Podcast Insight In Person
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Talk may be cheap, but listening to what people are saying about your product can be a valuable method of improving corporate performance. According to recent research, there is a measurable connection between what is being said about a product in online posts and real-time customer behavior. Becoming aware of online comments and learning how to use the information can avert potential downturns in sales and can help companies fine-tune their marketing.
Potential buyers of a product are influenced by existing users of that product, the research shows. According to Lakshman Krishnamurthi (A. Montgomery Ward Professor of Marketing at the Kellogg School), “If you ask people what’s most important to them in evaluating a product, they say, ‘What other people like me say about it.’” Krishnamurthi and his colleagues, Shyam Gopinath (a doctoral student in marketing at the Kellogg School) and Jacquelyn Thomas (Associate Professor of Marketing at Southern Methodist University), examined online word-of-mouth for the cellular phone industry. They focused on five specific brand models from five leading cell phone companies in the United States. Using data from an online forum with more than eight million posts, they explored the conversations of individual posters over time and analyzed how the nature of these posts related to individual customer behavior. From there they examined how the nature of online conversations relates to corporate performance.
In a working paper based on their research, Krishnamurthi and his colleagues report that they developed the data set by identifying keywords in the posts that expressed an attitude toward a cell phone and usage experience. As Krishnamurthi explains, “We classified people’s comments in these posts in three ways. One is an action-type statement, such as ‘I’m going to buy it.’ Another type expresses emotion, such as ‘I hate it.’ The third category is made up of attribute-type statements that have to do with quality, things that relate to the functionality of the product, such as ‘It has great reception.’” Each type of rating can be positive or negative.
Using specially designed software, the researchers rated the action, emotion, and attribute statements on a scale. According to Krishnamurthi, “It’s a little bit like artificial intelligence. You take a large number of posters and look at all the words they use, and create a classification of these words as highly negative through highly positive. The software has a dictionary, and when these posts are made the software automatically classifies them on this continuum.”
Buzz Action Score
With their data the researchers developed a customer-level metric based on active and passive customer behaviors, which they dubbed the “buzz action score.” Using the buzz action score from individual posters, they derived two aggregate-level metrics: the “buzz index” and the “buzz share.” Evaluating the buzz index and the buzz share allowed them to demonstrate how online word-of-mouth relates to actual market performance. Figure 1 illustrates the conceptual framework for the process. First, an existing user posts an evaluation. Next, a potential purchaser encounters the evaluation. As a result of the online “conversation,” the potential purchaser either does or does not buy the product. The results of the “buy” or “don’t buy” decisions resulting from the conversations ultimately influence the company’s performance by showing up in sales data.
Figure 1: Online word of mouth - conceptual framework
Previous research has shown that a relatively small group of people can have a substantial influence on the majority. The researchers found that the same is true in online communities. The highly influential posters, whom Krishnamurthi and his colleagues call “mavens,” tend to have an especially intense interest in the brand, either positive or negative. Because of this intense interest, they are more likely to advocate a strong opinion, which in turn has a much bigger influence on potential purchasers than does the opinion of the average user.
So how does that influence relate to measuring brand performance? According to Krisnamurthi, “The traditional way is to look at sales and look at marketing and try to see if marketing has been effective in moving the needle.” However, a problem with actual sales measures is that they are delayed. For example, phone manufacturers only know what they are shipping out to vendors. They will not know actual sales figures until some later point in time.
In contrast, by monitoring online postings, a firm can find out much sooner if there is negative sentiment among customers about the brand. The researchers found that the buzz index, whether positive or negative, is a leading indicator of sales. Krishnamurthi explains, “There is very practical value to tracking these online posts. You don’t have sales data for three months from now, but you have the online postings right now. This makes it possible for companies to fine-tune their marketing and not wait for the sales to go down.”
The researchers also found that the importance of the attribute and emotion scores varies among brands. “Let’s say you’re a communications company, and your marketing has been pushing the coolness factor,” Krishnamurthi says. “Then this is a gut check to see if your marketing is consistent with how your customers are reacting to it.” He added that the finding about variance of attribute and emotion scores among brands has far-reaching implications. For example, “Say I am brand A, and you come to my Web site. One of the questions I’m going to ask you is, ‘What brand do you own now: A, B, or C?’ If you say ‘B,’ I know what I’ve found in terms of whether attributes or emotions are important to brand B customers, and I’m going to lead you to a portion of my Web site that talks to you about my product in terms you might like.”
The authors suggest that firms should monitor online word of mouth in order to “stay ahead of the race.” They add, “An Internet-armed consumer can become your greatest asset or your worst nightmare.”