WHITE PAPER

You Are Only as Good as Your Customer Thinks You AreThe Perceptual Power of Social Media: Gaining Insights from Social Media on Product Quality

SAS White Paper

Table of Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

What Is Quality? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Why Use Social Media? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Types of Social Media Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Looking Beyond Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Best Practices in Monitoring and Improving

Perceptual Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Integrate Social Media into Your Current Quality Strategy . . . . . . . . . . 5

Implement a Repeatable Analytic Framework . . . . . . . . . . . . . . . . . . . . 5

Analyze Competitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Context Is Important . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Identify Your Most Powerful Influencers . . . . . . . . . . . . . . . . . . . . . . . . 6

Continuously Improve the Accuracy of Text Analysis . . . . . . . . . . . . . . 6

Invest in Social Media Analytics that Can Grow with You . . . . . . . . . . . 6

Closing Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

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You Are Only as Good as Your Customer Thinks You Are

IntroductionShortly after Microsoft launched the Xbox 360, customers started complaining about widespread product failures . Although Microsoft stated that failure rates were within normal ranges, blogs, chat rooms and social media sites were bombarded with comments about failures . The Internet quickly dubbed the onboard diagnostic code of three red lights the “Red Ring of Death .” Commenters were quick to propose potentially dangerous fix-it-yourself remedies and extol the virtues of competitive products . The Red Ring of Death quickly grew into a public relations nightmare and eventually caused Microsoft to extend customer warranties at a cost of more than $1 billion . But Microsoft wasn’t alone . Apple’s issue with the iPhone 4 antenna and Toyota’s sudden-acceleration complaints are two more examples of how social media can shape perceptions of product quality . Understanding those perceptions are essential to identifying real or imagined quality issues and reacting effectively .

If you’re not convinced that social media warrants serious consideration, consider some basic facts: There are more than 800 million active users on Facebook who “like” or comment on more than 2 billion posts and share more than 30 billion pieces of content a month . Twitter has more than 100 million active users generating 230 million tweets per day . In the blogosphere, WordPress alone powers more than 50 million websites . In addition to social networking sites and blogs, there are comments on traditional media and e-commerce websites, review sites such as Epinions and Edmunds, content-sharing sites such as YouTube and Flickr, collaborative projects such as Wikipedia, and even government-sponsored reporting sites such as safercar .gov and saferproduct .gov .

In the past, a dissatisfied customer told 10 friends; now they tell 10,000 . People are talking about their experiences with your products – and your competitors’ products . Some may be ambassadors and advocates, others are detractors and malcontents – but all of their voices are in the mix, shaping customers’ buying decisions . At a time when product recalls, govern¬ment intervention and lawsuits are front-page news, customers are questioning brands that seemed to have been above reproach . The ability to monitor, measure and manage those perceptions has a significant impact on your ability to detect field issues, differentiate your products and increase profitability .

Descriptive statistics, text analytics and social network analysis provide a way for you to determine how customers purchasing decisions are affected by what they see on the Internet . This paper describes how analytics can be applied to social media and provides some best practices for monitoring and addressing perceptual quality .

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What Is Quality?Quality has many definitions . While some focus on adherence to design specifications, some focus on the nature of the ingredients or features, and others focus on the extent to which the product or service delivers superior service . However, when it comes to a purchasing decision, your potential customer’s perception of your quality trumps all of the other definitions . As W . Edwards Deming famously said, “The customer is the most important part of the production line . Without someone to purchase our product, we might as well shut down the whole plant .”1

For that reason, understanding your customers’ perceptions of your products, and what they are saying to other prospects, is critical . Understanding what is important to your customers can help with issue detection and prioritization and can start you down the road to resolution – whether that means a product or process improvement, user training or a new marketing campaign .

Why Use Social Media?Although customer perception may seem less controllable than traditional quality metrics, the good news is that your customers’ perceptions are more accessible than ever . The proliferation of Internet access and “smart” devices has brought social media to the forefront . Not only are your customers eager to tell the world about their experiences, prospective customers are searching for those insights . Organizations find this new data source valuable because it is fast and freely available (to you and your competitors) .

The moment a design change goes into effect or a new product is launched, customers eagerly begin sharing their experiences . New data is constantly being generated and is available much more quickly than traditional data sources, such as warranty claims and call centers . By harnessing social media data and integrating it into your issue detection process, it is possible to achieve an even earlier warning of product issues .

