Your customers are talking to you, and about you, but are you gaining any value from those conversations?
In this blog post I’ll explore the importance of analyzing customer feedback and sentiment for business success in the digital age, and how Artificial Intelligence (AI) and text mining help overcome the inherent challenges organizations face when implementing “voice of the customer” analytics.
Positive or negative, customer feedback is important
Whether you are in retail, financial services, public sector, or any other organization that serves people, there is an opportunity to learn from their product, program, or brand (and competitors) feedback as they interact with your company.
When properly implemented, “voice of the customer” analytics provides valuable intelligence on customer perceptions to better engage with them through the personal experience and products they expect by answering questions such as “what are the most popular vs. most problematic features in my new product release?”
“We welcome your thoughts”
Of course, companies have been doing their best to listen to consumers for generations but “Voice of the Customer,” or “social listening,” is a more technical art that leverages digital communication and commerce for greater precision and follow-through.
For example, about 10 years ago a global bank launched a new mobile app that let customers send money using only the recipient’s phone number. The bank tracked social media comments about this app and found that while most were positive or neutral, there was also a significant amount of criticism on the application’s availability to minors, so it expanded access to act on ongoing complaints and capture future customers.
Additionally, the bank also experienced the unexpected benefit of product improvement. They added a whole range of new features thanks to the insights gained from their customers’ favorable comments on specific features.
Listening is the key
With so many obvious benefits, why isn’t everyone running “voice of the customer” analytics? Well, the actual “listening” presents the first hurdle.
When you consider the many types of customer data that evaluated, including emails to your company, instant messages, and social media feeds, you start to see why this is a fundamental challenge. Unlike analyzing structured data, like product ID numbers or pricing information, processing unstructured data – i.e. all the sentiment information that’s really interesting – is very complex.
So, how does one analyze conversations across millions of customers in real-time? With AI, of course!
AI-based text mining
Imagine the human effort of reading thousands of documents to understand and correlate their topics and context, and you begin to get a sense of the magnitude of the effort. And that’s not even considering the inclusion of other sources such as social media.
Machines can provide an efficient and scalable method of gaining value from information. By leveraging “text mining” and “natural language processing”, machines can read at the pace required for analyzing the voice of the customer while analyzing brand mentions. These technologies can identify people, places, things, events, and time-frames mentioned in written text, assign an emotional tone to each mention (negative, positive or neutral) and even understand if the document is factual or opinion-based.
Big Content: Scale at speed
Another hurdle to overcome for “voice of the customer” analytics is handling the volume of data, especially when considering large collections of correspondence in content management systems and adding them to rapidly growing sources, like social data. Enough data to bring typical analytics tools to their knees.
With AI and machine learning you can be effective at customer analysis as long as you have the horsepower to process and analyze that data quickly, at scale, to meet increasing volumes of data over time.
Just the facts: Graphics and dashboards for easy viewing
Another important consideration is how to communicate your insights to stakeholders so they can improve decision-making.
An effective solution focused on optimizing experiences must include shareable interactive visualizations to make the information accessible, understandable and easy to act on, maximizing its reach and benefit.
OpenText Magellan helps you track the voice of the customer
By now, maybe you’re starting to think of how to measure and monitor feedback and sentiment. OpenText can help.
OpenText™ Magellan, an AI-augmented analytics platform combines open-source machine learning with advanced analytics, enterprise-grade BI, and capabilities to acquire, merge, manage and analyze Big Data and Big Content. It is ideally suited for VoC analytics as it can analyze content from web sources, social media such as Twitter, and Enterprise Information Management systems including surveys.
Magellan’s solution, AI-powered Voice of the Customer includes strong natural language processing capabilities for textual sources of all kinds and includes features such as concept identification, categorization, entity extraction, and sentiment analysis. It is specifically designed for massive amounts of information and can scale up as data volumes grow. It allows organizations to deliver insights that are usable by everyone – not just data scientists but regular business users – by providing powerful dashboards and visualizations.
With the powerful combination of Magellan’s AI-powered Voice of the Customer solution and AI professional services, you can leverage feedback data and track sentiment, awareness, and relevance to realize the promise of “voice of the customer” analytics: Increase revenue by improving the experience you offer to your customers, creating dominant offerings. understanding social sentiment toward your brand (and competitors) or programs, protecting your reputation, and identifying decision drivers for your target market.
Are you ready to listen?
To learn more about how AI can help with customer analytics, click here.