Beyond Automation: The Importance of Human Analytics in Social Media Monitoring | Article

Man analyzing social mediaMobile networking. Online games. Social computing.

As personal and professional worlds collide, social media is generating huge amounts of unstructured data that’s brimming with business insights — from consumer preferences to competitor activities. That’s why many companies are using social media monitoring tools to “listen” to these conversations.

These automated tools — from firms such as Radian6, Visible Technologies and Lithium Technologies — crawl thousands of tweets, posts, blogs and forums to collect data based on key words. But data collection is not the endgame. Indeed, a report by Forrester Research suggests that the most common pitfall in social intelligence initiatives is relying on listening platforms alone.

The real opportunity is what happens after collection. How can you translate your data into actionable insights on factors like customer retention, product development or competitive advantage? How do you use your data to make strategic decisions?

The answer is human analysis, which adds meaningful insight to automated collection.

From data to informed business decisions

In truth, automation only takes you so far. You can use automated tools to measure the volume, frequency and geography of key words on Facebook, Twitter, blogs and other forums. But the next step is to thoroughly analyze the data and act on it. Consider some examples:

  • Brand sentiment and its reach. Some automated tools analyze the sentiment of social media comments, but the data often comes in neutral when, upon human analysis, it is actually positive or negative. For example, when an analyst reviews Twitter posts that are earmarked by a monitoring tool, he or she can pinpoint individual tweeters and determine why they’re tweeting so much about your brand, whether their comments are positive or positive, and the extent of their following. Both positive and negative comments can have a significant impact on your brand, so it’s important to measure the sentiment accurately and respond appropriately.
  • Product feedback. If you launch a new product, an automated tool can track comments about it based on key words, but the tool can’t intuit the full story or categorize comments based on the feedback. For example, if your company releases a new printer, some of your customers may talk about paper jams, ink cartridges or their experience on the website, while others may praise the product. This is important context that is best analyzed by a person, who can categorize comments while helping you identify opportunities for product improvement, respond to concerns and celebrate positive comments. In addition, keep in mind that automated tools cannot discern sarcasm: A comment like “great job!” can have different meanings based on the context.
  • Segregating feedback and resolving concerns. In a similar Wipro example, we used an automated tool to collect more than 3,500 customer comments in a month about one telecom client. Our analysis team then segregated the comments into four categories: billing, technician visits, experience with the phone agent, and overall customer service. We discovered that one of the most frequent concerns was related to technicians: Customers complained online that technicians often showed up without notice or failed to correct the problem. In addition to illuminating an opportunity for improvement, this analysis enabled the company to reach back to individual customers to address their concerns — and let them know that the company is listening. This kind of customer research and reputation management doesn’t come automatically from a monitoring tool.
  • Improving the customer experience. An analytical team can also help leverage social media as an additional customer service channel. Analysis of comments about an airline, for example, could identify customer queries about topics such as flight status, schedule changes, onboard entertainment, promotions and food. In response, the airline could answer these questions through social media channels, while reducing average handle time in the call center. In a real marketplace example, Lenovo learned through an analysis of comments in third-party forums that customers were talking about its products. So the company launched its own online support community to respond to the concerns, which resulted in a 20 percent reduction in calls about laptop support. Intuit, likewise, has used social media to listen to customer feedback about its TurboTax software and incorporate customer-driven features. As a result, the company has expedited product development and improved the customer experience.

Final thoughts

Ultimately, the automated collection of data through multiple social channels, though very important, is but the first step in a strategy for social media monitoring. The next steps are to extract and organize the data, identify issues and make informed business decisions — all of which involves the work of a skilled analytical team.

What are your competitive weaknesses from customers’ perspective? How can you leverage social media to satisfy customers’ needs? How can you prepare your contact center for customer concerns? How can customer comments inform product development? With the right analysis, you can turn social media data into actionable insights.

Anand Chopra, Ph.D., is a senior manager in the Research & Analytics business unit of Wipro BPO. Dr. Chopra also heads the company’s Social Media Center of Excellence, which provides thought leadership and innovative social media solutions in close collaboration with niche technology partners.


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