Large enterprises to small businesses can now harvest actionable intelligence from social media and Big Data in 15 native languages. This breakthrough, called Sentiment@Work™, is the newest human language technology from LinguaSys, a leader in natural language processing solutions.
Other solutions usually require data to be translated into English before being analyzed, destroying much of the content’s information value. The few companies that offer native language analysis can only work in a handful of tongues. No other solution delivers the linguistic breadth and data granularity of Sentiment@Work™.
LinguaSys CEO Brian Garr participated in a Q&A with Loyalty 360 to discuss this new solution and his observations on customer loyalty, customer engagement, and customer experience.
Q: Engaging experiences are the baseline for creating loyal customers. How should brands create the culture by which engaging experiences (even though they may be more expensive to put in place and more difficult to measure) are table stakes for brands?
The first, most critical step is being a “good listener” and really understanding the basis of the conversation: What do your customers and prospects think about your brand today? This provides inspiration for communication themes and a baseline for measuring progress. There are several ways to capture this information, from surveys to social media. Unique value can be mined from social media because it’s candid, real-time feedback to a customer’s network. It’s now possible to measure perhaps the most important but previously elusive factor – the sentiment customers really have for your brand and its products.
Q: What will be the biggest opportunity for marketers in the next 3-5 years?
Harnessing technology to have “one-on-one” conversations with large numbers of customers and prospects at the same time. This is now possible – in both directions. You cannot only understand what consumers are saying, but also have a powerful, customized dialogue with them. Natural language interfaces take interactive Q&A to levels never possible before for everything from surveys to order taking.
All of this includes having these conversations in the language in which your customer feels most comfortable, which increasingly will be a language other than English. In global markets, some 80% of your available global prospects prefer to converse with you in a non-English language. At LinguaSys, we can analyze content and sentiment in 15 native languages (unlike other solutions, we don’t have to translate Tweets or other data before analyzing them, which gives our clients much more valuable insights.) Of course, we can also allow your brand to respond in these languages as well through our translation capabilities.
Q: In traditional push marketing we measured effectiveness by response rates and CPMs and the ability to hit as many eyeballs as possible to increase trial and conversion rates? How is today different? How do you effect change with a CMO who still may be “push” focused?
Today’s technology, from text analytics to Big Data analysis, allows CMOs to go beyond traditional “push techniques” for everything from gathering data to converting prospects. On the data side, these technologies enable CMOs to move beyond “hypothesis-driven” data collection. In other words, analysis of the data itself may highlight critical issues you never thought to ask about, as well as providing early warning of critical issues.
Another critical technology advance is that semantic analysis now allows you to understand the “why” and “how,” in addition to the “what” and “when.” This allows you to gather “qualitative” insights far more quickly and effectively than before.
Q: In the move toward customer-centricity, if you could give one piece of advice to a brand to help them increase loyalty and engagement with their customers; what would it be? What would it have been two years ago and what might it be in two years?
Listen to your customers. We gave the same advice two years ago and will give the same two years from now. The change involves harnessing the most effective methods and technologies to listen, analyze and respond.
One basic example: When a loyal customer gives you his or her name, you should be able to use it correctly in spoken and written communication with the customer. This can be tricky, especially for foreign names in languages like Chinese, when employees and software systems may not even be sure which is the right first and last name, let alone how to say them. This is the type of challenge LinguaSys helps solve for our customers.
Q: The plethora of data (increasingly so) we have in the market makes it more difficult to model the current and optimal behavior. Advice for brands?
Embrace the onslaught of data! First, realize that not all data are created equal. Focus on the most “actionable” and integrate less valuable but still important data later as your system scales. This will become easier, not harder, with the growth of cloud analytics and software-as-a-service.
Q: What emerging technology do you believe will have the greatest impact in helping drive more effective engagement and therefore better marketing outcomes?
If great results are all about having the best “conversation” with consumers and customers, which we believe, then the most important technology is about enabling and scaling that conversation. If you could, you’d want your most persuasive brand champion to chat with each and every customer and prospect, but that’s not scalable. However, the next best thing is using human language technology to facilitate those conversations on a wide basis, from understanding the consumer via text analytics to responding to questions and orders via natural language interfaces in whatever language the consumer feels most comfortable.
Q: How should brands attempt to keep up with the rapid proliferation of technologies, especially with some of the newer technologies harder to measure versus more traditional technologies that brands are more comfortable with?
In most cases, an evolutionary rather than revolutionary process proves most effective. This is also consistent with the growth of cloud software services. There’s no reason to spend huge amounts of time and money ripping out old software systems. Instead, you can experiment with new capabilities that extend your current level of analysis and understanding. In our own world, that includes using LinguaSys technologies to increase the value of text analytics and/or machine translation results from older existing systems. Over time, solutions than work better can be used more widely while those with diminishing results can be phased out.