3 Findings on Real-Time Trust and Influence in Online Communities

TrustI recently wrote a blog post titled: 3 Social Media Tips for Oogy — The Dog Only a Family Could Love. I wrote this post because I wanted other people to discover this moving and inspiring book about a very special dog and the people who rescued him.  The post was my small contribution to promote the book and hopefully increase its public awareness via social media.  I have no personal or business relationship with the book's author and its publishers.  I just love this book and its beautiful story.

 

After publishing this post on November 1st (late evening), I sent out this message via Twitter on November 3rd (mid-day):

  

Trust - Tweet 

 

I couldn't predict what happened next.  In my opinion, I think the following events and findings are an example of the real-time power of trust and influence in online communities.

 

Thursday, November 4th (approximately 10:30 AM Central Time)
My wife calls me at work and says my blog post is posted on Oogy's Facebook Page!  Unbelievable!  I was busy at work so I couldn't go to Facebook until later in the afternoon.

 

Thursday, November 4th (a little after 4 PM Central Time)
I checked Oogy's Facebook Page and look what I find — I was thrilled and honored!  People even commented on the link posted by Mr. Levin (the author of the book and Oogy's owner).  I left my own Facebook comment thanking Mr. Levin and Oogy for their kindness and generosity in linking to my blog, and I commented on how Oogy's book genuinely inspired and moved me.

 

Trust - Oogy Facebook 
 
 

That evening and over the next few days, I asked myself the following questions:

* What could be the potential impact on my blog post traffic due to Oogy's inbound link and personal referral to his growing legion of Facebook Fans (9,550+ and growing)? 

* What type of real-time influence do Oogy and Mr. Levin have with their Facebook Fans online behaviors (i.e., positive / negative)? 

* Is there a way to quickly measure the impact of this real-time influence?

 

Here's my analysis in addressing these questions using some of the basic features of Google Analytics.

 

Finding #1: Oogy's Facebook Fans Trusted His Referral to My Post

Why? Mr. Levin inserted the link to my blog post on his own.  In my tweet, I made no solicitation or request for an inbound link.  The purpose of my tweet was to bring the attention of Mr. Levin's book and my blog post to my Twitter Followers (and it's a modest 400+ following).  Mr. Levin and Oogy's inbound link was confirmation that I wasn't some spammy website.

 

 

Google Analytics - Oogy FB Referrals 
 

And maybe, they thought I had some worthwhile content to share …

As a bonus, here are some of my favorite articles covering trust and online word-of-mouth (WOM):

* The State of Online Word of Mouth Marketing [STATS] from Mashable
* What Advertising Do Consumers Trust from eMarketer

* Trust Word-of-Mouth from eMarketer
Does Anyone Trust the Media from eMarketer 

 

Finding #2: If Readers Trust the Source, Positive Word Travels at Real-Time Speed

Here's some back-of-the envelope analysis with Google Analytics on how quickly Oogy's Facebook Fans clicked on Mr. Levin's inbound link to access my blog post.  These fans were clearly positively influenced by Mr. Levin and Oogy's referral because they didn't take long in accessing my blog:

* Date/Time Inbound Link was Posted on Oogy's Facebook Page – November 4th, 8:31 AM Eastern Time (assumption because Mr. Levin lives in the Philadelphia, PA area)

* Date/Time of 1st Facebook Visitor's Click to My Blog Post – November 4th, 9:00 AM Eastern Time (my Google Analytics Time Settings are in Central Time so I did the conversion here)

* Real-Time Elapsed Between Inbound Link Post and 1st Visitor Visits Less than 30 minutes.  This 1st visit could have come even faster but I can only measure visitor traffic in Google Analytics on an hourly basis.  I examined data from another web analytics tool, and that tool tells me the post was accessed six (6) times within the first 5 minutes of the Facebook inbound link's placement.

 

Google Analytics - Oogy FB Visits 

 

The swift reaction by Oogy's Facebook Fans to access my blog post emphasizes the real-time speed of the World Wide Web.  David Meerman Scott has published a recent series of blog posts and a new eBook on the World Wide Web's power in real-time marketing and communications for individuals and organizations.  You can access the links here:

* Make Your Website A Real-Time Machine: A Manifesto
* How B2B Companies Use Real-Time Blog Posts to Get Trade Media Exposure
* How Real-Time Communications Drives ROI At Fortune 100 Companies

* (eBook) Real-Time: How Marketing & PR At Speed Drives Measurable Success

 

Finding #3: Oogy's Fans Actually Read The Blog Post — How Cool!

This made me feel really good.  It looks like these new visitors took time to read the article, and I believe the referral from Mr. Levin and Oogy had a lot to do with that.

 

Google Analytics - Oogy FB Referrals 

Conclusion

Oogy's Facebook Fans came to my blog and read my post because they trusted the referral from Oogy and Mr. Levin.  These fans didn't come to my blog because they knew me, or because I'm a widely known blogger.  I'm just starting out in blogging, and I'm trying to build a loyal following and positive reputation one blog post at a time.

