The notion that listening to your customer’s voice is important is well entrenched. Companies have long depended on data from customer surveys, call center transcripts and focus groups, captured in structured formats and handled through business intelligence applications, to help point the way to improved customer service, product enhancements and competitor vulnerabilities.
But the sheer volume of the customer choir in the Web 2.0 age often leaves companies scrambling to keep up. Publishing is now in the hands of the public, who have a vexing tendency to post with blunt honesty in unstructured formats via blogs, tweets, e-mails and forums about products and services that delight or disappoint them. And those opinions hold weight. A 2007 study by Jupiter Research (since acquired by Forrester), called “Social Networking Sites: Defining Advertising Opportunities in a Competitive Landscape,” found that 30 percent of frequent social networkers trust their peers’ opinions when making a major purchase decision, compared to the 10 percent who trust advertisements.
As Andreas Wiegend, former chief scientist of Amazon.com, predicted in a blog post for the Monitor Talent Group, “In 2009, more data will be generated by individuals than in the entire history of mankind through 2008.” Companies face a very real need not just to acknowledge the impact of unstructured social media on brand and product perception, but to understand and filter it sensibly, and to integrate it with structured customer data and get it into the hands of the right people to make it actionable.
For many companies, the burgeoning text analytics approach of sentiment analysis is becoming a critical component of their overall strategy, giving them a much-needed assist to stay responsive to customers, market opportunities and trends.
What is it?
In his white paper “Text Analytics 2009,” Seth Grimes, analytics strategist at Alta Plana, describes text analytics as “the software and the transformational steps that discover business value in ‘unstructured’ text.”
There’s special business value in discerning opinion, sentiment and subjectivity—the “voice of the customer”—in text as varied as blogs, forum postings, articles, e-mail and survey responses. That field of “customer experience analysis” applies sentiment analysis and other techniques to understand and help predict consumer behavior via text analysis coupled with analysis of customer transactions, profiles and demographics.
Vendors generally use a combination of statistical analysis of wordfrequency and co-occurrences with linguistics (involving lexicons, dictionaries and language rules) in an algorithmic approach to understanding exactly what the consumer is saying. Grimes, who will be presenting a talk in April on “Search for Sentiment” at the Search Engine Meeting 2010, which is co-sponsored by KMWorld, says, “The narrower you can frame the problem and the data you collect, the better, because you can then adjust your approach to match specific business requirements and information sources.”
The technological challenges are not for the faint-hearted or the linguistically timid. Suresh Vittal, analyst at Forrester, says, “For a long time, text analytics was a technology in search of a business need. Now, thanks to social media, the need is there; the question is whether the technology can ramp up fast enough to be commercial.” Early adoption by government agencies, which sought to apply text mining to mountains of classified documents, is giving way to more mainstream commercial demand from industries for whom customer perception is critical: hospitality, consumer brands and high-tech, among them.
Classifying the messy middle
Ours is a world in which online consumer reviews of hotels that might include the phrase “the lobby is baaaaad!” meant in a positive way, or a review of a holiday toy saying, “I would give this to all the children in my life, if I were Scrooge,” meant to disparage. Throw in slang, language evolution and socio-cultural gradations in word use, and you have a mammoth challenge for accurate computational treatment of opinion.
Larry Levy, co-founder and chief opinion gatherer at Jodange, an opinion utility that filters and aggregates thoughts, feelings and statements from traditional and social media, says accuracy remains a challenge in the industry. “The sentiment side is good at the two poles, positive and negative,” he says, estimating that Jodange’s Opinion Lens gets those sentiments right around 80 percent of the time. “But the neutrals are difficult. If you give four people in a room 100 neutral opinions and ask them to classify, even they will only agree 55 to 60 percent of the time.”
The level of granularity can also be important. If sentiment is assigned at a document level—that is, each tweet or blog post is assigned a positive, neutral or negative sentiment—how does the hypothetical tweet “I love Marriott’s bathrooms but the beds are lumpy” get classified? Marcel LeBrun, chief executive officer of Radian6, which offers clients a platform to listen, measure and engage with customers across the social web, cautions, “Ratings need to be assigned on a subject level at a minimum; a solution that assigns them at a document level is going to miss something.”
Whose opinion is it?
Even if a sentiment analysis tool were always accurate, the opinions don’t necessarily carry equal weight. LeBrun estimates that Radian6 customer Dell has 8,000 to 10,000 online conversations about its brand each day, which span the spectrum of positive to negative; the company needs to understand whose opinion actually has the power to move brand perception, and keep close tabs on those. “Sentiment analysis needs to be connected to social metrics and influence analysis to make sense,” says LeBrun.
Levy agrees, saying Jodange customers understand that listening to social media is important but now need help in filtering. “There is no longer the notion that trusted information only comes from The New York Times,” he says. “Once you get a handle on who is influencing your brand, that becomes actionable.” Influence analysis, analyzing digital breadcrumbs to see which individuals have the highest credibility and widest reach, should be a part of the overall text analytics strategy. By knowing in advance who the influencers are for your brand, you’ll be better prepared to manage crisis and opportunity effectively, reaching out to 20 key contacts instead of 10,000 questionable ones.
Taking sentiment out of the silo
There’s widespread agreement among vendors and analysts that text analysis is only as valuable as the actions it prompts. In a Forrester report from February 2009, called “Obstacles To Customer Experience Success,” a survey of 90 customer experience decision-makers from large North American firms found that 89 percent said that customer experience would be either very important or critical to their 2009 efforts, but a lack of cooperation across organizations remains a major obstacle.
When it comes to sentiment analysis, different functions are listening for different answers. A customer service manager needs insight into customer experience, a product manager wants to hear complaints or praise for features as well as product design ideas, and brand managers may be looking for competitive intelligence.
“It’s easy to turn on an application and get a feed of data,” says Sid Banerjee, CEO of Clarabridge, a provider of text mining software. “For our customers, acting on it is the hardest part of the equation. You must have an environment where people are culturally attuned to action.” Banerjee points out that where business intelligence solutions have traditionally been sold to the IT function, Clarabridge has had success selling its customer experience management solutions directly to the primary consumers of the data.
The challenge to the enterprise is to combine analysis of what is being said, by whom, with more structured customer intelligence data in order to develop a robust customer engagement strategy. Forrester’s Vittal says that to break sentiment analysis out of the silo, “The platforms must be open and integratable. Customer intelligence data is still siloed, and there is a complexity gap that must be overcome.”
Clarabridge’s December 2009 announcement of a partnership with pollster Harris Interactive is an illustration of how it can be done. The companies plan to combine unstructured data from opinions about President Obama’s healthcare reform initiative, posted on social media and other online sites, with public opinion as measured through structured survey research, to paint a truer picture of citizens’ emotions and decision factors on that volatile issue.
For companies that are just getting started with sentiment analysis, Radian6’s LeBrun suggests what he terms “the Yellow Brick Road” approach. “You have to pick up the social phone and listen to what’s being said, analyze and identify who the influencers are,” he says. “Step two is to start responding, not just by data mining but by building community.”
The third step is participation. By moving from metrics to diagnostics—understanding not just what is being said, but the root causes behind it—a company can truly begin to capitalize on the promise of text analytics. It’s another arrow in the quiver that can help companies understand and respond to the messages their customers are sending, loud though not always clear.
Partial list of text analytics/sentiment analytics vendors
Teragram (a division of SAS)
To download the Alta Plana Text Analytics 2009 chart which shows the breadth of usage for text analytics tools go here: http://www.kmworld.com/downloads/60764/2085_chart-2.pdf