Competitive data has always been a challenge to acquire . Surveys, focus groups and product tear-downs are expensive and time-consuming . Although each has its place in the process of understanding customer perceptions of quality, social media has emerged as a new, inexpensive way to gather competitive intelligence . Through monitoring, comparisons can be made quickly and insights can be uncovered . Your competitors are probably monitoring your performance – shouldn’t you be monitoring theirs?

Types of Social Media DataFacebook and Twitter get most of the attention, but there is a much larger social media universe . Useful information can be found in many places . There are several categories of information that should be gathered, analyzed and exploited .

1 Deming, W. Edwards. 1982. Out of the Crisis: Massachusetts Institute of Technology.

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You Are Only as Good as Your Customer Thinks You Are

The first and most obvious data source is unstructured text data, such as blogs, microblogs, tweets, Facebook comments and reviews on sites such as Epinions and Edmunds . People are also posting pictures, audio and video to sites such as Flickr, YouTube and others, all of which can give us insight about what’s important to consumers and what kind of issues they are experiencing .

Some quantitative data is available through sites that collect and aggregate ratings . A consumer might rate a product using a five-star system, and highlight their complaints about specific components of the product .

Some sites ask visitors to share demographic information that can be useful in segmentation strategies .

Then there is the issue of connections . By analyzing the connections among fans, friends, followers, etc ., you can identify hubs in networks and interest-based groups that are connected – a potentially powerful source of information about who are the most influential voices .

Looking Beyond Social MediaAlthough social media is an effective channel for understanding and interpreting customer perceptions, it isn’t the only source of conversations . If you really want to evaluate sentiment, conversations, topics and what people care about or don’t care about, it doesn’t make sense to isolate social conversations in one silo and other customer communications in a different silo . It gets really interesting when you start to blend social media data with government databases and internal data sources .

In 2008, the United States’ National Highway Traffic Safety Administration launched the safercar .gov website . This site allows consumers to post complaints about motor vehicles and related products . In 2011, the Consumer Product Safety Commission followed suit, launching saferproducts .gov . This broadened consumers’ options to post complaints on 15,000 types of products . These databases are filled with publicly available customer complaint information that did not flow through your company’s call centers and surveys . This information is available to prospective customers, competitors and governmental organizations .

These new sources of information complement traditional customer touch points that most organizations already have in place . Call centers, surveys, customer service records, online chat and customer emails generate volumes of comments that can be integrated with social media data . Each of these data sources brings its own pros and cons . Some are faster . Some are more detailed . Some are more reliable . Bringing all of this information together gives a common viewpoint to understand customer conversations and sentiment – and a much better handle on leading indicators . If an uptick happens across channels, that is a more reliable insight to use as the basis for business decisions .

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AnalyticsCan you make fact-based decisions based on opinion and chatter? Yes, if you apply the right statistical rigor . Social media is a relatively new data source with many imperfections and idiosyncrasies, but other data sources experience many of these same problems . By using analytic techniques to filter out noise and isolate the signals, you can make more accurate decisions . These tools and techniques fall into three broad categories: descriptive statistics, text analytics and social network analysis .

Descriptive statistics clarify activity and trends . How many people are talking about my products? What components are they mentioning? Is this changing month to month? Are we seeing a positive trend? Understanding and monitoring these basic questions is the first step in understanding social media . However, the answers often generate more questions than they answer .

Taking the next step requires more powerful analytics . Text analytics dig deeper to examine the content in online conversations to identify themes, sentiments and connections that would not be revealed by casual review . There are two approaches to text analysis: discovery-driven and domain-driven . Discovery-driven methods take a bottom-up approach . They mine the data for patterns and are useful when you’re not sure where to start . Domain-driven methods take a top-down approach, using predefined terms and hierarchies . Each of these approaches is valid and, more importantly, they complement each other . For this reason, SAS recommends a hybrid approach incorporating the following elements:

• Content categorization . The system scans a piece of information and categorizes it based on content . This technology can be very helpful for scanning forum comments and reviews and organizing them by components of the product . For instance, consider the thousands of comments coming in from an automotive review site . A content categorization program can quickly scan and classify them to identify a braking issue or a stalling issue . You can then direct those comments to the right party without someone having to read through them .

• Text mining . In the same way that you can use data mining to explore data in your databases and establish relationships, you can now do the same thing to all those unstructured text documents . You can dig around in volumes of unstructured data and look for themes and connected concepts . Text mining is often useful for discovering new categories of comments that you hadn’t thought to look for in the past . You start to uncover relationships among conversations and among linked concepts and see whether they are significant .

