Your customers are talking to you, and about you, but are you gaining any value from those conversations?
Success in business requires a relentless focus on customers. And learning from their own words provides you with the insight to better engage with them through the personal experience they expect. So how do you measure and monitor customer feedback and interactions across multiple sales channels, customer service centers, social media, and documents of all types, in real time?
In this blog post I’ll explore the reasons why analyzing customer activity and opinion is important 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, transportation, telecommunications, or any other business that sells to consumers, there is a huge opportunity to learn from their comments about your offerings as they interact with your company, as well as when they talk about your brand (and competitors!) in online social media and review sites.
Aggregating customer feedback across all available channels and outlets lets you analyze the full range of their comments and opinions, and extract insight that helps to improve products, provide a superior customer experience, and anticipate trends that affect your business.
When properly implemented, “voice of the customer” analytics provides valuable intelligence on customer perception. It can answer questions such as “what are the most popular vs. most problematic features in my new product release?” or “will a high-profile celebrity’s involvement with my brand help or hurt sales?”
Learning from your customers’ own words allows you to provide just the right response at just the right time, improve your products and services, protect your reputation, and predict future trends that will impact your bottom line.
“We welcome your thoughts”
Of course, companies have been doing their best to listen to consumers for generations – everything from running contests for new ice cream flavors to reviving cancelled TV shows if they get enough mail (and strange things like bottles of Tabasco sauce) from anguished fans. “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.
Users were complaining the app wasn’t available to minors, which inconvenienced both teens and their parents. Naturally, the bank didn’t want to alienate a whole generation of future customers, so it expanded access. That’s a standard best-practice in customer relations: When you hear a complaint, act on it.
However, the bank also found an unexpected benefit in social listening – product improvement. Customers were making favorable comments about relatively obscure but novel features, such as the convenience of checking their balances in real-time from their phones. Surprised but pleased by the reaction, the bank added a whole range of new features and apps.
Listening is the key
With so many obvious benefits, why isn’t every company running “voice of the customer” analytics? Well, the actual “listening” presents the first hurdle.
When you consider the many types of customer data that must be acquired and evaluated, you start to see why this is a fundamental challenge. Structured data, like product ID numbers, pricing information or even opinion survey data is easy for software to tally up and analyze. The hard part is all the unstructured data – i.e. everything that’s really interesting.
Customers contact your company through email, instant messaging and by phone, where they tell you exactly what they think. Imagine the potential of harnessing that information!
Customers also take to social channels such as Twitter and Yelp to tell other consumers what they think of your brand – good, bad or ugly.
Each of these sources presents its own nuances, but when you consider that most of them are unstructured and written in natural language, you can see why merging and analyzing multiple sources is so complex.
How does one quantify and measure a conversation across millions of customers in real time? How does an organization gain the benefits of truly listening to its customers? With AI, of course!
AI-based text mining
When you consider all the varying types of customer data, and the complexity of reading and merging it at scale, you can see why machines provide the only efficient method of parsing, understanding and gaining value from the information. The application of AI for analyzing text is known as “text mining” (also called “natural language processing”). Machines can learn to read and identify people, places, things, events and time-frames mentioned in written text, assign emotional tone to each mention of them (negative, positive or neutral) and even understand if the document is factual or opinion-based.
Text mining is important for analysis in its ability to “read” unstructured textual data, which contains more context and valuable insights than structured, transactional data because it reflects the authors opinion, intention, emotion, and conclusions.
But humans can already do all this, right? Why do we need machines to do it for us? Because only machines can conduct the analysis at scale.
Imagine the human effort of reading tens of thousands of documents to understand and correlate their topics and context, and you begin to get a sense for the magnitude of the effort. And that’s not even considering inclusion of other sources such as social media. Only machines can read at the pace required for analyzing the voice of the customer, acquiring and processing thousands of documents and articles every second, merging, aggregating and persisting the information while analyzing it for mentions of your brand, and more importantly for the tone of those mentions.
Big Content: Scale at speed
Another hurdle to overcome for “voice of the customer” analytics is handling the sheer volume of data, especially when you consider large, existing collections of documents like contracts or correspondence stored in content management systems. Harvesting these sources and adding them to other sources that grow quickly, like social data and CRM data, results in huge amounts of information that must be processed and analyzed – enough data to bring typical analytics tools to their knees.
With AI and machine learning comes an assumption that the more data you have, the more accurate your predictions become. But this also assumes you have the horsepower to process and analyze that data quickly, at scale, without dimming the lights in your city. To be effective at customer analysis, your AI solution must be able to process immense amounts of data efficiently, and should scale to meet increasing volumes of data over time as it is collected and persisted.
Just the facts: Graphics and dashboards for easy viewing
Gaining insight isn’t the end of “voice of the customer” analytics. Another important consideration is in how to communicate your insights to stakeholders so they can perceive and take advantage of them as easily as possible.
Shareable interactive depictions of analysis results and recommendations make the information accessible, understandable and easy to act on, maximizing its reach and benefit.
An effective solution must include data visualization and provide dashboards with charts, maps and alerts that answer questions and provide advice on next best actions to optimize engagement or operations.
OpenText Qfiniti helps you track the voice of the customer
By now, maybe you’re starting to think of what it would take to build a Voice of the Customer solution of your own. OpenText can help.
Start with a platform based on OpenText™ Qfiniti, a unified, centrally managed platform for multichannel interaction analysis. Enterprise contact centers around the globe rely on OpenText Qfiniti for fulltime call recording, robust workforce optimization (WFO), and advanced cross-channel analytics. The fully integrated Qfiniti platform optimizes performance management, liability recording and archiving, workforce management, desktop analytics, and Voice of the Customer (VoC) insight.
Specialized modules bring advanced VoC, speech and multichannel analytics to the contact center and the broader enterprise, providing analysis of all customer interactions across voice, email, chat, web and social media interactions. They also provide secure, cost-effective deployment of cloud-based voice and web surveys for both inbound and outbound customer insight.
Tying it together: OpenText Magellan
The finishing touch comes when you add OpenText Magellan, an AI-augmented analytics platform ideally suited for VoC analytics. The 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, especially content from web sources or Enterprise Information Management systems.
Magellan 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 efficiently crunching massive amounts of information, and can scale up as data volumes grow. And by providing enterprise-grade BI, it allows the organization to deliver insights that are usable by everyone – not just data scientists but regular business users.
With the powerful combination of Qfiniti for customer engagement and Magellan’s AI-enhanced analytics and text mining capacities, you can leverage data to realize the promise of “voice of the customer” analytics: Engage your customers with the right offers at the right time, provide your product group with the valuable feedback they need to create a dominant offering, understand social sentiment toward your brand (and competitors!), protect your reputation, and identify and predict likely future customer behavior.
Are you ready to listen?
To learn more about how AI can help with customer analytics and other real-world challenges, click here.