Amanda says in a forum: “I have an iPhone 4. It sucks.” Her post, along with many others, is gathered by “online sentiment” robots trying to make sense out of them and give the clients (the companies and brands) some insights, like “30% of the online talk about the iPhone 4 is negative.” The question is, does this make sense? What real information can companies get out of statements such as Amanda’s? In our case, it is possible that Amanda is dissatisfied with Apple’s iPhone, but it is also possible that she is waiting to get an iPhone 5. Meantime, she has to “endure” her iPhone 4, and it sucks.

That companies want to understand what their customers think is old news. Companies have been conducting surveys among their customers and among the general public for years now, trying to get an insight into what people think about the company and its products. However, these surveys were not proven to be a real predictor of customers’ actions. For example, a customer may complain in the survey about an airline and say that the service is horrible and he’s never going to fly with it again, yet remain loyal since it’s the only company that has a direct flight on the routes he normally travels.

Extracting Brand Sentiments from Online Conversations – Challenge #1

So, the next step was to eavesdrop on consumers when they are having conversations online, and are not necessarily trying to communicate with the brand. The web is full of conversations, many of them about specific brand – on Facebook, Twitter, blogs, forums, reviews, and more. When a brand is very small, this “eavesdropping” can be done manually – a person or a team inside a company can try to constantly monitor what people are saying about the brand by simply searching for the brand name on search engines and on social websites. When the brand is larger, however, the online conversation can consist of hundreds or thousands of mentions daily – making it impossible to track them manually.

This quest to harvest the sentiment towards the brand brought on the emergence of services that claim to be able to collect large amounts of “online conversation” data about a brand and algorithmically analyze it. These services then output a number representing the brand sentiment – for example, by checking the percentages of negative and positive mentions out of all mentions in total – and giving the client reports such as: “30% positive, 20% negative and 50% neutral.”

This analysis is a challenge, since unlike human beings, who can pick up sentiment easily even from not-so-obvious sentences, algorithms have to follow a predetermined set of rules and look for specific sentiment-oriented words that are not always present in the conversation. Check, for example, this sentence: “why would Apple have this application in the iTunes Store anyway??” Put in the right context, many humans will accurately detect this to be a negative comment about a certain application. To an algorithm, however, this can appear to be a neutral statement, owing to its lack of overtly negative words.

Extracting Brand Sentiments from Online Conversations – Challenge #2

In an MSI insights article called “measuring online brand sentiment,” Julia Hannah writes about the current hot issue of trying to extract meaning out of large quantities of word-of-mouth. The paper describes a large-scale academic study by David Schweidel and Wendy Moe examining word-of-mouth sentiment across different online venues. Schweidel and Moe do not focus on the problem of an algorithm’s limited ability to understand context, they are interested instead in inherent differences in sentiment that exist across venues, products, and brand attributes.

For example, conversation venues that are more ‘one to many’ in their communication style (such as Twitter and blogs) tend to have more positive posts than venues that are more ‘many to many’ communication style (such as forums). They suggest that this “forum negativity” happens due to the fact that “when people interact with each other and respond to someone else’s opinion, there’s a tendency to differentiate and try to outdo one another by being more negative.”

Moreover, if we look at the different attributes of a product, it is very common for fans to criticize some aspects of the product while still maintaining an overall positive sentiment and positive purchase intent. One example could be “I don’t like the interface of the new speech recognition in the Galaxy 4,” or “I hate it that you can’t record your calls on the Galaxy.” The writer of this comment, even though expressing a negative opinion about the Samsung Galaxy 4, is actually only criticizing one attribute of it while still liking the overall product, and potentially being loyal to the brand.

Conclusion

Listening to online conversation is definitely necessary for successful brands these days. However, jumping to conclusions based on these conversations is dangerous. Both the theories and the software supporting extraction of online conversation sentiment are, at the moment, far from being predictive. Human involvement in these processes is still highly effective and required, and the overall best measurement of customer sentiments remains sales figures.