It's telling how Oogy's Fans literally arrived at my site in 30 minutes or less after Mr. Levin posted his inbound link to my blog.  The real-time power and influence of trust is truly a driving and powerful force in online communities.

Photo Credit: Terry Johnston via Flickr

Avinash Kaushik’s Rules for Insightful & Actionable Web Analytics

Picture of Avinash Kaushik Amazon Profile Avinash Kaushik, is THE defacto thought leader in web analytics.  I study his blog, Occam's Razor, and his books, Web Analytics an Hour a Day and Web Analytics 2.0.  I've also attended his webinars and am always grateful for the insights he generously shares.

In this blog post, I'll discuss what I've learned from Avinash's webinars.  His insights explain how companies can reinvent measurement of their online marketing initiatives.  Also, I've added extra notes by including content from his books.

 

Rule #1: Review & Take Action on Your Bounce Rates

Avinash is passionate about how marketers can improve the performance of their websites.  This is why he espouses implementing metrics that will reveal when your site is performing poorly (e.g., your site sucks).

J0422750[1] Bounce Rate: A Powerful Measurement of Website Performance and Visitor Behavior.

Avinash defines bounce rate as an "audience behavior" metric that tells you (1) if your visitors are coming to your site and leaving right away or (2) if they're staying longer to read more of your site's content. 

From Web Analytics 2.0, he defines bounce rate as "the percentage of sessions on your website with only one page view." 

 

Examine Your Bounce Rates and Take Action.  Bounce rate benchmarks that he recommends in monitoring and evaluating your blog / website:

  • Good: 25% to 30%
  • Not-so-Good: 50% or higher
  • Low bounce rates may provide clues on undiscovered referral traffic (is that traffic valuable?)
  • High bounce rates provide an indication that audience engagement may be low for particular pages or specific content 

For blogs, a bounce rate might be high for a particluar page or post because the visitor reads the post and then exits.  That's not a bad thing, but if you want to learn more valuable insights your blog, Kaushik recommends examining and segmenting the bounce rates of your new visitors.  Based on his sample screen shots, it looks like segmenting new visitors can be performed in Google Analytics.

Here's a great video by Avinash explaining Bounce Rate.  In particular, I admire his highly technical explanation that Bounce Rate is an indication of your website visitor saying, "I came, I puked, I left."

 

 

Rule #2 Move Beyond The Top 10 & Monitor Statistically Significant Changes/Events

J0439363[1]Look Below the Fold. Too often, marketers focus only on their Top 10 Performing Content Titles or Top 10 Performing Pages for insights (Yes, I plead guilty as charged).  Avinash recommends looking deeper by examining items demonstrating significant statistical changes:

* Keywords that are increasing or decreasing
* Top 10 rising content pages / titles
* Top 10 decreasing content pages / titles

Significant Statistical Changes Can Inform Your Content Strategy Choices.  Monitoring these changes results in evidence for altering content strategy.  He suggests setting up "Alerts" in Google Analytics and experimenting with the alert sensitivities.  By drilling down further, we can uncover insights about:

* Referral Source
* Date
* Geography
* Content Type

Demand More From Your Data So You Can Take Action.  Avinash says he often finds important revelations such as new websites that are driving high referral traffic.  One of the first questions he asks is "was it because someone new is linking to me?" If you're not looking for these types of changes, you may lose great marketing opportunities.  That's why we should all "dig deeper" and "look beyond The Top 10."

Rule #3: Segment, Segment, Segment

J0442403[1] Many Different People have Many Different Intentions.  I love the simplicity and power of that statement.  This is a key insight of well-performed web analytics.  Marketers should always strive to understand why the audience visited their websites and even more importantly try to figure out what the audience was trying to learn during its visit.

Analyze the Depth of Visit.  Avinash defines the "depth of visit" as a distribution of the content consumed (e.g., the number of pages people are reading on a particular visit).  In particular, he likes to look at visitors who are consuming three or more pages.  These are the audience members he defines as "Loyalists," and they are visitors to be cherished.

 

Create and Manage Advanced Segments so You Can Love Your Loyalists.  The example Kaushik described here looked like it could be implemented in the "Manage Advanced Segment >> Create Advanced Segment" of Google Analytics.  This form of analysis will tell you the specific type of content that's preferred by specific visitor.

This type of analyses will help you build simple bar charts that you can analyze by what I'll describe broadly as:

* Number of Content Pages of Topic A, B, or C
* % of the Overall Visits Those Content Pages Garner

By looking at your content this way, you can understand:

* Am I emphasizing a particular topic too much?  Look at the % of visits here and determine if they're low (especially if you've dedicated a high number of content pages to this topic)

* Do I need to create more content around a particular topic?  See if you have a low number of dedicated content pages to this topic but the % of overall is visits high.

