When was the last time your car made a funny noise or ran a little hotter than usual? Chances are, you quickly consulted a web browser for answers and found your way to a forum full of similar consumers and car enthusiasts, all eager to get answers to questions and argue over best solutions.
A few pages through the conversation and you probably end up with a fairly educated guess as to whether a trip to a mechanic, or a car parts dealer, is in order.
Forums like the one mentioned are an invaluable resource for segments and communities of people across nearly every industry or interest. These focused conversational microcosms can be created for virtually any purpose or topic, and most usually allow concentrated attention on specific questions.
Forums for common customer services questions and concerns tend to have highly recurring conversation patterns that monitoring workers don’t expect to find.
From businesses using forums internally, sharing common IT issues, to fans of a specific book or movie genre, these forums contain interesting conversational patterns that could enable their parent organizations to better understand customer and worker needs, motivations, and desires.
When you know how conversations are connected, the chance to better serve those involved in the forum emerges-as well as a unique opportunity to develop other forms of value from the conversations.
Using pattern-detection, Emcien analyzes all existing forum data, sifting through it to find patterns and topics most relevant to getting value. Emcien can also include social media data to provide more depth to patterns across many forms of conversation.
The ability to quickly and accurately understand what people are talking about across streaming forum data provides businesses and other organizations the chance to better serve audiences, quickly target emerging problems, and use these conversations to predict what conversations might continue to occur.
Here are some ways to develop value from patterns detected across conversations in forums:
Forums for common customer services questions and concerns tend to have highly recurring conversation patterns that monitoring workers don’t expect to find. Instead of relying on a team of customer care employees to manually monitor and analyze the forums, automatic pattern detection systems could highlight the most interesting, low occurring terms, illuminate how these conversations are connected, and point out other terms that the complaints have in common. Information from other sources such as social media and external conversations around the web about products can also be analyzed alongside internal forums to confirm or strengthen complaint patterns. This provides customer care representatives the chance to waste less time scouring forums and more time providing highly focused attention to customers’ needs and wants.
Have a forum for fans of your products? How about Facebook and Twitter accounts? Pattern-based analytics can sift through forum and social media conversations relating to a product, root out irrelevant chatter, and find patterns in conversations that denote the chance to create value. Consider the clothing example. After running automatic pattern detection software on your dense conversational data, a high rate of co-occurrence happens between mentions of a particular skirt and top that are posted on highly disparate parts of your website. This insight could inform better targeted suggestive sales based on word-of-mouth popularity-as found in your own data.
Resources for large hospital networks like web forums are being used to give patients a sense of community while recovering from an illness or entering a new stage in life. From forums for expecting parents to cancer treatment support communities, health care providers could learn a lot from what their patients are (or are not) discussing. Patients also frequently seek out opinions and support through other conversational mediums like blogs and social media. Pattern-based analytics could help nurses and public health workers assigned monitor forums and social media to better target conversations of distress or incorrect information, leading to better facts about health care options and less instances of ill-informed panic. More importantly, they allow healthcare workers to attach more closely to patient needs and questions, rather than making assumptions biased by surveys or policy-dictated procedural rules.