When people buy a coffee at Starbucks, they get more than just coffee. When people shop at Target, they get a different experience than when they shop at Wal-Mart. Individuals describe different experiences when using the Apple’s iPhone or Samsung’s Galaxy phone, even though the functionalities are almost the same.

Both of these examples can be attributed to customer experience, which has always been a core interest for corporations—a good customer experience leads to customer satisfaction, which then leads to customer loyalty and growth (Garg, Rahman, & Kumar, 2010).

Customer experience is a very broad and abstract concept, hence many of the definitions researchers construct lack specificity. When looking at packaged goods, for example, the customer experience often takes place out of the reach of the company, removed from the purchase location. Marketers of packaged goods create messages to convey a branded customer experience via advertising, packaging and other forms of marketing communication (Wyner, 2003).

The customer experience can be separated into a concrete component and a perceptual component; the concrete component is the physical experience created; for example, “Melanie received her package from Amazon after four days.” The perceptual components consist of elements that are harder to measure: the thoughts, feelings and attitudes created in the transaction – for example, “Melanie was pleased when the Amazon package arrived faster than she expected.” The conceptual part of the customer experience depends on countless abstract variables; among them are the customer’s expectations, the customer’s mood while engaging in the transaction, prior attitudes towards the brand, and in-store interaction. Even terms that we perceive to be quite concrete, such as “quality,” are in fact very subjective and ephemeral: “Quality” is often a measure by the extent to which the service or product delivered meets the customer’s expectations (Ghose, 2009), thus even the concept of quality is subjective and relative.

Humans are social creatures, and as such are influenced by others – be it friends or strangers. A few studies dating as back as 1943 suggest that communications among customers were more important than marketing communication issued by the company in influencing product adoption (Rogers, 1962; Ryan & Gross, 1943).

Marketers, aware of these findings, have since tried to harness this property to their service in their quest to create a positive attitude towards brands.

Such attempts can be seen in the use of various social endorsement avenues. Some marketers present expert testimonials to create trust and credibility; others opt for customer testimonials – either real or staged- that are said to positively enhance advertising effectiveness (Martin, Bhimy, & Agee, 2002).

In terms of positive word-of-mouth, Silverman (2001) notes, “getting people to talk often, favorably, to the right people in the right way about your product is far and away the most important thing that you can do as a marketer”. A BuzzMetrics report (Nielsen, 2012) suggests marketers to “Segment high-value customers and treat them as special ambassadors by offering them loyalty programs, member clubs, special offers and the like”, in the hope that they would spread positive word-of-mouth.

Once a customer has an experience with an organization and wants to spread word-of-mouth, the special characteristics of the Internet come to play a major role. As described above, the Internet facilitates a large reach communication channel, which allows for customers to post a message regarding their experience with the brand and instantly reach great audiences. Such large spread was hard to accomplish before the Internet era, when word-of-mouth used to spread much slower – by conversations with another individual or with a relatively small group. New social media referrals are between 20-30 times more effective at driving business than traditional marketing or media appearances (Trusov, Bucklin, & Pauwels, 2009).

This one-to-many communication by the customer becomes a many to many communication once a few individuals start speaking about the same brand – in an online forum, a talkback to an article, or a company’s Facebook page. From there, opinions and experiences can spread exponentially -social media word-of-mouth has up to 30 times larger effects than most paid marketing attempts, with a higher average carryover (Stacey, Pauwels, and Lackman, 2012).

One of the ways in which companies try to harness power of word-of-mouth over the Internet and promote users to spread it, is by placing social plug-ins. Social plug-ins are clickable elements embedded in a company’s websites that facilitate the sharing of content from the company’s websites (or word-of-mouth about it) with  the visitor’s network. While a few years ago individuals had to copy the page URL and paste it to an email message to spread it to their friends, today a simple click on an icon in the company’s websites will automatically send the message through the visitors’ favorite social network: Facebook, Twitter, Pinterest, and more.