Rule #4: We Don't Get Love Because We Don't Make It a Goal

GoalLearn How to Set Up Conversion Goals.  This was a key takeaway and another powerful feature I need to learn and set up in Google Analytics.  Avinash defines conversion rate as "the percentage of people who take action on something that's of value to you."  In the case of blogging, these valuable actions could include:

* Subscribing to your blog or newsletter
* Giving you an email address
* Reading the "about page" of your blog 

 

Rule #5: Silence the HIPPOs by Experimenting and Learning to Fail Fast

HIPPOHIPPOs Make a Website Suck.  Avinash defines HIPPOs as the Highest Paid Person's Opinion.  These folks can make a website suck by imposing their opinion (and this opinion may not represent the voice of your customers).  This is why you should conduct experiments and see how they perform.  Point being, if you're going to fail, "fail well" by failing fast.

Google Web Optimizer allows you to perform A/B testing (and you can test for free!).  Even better, you can learn the results in as quickly as 6.5 minutes — now that's fast! 

A sample experiment might be testing how well a certain type of email campaign performs (i.e., includes all images because that's what the HIPPO wants).  You could test different types of emails such as:

* Text only
* All image
* Hybrid

At least this way, you can validate how well different options work with measurable data.  If you have data to back you up, the HIPPO is dead in the water!

If you have some favorite Avinash Kaushik quotes or learnings from his books or presentations, please post them in the comments.  I would love to learn more pieces of wisdom about web analytics!

A Twitter Tryst: 3 Reasons Why I’m Cheating on My Beloved Tweetdeck (By Using HootSuite)

* Will you forgive me?
* I was thinking of you the whole time …
* It didn't mean anything to me …

Okay, okay, I know these are a bunch of stereotypical cliches to comically describe the types of apologies portrayed in "cheating scenarios" in relationships.  But, I also have have a confession to make.  Please don't hate me Tweetdeck, but I've started using HootSuite!

Why I Fell in Love with TweetDeck in the First Place …

Swiss Army KnifeWhen I first started learning Twitter (about 6 months ago), I found TweetDeck a fantastic tool for broadcasting tweets.  For a Twitter novice, it represented my user-friendly multi-purpose tool, and it made the mechanics of tweeting less daunting.  My favorite features for TweetDeck at that time included:  

* Creating an "executive dashboard" multiple-column view
* Sending retweets (RT's) and @replies and
* Shortening URLs

When I want to monitor Twitter activity on my iPhone, I go straight to my TweetDeck app.  Even on a smaller screen, the information is displayed beautifully and zipping from column-to-column is just a finger swipe away.  But 645 tweets later (as of this morning), I found myself wanting something more …

… and the 3 Reasons Why HootSuite Has Stolen My Twitter Affections

Reason #1 You Can Easily Schedule Tweets for a Future Date or Time.For me this is huge.  I conduct my article research either early in the morning or late at night so I can include their links in my tweets.  Also, I have a demanding full-time, daytime job so sending out tweets during the workday isn't going to work.  As a result, few followers would see my tweets because I was sending them out during "non-peak" Twitter viewing times (at least for my following which is primarily US based).  With the HootSuite Scheduler, this problem is easily addressed with an easy-to-navigate, "point-and-click" and click solution:

HootSuite SchedulerI now rely heavily on this feature and I love it!  Now, I can schedule the tweet to be sent out either the next day or later in the day during "peak Twitter viewing hours" (i.e., 9 AM – 9:30 AM Eastern Time, Noon Eastern Time).

Quite simply, the HootSuite Scheduler increases the likelihood of a follower reading my tweet.

And how do I know that followers are reading my tweets or "engaging with my content?"  That leads us to reason #2 …

Reason#2 You Can Track Twitter Viewer Engagement. HootSuite allows you to view clicks on your URL-shortened links in near real-time.  This capability is very important for organizations who desire to:

(1) Measure audience engagement with their content
(2) Evaluate messaging effectiveness / tweeting effectiveness or
(3) Monitor what type of Twitter content really attracts consumers

Here's the link to the HootSuite YouTube video describing its viewer statistics capabilities.  Also, here are some screen shots from my computer showing how HootSuite can help you in measuring audience engagement, your messaging effectiveness, and content popularity:

HootSuite Summary Statistics

 

 

HootSuite Summary Statistics 2

 

 

HootSuite Summary Statistics 3

 

 

 

 

Reason #3 Viewing User Information is a Lot Easier (My Opinion).  Admittedly, this is subjective argument on my part.  I just like being able to easily view information about other users (especially if they've granted me the privilege of their follow).  In TweetDeck, this feature usually set up what looked like a new column (even though it really wasn't).  The HootSuite solution just appears a little cleaner and more visually pleasing (at least to me).

HootSuite User InformationPlease let me know what you think by leaving me a comment:

* How many of you out there have a preferred Twitter client / user interface?

* Which one do you prefer (i.e, the Twitter web interface, TweetDeck, HootSuite, others?)

* What's the favorite feature(s) of your favorite Twitter Tool?

* Are their other Twitter Tools you can share with us that I need to learn?

Many thanks and I hope to hear from you in your comments!