2012 Mike Moran Group LLC

70% accuracy on relevance

70% accuracy on sentiment

70% times 70% = 49%

The problem: Algorithms alone fall short

“Oh, the iPhone is a beautiful girl, no doubt.”

The best algorithms seem to fail half the time

Mike Moran

© 2008 Mike Moran© 2012 Mike Moran© 2010 Mike Moran© 2012 Mike Moran Group LLC

If you need the data to be right, you need people to check the machines

The machines collect the data andmake the easy calls, and theysuggest the answers for the toughones, but humans make the finaldecision

Human analysts can correct the algorithms

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Mike Moran


But that can be expensive

You can’t afford to have people look at everything

What do you instead?

Sampling

Machine learning

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Mike Moran

© 2008 Mike Moran© 2012 Mike Moran© 2010 Mike Moran© 2012 Mike Moran Group LLC

Machine learning algorithms can detect patterns where human analysts corrected the machines

That feedback can then be usedto update the computer algorithmsso the computers are more accurate on the first try

But…you need accurate trainingdata—sometimes lots of trainingdata

Machines can learn from the humans

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Mike Moran


How does machine learning work?

Supervised learning corrects the machine

Unsupervised finds unknown patterns

Semi-supervised corrects when the machine is unsure

Mike Moran


Still 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…

Mike Moran


Yep, the big G…

Google has islistening ears on

No one knowseverything Googlemight be listening to, but…

…two we have evidence for:

Sentiment of links

Human ratings

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Mike Moran


Google was embarrassed by negative links

Making customers irate yields links

Google said sentiment analysis wouldn’t work

But manybelievethey use itnow

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Mike Moran


Google 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 getbumped higher in ranking

The sites they don’t likeare shoved down

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Mike Moran


Machine 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 thelow-rated sites, your sitegets ranked lower

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Mike Moran


What 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, butnot 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!

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Mike Moran


Next: Listen to non-text objects

Audio

Video

Images

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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

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Next: Predictive modeling

Conversation Mining

Traffic

Conversion/Analytics

Sales Brand Tracking

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Mike Moran


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read it again.” --Chris Sherman, Search Engine Watch

Updated at each printing

Web: mikemoran.com

Twitter: @mikemoran

Blog: biznology.com

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

Technology

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