Editor’s note: Terry Lawlor is EVP product manager at market research software firm Confirmit, London.
The Internet of Things (IoT) – the network of physical objects that contain embedded technology to communicate, sense or interact with their internal states or the external environment – has quickly become a hot topic for 21st century business.
Gartner recently estimated that 4.9 billion connected “things” would be in use by the end of 2015, expanding to 25 billion in 2020, with each device capable of providing potentially invaluable customer insight that could be vital to commercial survival.
But how do you know if your business is prepared for the booming IoT revolution?
Businesses today must prepare themselves for the barrage of data coming from the IoT by embracing next-generation customer experience and market research best practices. This means taking a more sophisticated and automated approach to listening to these “things” to better understand your customers, using a smart hub that intelligently brings the data together. As the contextual data that Internet-connected devices can capture increases, it also becomes easier for the new generation of digitally-savvy consumers to openly share feedback and opinions. Best practices need to incorporate social and text analytics – alongside traditional methods – in order to simplify and streamline the entire customer experience management process.
Take Apple’s entry into the wearable tech market with the Apple Watch. Consumers will have a device that automatically captures context such as location and health data, while enabling instant payments and allowing for easy social interactions.
As a result, companies need to analyze all kinds of data: unstructured and structured, solicited and unsolicited. And they need to do this across multiple platforms, channels, countries and languages to help them identify and track issues that could make or break them. Sifting through this massive volume of diverse data has become a hugely complex and labor-intensive task that is virtually impossible to achieve manually. By the time any insights are uncovered it is often too late to make any impact on the business.
As Sony Mobile has found, the automation of the process for listening to its customers across thousands of social media channels has transformed its ability to identify, analyze and act upon feedback and trends in hours, instead of days.
Text analytics + IoT
A February 2014 report, How To Use Text Analytics In Your VOC Program by Jonathan Browne, senior consultant at Forrester Research, Inc., found that text analytics help organizations provide more intelligent service recovery, drives continuous improvements in operations and supercharges executive decision-making.
This indication of the need to help companies mine the large volumes of data resulting from the IoT has been a significant factor behind new text analytics technology development.
Mining both solicited and unsolicited free-form content, organizing feedback according to the categories important to the business and analyzing complex and sometimes conflicting sentiments held within each piece of content are important capabilities for companies. To future-proof your business, you must be prepared to address these challenges by meeting the need for categorization and sentiment analysis for free-form text, verbatim and other unstructured survey data. This also means capturing social media feeds, online media feeds, forum comments and blogs, and enabling your business to capture, analyze and respond to customer and market feedback across multiple channels and sources in real time.
With the likely explosion of new connected devices, and with continuing rapid innovation in social networking, consumers have an amazing array of choices for providing feedback on an organization’s brand, products and services. They are voicing their views on new channels so it is vital that social and text analytics are incorporated into customer experience best practices to complement the insight that traditional, structured data delivers.
Organizations cannot ignore the changing landscape of customer interactions and the need to intelligently manage, map and mine them if they are going to correct the imbalance between too much data and not enough insight.