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Are You listening to the Voice of The Customer Lean?

Posted by kyle toppazzini on Wed, Aug 22, 2012

Lean Six Sigma, Sigma, Voice of the Customer,Social MediaIn the last decade witnessed the explosion of social networking sites, such as LinkedIn and Twitter, has been pushing companies to be more conscious of the Voice of Customer than ever.  According to “The 2012 American Express® Global Customer Service Barometer, a survey conducted by American Express, it indicated that, on average, people using social media for customer service are likely to tell their negative customer experience up to 53 people, compared to 24 people for those “non-social media” people. Keep in mind that these statistics are probably underestimated the “true impact” as it is difficult to know how many people read the complaint when posted on any social media sites.

In a Lean Six Sigma engagement, I often conduct an assessment of the voice of the customer (VOC) as one of the first activities in the Lean Six Sigma process. I look for the factors that 1) drive customers away, 2) satisfy customers, 3) delight customers, and 4) customers are indifferent towards.  Few of the most common tools I use to conduct this type of assessments are surveys, focus groups, interviews, complaints tracked by the company, and industry data. 

Now, with a new data source called social media that contains enormous amount of information,  this blog examines how to use some Voice of the Customer tools to extract data from two of the many social networking sites, Twitter and LinkedIn.


Twitter is a platform that allows users to communicate using a string of short messages (i.e., tweets).  I see it very much like reading a conversation online that involves many people.  Since this information streams in real time, if you don’t read the tweet once it is published, you may never read it at all.  Of course, you can do a search for a particular tweet or use tools such as TweetBeep or Twendz to view conversations about your favorite topics, people, or companies.

When I want to assess Voice of the Customer using Twitter, I can utilize many tools. One that looks interesting is called Twendz.  According to the Twendz website, “The twendz Twitter-mining Web application uses the power of Twitter Search, highlighting conversation themes and sentiment of the tweets that talk about topics you are interested in. As the conversation changes, so does the twendz application by evaluating up to 70 tweets at a time. When new tweets are posted, they are dynamically updated, minute by minute.” 

I typed Microsoft in the search tool of Twendz, and I can see that in the last five minutes one complaint has been filed about Microsoft.  The complaint was about Microsoft never phoning customers, which I know is untrue. Nonetheless, that was the tweet.  The Twendz search result also told me that there were 1,491 tweets per hour about Microsoft, which means that there are many conversations taking place about this company.  Thus, this could be a very valuable tool for companies to use to monitor Voice of the Customer in cyberspace.  

There are other tools, such as Tweetdeck, that allow you to monitor your network’s conversations across Twitter and Facebook. 

Another Twitter-related tool is TweetBeep, which allows you to receive daily emails about tweets concerning your company or Twitter profile. 

Lastly, you can perform a search within Twitter. However, the result only includes the most recent Twitter conversations relevant to your search. 

Companies can use the Twitter tools mentioned throughout this section to understand Voice of the Customer.  The one limitation with all of these tools is that they don’t tell you how many people these tweets are potentially reaching and, thus, you don’t know the reach of a single complaint.



LinkedIn is a professional networking social media site that allows users to connect with other users all over the globe.  Members of LinkedIn can share questions, information, blogs, presentations, events, job postings, and more.  In addition, people can join various groups of interest to network and share information.

There are many tools available and many uses for LinkedIn.  Some of these can help in obtaining data related to Voice of the Customer.  One of the tools is called LinkedIn Signal.  LinkedIn Signal allows you to browse through status updates across LinkedIn and Twitter by using a key word search.  With LinkedIn Signal, you can filter data by connection type, location, company, etc. 

When I typed Microsoft into the LinkedIn Signal search field, I obtained over 265,000 results.  When I filtered the data by topic within the search for the term Microsoft, I obtained 573 results.  Overall, LinkedIn Signal is not a bad tool. However, there is a much better method to retrieve VOC data.

Rather than using LinkedIn Signal, you could simply do a search within the LinkedIn search field at the home page of your LinkedIn profile.  A great feature of the LinkedIn search is its ability to filter by search type (e.g., people, groups, answers). For example, if I search for Microsoft complaints within the search type of answers, I get an enormous amount of VOC data that Microsoft could really use.

Closing Thoughts

According to Business Insider, LinkedIn alone adds ten new users every five seconds and has about 135 million users.  Twitter has 140 million registered users.  Although the number of unique combined users of LinkedIn and Twitter is difficult to estimate, one fact remains: The number of users on these social media sites makes it important for companies to pay attention. 

With as many people as there are using these social media sites, if you are not using tools or methods to extract customer likes and dislikes from LinkedIn and Twitter, then you probably are not doing a good job of assessing your customers’ requirements. 

So the next time you conduct a Lean Six Sigma assessment and project, will your turn to social media sites as a source of data?


By Kyle Toppazzini


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Topics: VOC, Voice of The Customer, sigma, lean, Lean Six Sigma, Assessment, Social Media, process