• Sentiment analysis . This form of natural language processing looks at how people use words and phrases in context, and then assigns a sentiment – positive, negative or neutral . It is incredibly useful to apply this technique to online reviews and comments . Knowing that people are commenting on your brakes is valuable, but knowing whether those comments are positive or negative is critical . You can classify and categorize sentiments, look at trends and see significant differences in the way people speak either positively or negatively about you and your competition .

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You Are Only as Good as Your Customer Thinks You Are

Finally, social network analysis follows the links between friends, fans and followers to identify connections of influence as well as the biggest sources of influence . Who are the most connected people within social networks, and who has the most influence on perspective customers? Understanding the answers can help you target individuals who could become your advocates and generate positive word-of-mouth within their networks . This approach can significantly reduce the impact of perceived quality issues .

Best Practices in Monitoring and Improving Perceptual Quality

Integrate Social Media into Your Current Quality Strategy

Although social media may be a new data source for many organizations, the underlying questions it answers are consistent with traditional field quality data sources such as surveys, call centers and warranty claims . Social media can provide an early warning to issues that may not have surfaced in these other data sources but are on their way . Social media can also reveal customer irritators that may not be covered under product warranty but, nonetheless, may drive away prospective customers . By integrating this data source into your quality strategy, you can build a more comprehensive view of customers’ experiences .

Implement a Repeatable Analytic Framework

Although social media is similar to other field data sources in many ways, it has its own challenges . The sheer volume of data, its lack of structure and its changing nature require a consistent analytical framework for effective use . Implementing social media scanning tools to find out what people are saying, filtering out the irrelevant content, categorizing which products and components are being discussed, and tracking the sentiments in those conversations are each important steps in the process .

Analyze Competitors

Competitive pressures are high, and competitive data is often difficult to acquire . The public nature of social media makes it ideal for competitive analysis . Competitive data can be analyzed using the same framework as your own data, allowing for direct comparisons . Understanding your position on specific issues relative to your competitors can give you critical insight for issue detection and prioritization .

Context Is Important

It’s not enough to listen to just the last seven or 30 days of commentary . Your listening and measurement efforts should accumulate a year or two of history . This enables you to see trends and understand whether a shift in volume or sentiment is a significant change, part of a seasonal trend or just noise .

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Identify Your Most Powerful Influencers

Organizations struggle to identify who is the most effective in shaping public opinion . There are hundreds of people talking about your company, which ones you should be engaging? Analytic techniques can identify who those influencers are – both consumer-generated influencers and professionals . These are the people you’d want to target with special campaigns/offers and proactive communications .

Continuously Improve the Accuracy of Text Analysis

An industry-specific text analytics package will contain the vocabulary of your business . The system will have built-in linguistics, but it learns and improvesover time . Much as you would fine-tune a statistical model as you get more data (better parameters or new techniques to deliver better results), you would do the same with the natural language processing that goes into sentiment analysis . You set up rules, taxonomies, categorization and meanings of words; monitor the results; and then tune it again .

Invest in Social Media Analytics that Can Grow with You

Because social media is one component in the bigger quality picture, it is critical to invest in an analytics platform that can grow with your needs . If you are thinking about approaching a vendor and making a technology investment, make sure you’re starting out with a platform robust enough to grow with you, something that’s flexible enough to be used across environments and can incorporate a broad range of quality data sources such as warranty claims, surveys, call center records and shop-floor quality measurements .

Closing ThoughtsQuality is in the eye of the beholder, and your customers’ perceptions of your product quality are more public than ever . Understanding those perceptions is essential to identifying real or imagined quality issues and reacting to them effectively . Those perceptions should be directing your issue identification, prioritization and resolution processes .

Analytics technology has taken much of the work out of making sense of these large volumes of unstructured data . Descriptive statistics, text analytics and social network analytics can guide your analysts and engineers to focus on the critical issues and resolve them before they grow out of control . Taking advantage of this information and integrating it into your quality process is an important step in avoiding costly quality issues and public-relations nightmares .

Social media data is out there . It is available to you, your customers, your competitors and regulatory agencies . Everyone else is looking . Shouldn’t you be?

About SASSAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market . Through innovative solutions, SAS helps customers at more than 55,000 sites improve performance and deliver value by making better decisions faster . Since 1976 SAS has been giving customers around the world THE POWER TO KNOW® . For more information on SAS® Business Analytics software and services, visit sas.com .

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