Even though these plug-ins are efficient and ubiquitous, it is essential to remember that not all word-of-mouth is created equal. First of all, as suggested by prior studies (Godes et al., 2005; Trusov et al., 2009), a lot of the word-of-mouth communication is triggered by a specific marketing action, that should be credited – at least partially – for the surge in online conversations. Second, a study by Stacey et al. (2012) finds that Facebook likes and comments do not significantly affect purchase behavior, and increase website traffic less than topic-specific word-of-mouth conversations.

Asur and Huberman (2010) find in an extensive twitter-based research that message valence (=positive or negative emotion expressed), as opposing to quantity is highly predictive of new movie box office sales; and Chevalier and Mayzlin (2006) find that “more positive” book reviews increase sales, and not just any review. In another online word-of-mouth research the researchers focus on the inherent difference in sentiment that exist across venues, products, and brand attributes (Schweidel, Moe, & Boudreaux, 2012). For example, conversation venues that are one-to-many (such as Twitter and blogs) tend to have more positive posts than venues that are many-to-many (such as forums).

Overall, many studies  support the notion that it is not enough to look at quantity of online word-of-mouth mentions, it is crucial to analyze the content – focus on online conversation quality over quantity. Thus, evidence suggests that seeing that a page is “liked” is not enough to create actual liking and trust- visitors have to see specific content rather than something general like a social plug-in suggesting vague social endorsement.

• Trend of increasing information on company ‘about us’ sections; they increased in content for almost all companies studied, with a lot of emphasis on giving back to the community and environmental issues.
• Generally, an increase in text on all big retailer websites – could be for SEO reasons.
• Not all transitions to new sites are smooth, even for large, resourceful companies: some sites like Expeditors or Disney seem to “sit on remains” of older sites –pages have different footers, some pages are only accessible through the site map, etc.
• A decrease in the number of websites using “live chat” suggests the service is not cost-effective for many of them.
• An increase in social plug-ins and social sharing options as social media continues to take a larger part in interaction with consumers.
• Added mobile applications – almost all big retailers and service companies have signature mobile applications at this point

Many believe that the Internet levels the playing field and allows smaller retailers to setup online stores with substantially lower entrance barriers. “The virtual operation of the Internet allows merchants to enter and leave quickly and with lower investment” (Watson, Akselsen, & Pitt, 1998).

However, it appears that there is more to leveling the playing field than reducing the entrance barrier. Trust, for example, remains one of the most important components of the relationship between vendor and consumer, and gaining trust for one’s online store is not trivial. For trust to exist the retailer has to project that it has the motivation and reliability to deliver the goods as expected by the consumer (Jarvenpaa, Tractinsky, & Vitale, 2000). A large physical operation can serve as a trust cue for the consumers; it delivers the message that a large number of suppliers and clients trust the organization enough to engage in business activities (Doney & Cannon, 1997). This large physical operation is harder to project over the Internet.

In addition, the ease of entrance and exit can act as a two-way sword for online vendors. Customers are well aware of the fact that opening a store in the physical world requires large resources and investment, while an online store can be opened instantly and almost for free. According to Doney and Cannon (1997), a large retailer size assures the customer that this is not a “fly by operation” – a more challenging task in the online world where it is possible to open an online store and disappear shortly after.

In their study, Doney and Cannon (1997) find a significant positive relationship between retailer size and buying. In an Internet study by Jarvenpaa, Tractinsky, and Vitale (1999), the effects of store size on trust varies according to the product category – when buying books, store size mattered less than when buying a flight ticket. The authors attribute this difference mainly to the different cost of the products; however perceived need for future repairs, interaction and support may also be a factor.

Another issue to keep in mind is the complexity of isolating retailer size effect. By definition, bigger retailers have more visibility – either due to their physical presence or to advertising budgets, so it is very likely that they will generate more brand awareness, which can be easily confused with size. Thus, it is important to take into account that a small retailer who will be able to create larger awareness may reap similar rewards as the big retailer.