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Mar 17, 2020
With rapid investment in customer experience management (CEM) platforms, leading brands know the quickest path to growth starts with listening to customers. But an increasingly complex customer journey spanning multiple touchpoints—many with distinct feedback channels—can make surfacing actionable insights seem like a daunting task.
To drive real change, brands require enterprise-level platforms that translate mountains of data into targeted insights that drive business outcomes—consistently and efficiently. And with customers growing weary of lengthy surveys asking about every aspect of their experience, open-ended comments offer enormous potential for streamlining the feedback-to-insight process.
But just because brands know it’s important doesn’t mean they’re doing it effectively. Most text analytics technologies either can’t handle the volume and complexity of the data being processed or they’re so convoluted that it’s difficult to know where to start.
How can brands ensure their text analytics solution is providing the level of insight they need? Let’s look at 3 steps you can take to unpack the complexities of text analytics and formulate a strategy that will optimize customers’ open-ended feedback:
Before you can properly evaluate your current text analytics solution or determine the right tech investment for the future, you need to be familiar with the lingo. Here are some definitions to help you brush up on the technical terms of text analytics:
These terms can be tricky, but knowing the basics is the first step in implementing a successful text analytics strategy. Once you have a solid foundation, you’ll understand what’s applicable to your CEM program and how to best incorporate the solution.
Next, it’s time to thoroughly vet providers to find the right fit for your needs. Not every text analytics solution is equal. Some will take a bare-minimum, DIY approach—leaving the heavy lifting to you and your IT team. Others will flaunt fancy features that seem promising, but are wrapped up in a confusing interface. The best text analytics technology will be accurate, powerful, and intuitive by providing:
Top-tier accuracy: You need customized libraries built for your specific industry, so the text analytics engine will look for relevant categories and terms. In addition, you shouldn’t have to depend on manual coding. Find a provider using human-assisted machine learning with complex algorithms that continuously fine-tune sentiment accuracy.
Powerful reporting: You need to be able to visualize your verbatim feedback—seeing aggregated scores across all feedback sources—so you can understand the data holistically. Plus, you need to be able to slice and dice the data so you can translate that omnichannel feedback into actionable insights.
Brands must thoroughly evaluate the functionality of a text analytics platform prior to making investment decisions. Forrester states “as more firms invest in voice-of-the-customer (VoC) technologies, vendors’ integration capabilities, text analytics, and service offerings will dictate where the money is best spent… Buyers should look at the industry ontologies a vendor supports, the languages available, and how the text analytics technology works—whether it is rules-based, supervised machine learning, unsupervised machine learning, or a hybrid. Buyers should have vendors do a live proof of capabilities during technology demonstrations— enabling the buyers to get a feel for the tool’s true out-of-box functionality.”
And finally, be sure to break down data silos and integrate your data. Just like survey scores don’t exist in a vacuum, neither do comments—and you need to understand the relationship between both to get the full picture. Every iteration of the customer experience offers an opportunity to gain a more nuanced understanding of where to improve.
Brands must bridge quantitative and qualitative data. Correlating mentions and measures will help draw a direct line between what’s being talked about and how those topics affect the experience. A solution that integrates text analytics with other datasets will help brands:
Whether it’s specific products, new service standards, or recent operational changes, noting when customers call out those experiential variables—and more importantly, understanding how they impact important measures—is mission critical to building an innovation strategy that resonates.
More than ever, brands need robust, intuitive tools designed with end users in mind—especially when it when it comes to input as complicated as the unstructured data found in customer comments. The right text analytics technology will provide the actionable insights you need to drive meaningful change in your business.
For more on how text analytics helps brands mine customer comments for deeper insights, check out this video on text analytics technology.