Why FinServ Companies Need Sentiment Analysis

In a highly commoditized and competitive industry like Financial Services, customer experience is the ultimate measure of success for companies. A change in customer experience ratings for a multichannel bank leaves $124 million on the table for every 1-point decline in its CX Index score, Forrester's Customer Experience Index said.

The Data Challenge Facing FinServ Companies

To measure, understand, and take action to improve the customer experience, FinServ companies need to tackle the challenge of unstructured data. In the past, CX teams measured the efficacy of the brand through structured data—data that’s measured, quantifiable, relative. Think CSAT or NPS scores, demographic data, dates, etc. Measurements from structured data were often good enough for most companies, but advances in Artificial Intelligence like Natural Language Processing and Sentiment Analysis are letting data-driven CX leaders move beyond relying on structured data.

Unstructured data holds a treasure trove of customer insights that most companies aren’t tapping into in a meaningful and scalable way. (Read more about the difference between structured and unstructured data in this article.) The challenge will only grow with time: IDC estimates that by 2025, 80% of an organization’s data will be unstructured. The increase is largely due to the proliferation of customer data. Companies are already bursting at the seams with text and audio from surveys, social media, product reviews, contact centers and more.  

FinServ companies are no exception. Surveys play an important role in measuring the customer experience, but more often now customers are providing feedback on apps and products in other ways. We analyzed one mode of feedback—Consumer Financial Protection Bureau comments—in our most recent report to uncover the trends in the industry last year. If FinServ companies aren’t careful, the advance of digital transformation will create dozens of silos of critical unstructured data.

As more data becomes unstructured, more insights will be locked away for one reason or another—not enough time or person power to sift through the data; their traditional analytics tools can’t provide the necessary context for trending topics; or executives may not be convinced it’s worth the effort.

That’s why the advancement of Smart AI technology matters so much. It takes away these hurdles by putting the power of AI into more business users’ hands and reducing the time to insights from weeks and months to when they’re needed in the moment.

Why Smart AI-Powered Sentiment Analysis Matters

In a perfect world, you’d be able to review all forms of feedback—from product reviews and survey responses to social media posts and employee feedback—with your own two eyes. Keeping up with so many channels all day and night manually is impossible for even the most dedicated companies.  

That’s where Artificial Intelligence comes into play. AI that understands natural language can ingest and analyze the data at a speed and scale that would otherwise be impossible. Advanced iterations of sentiment analytics like Stratifyd’s Smart AI have more nuanced understanding of language and can reveal the topics that are generating positive or negative emotions in the data.  

      Avoid Sample Size Biases

One of the challenges facing Contact Centers is how to analyze the endless stream of calls and chats. Are customers happy or upset when they call in and why? And when it comes to agent performance, are individual agents delivering satisfactory service to customers? For FinServ companies, knowing the answers to these questions for every call is critically important.

Since there are only so many hours in the day, Contact Center managers rely on random samples to evaluate performance. Sentiment analysis can help uncover the trends and anomalies in all calls. That means managers can track overall agent performance while flagging the outliers for manual review. This helps avoid the potential for sample size bias and ensures managers have a complete picture of what’s happening in the interactions.  

Sentiment analysis helps improve training when they know what topics are trending, and it can help find problems that would otherwise fly under the radar with random sampling.  

     Predicting Emerging Issues More Quickly

Smart AI allows you to ingest tons of unstructured data and quickly turn it into insights. Traditionally, CX teams rely on surveys to understand emerging issues, but this can have a serious lag time—if consumers are responding to survey requests at all. An example below from our recent webinar, The CX Best Practices You Didn’t Know You Needed for FinServ, the lag between when sentiment analytics in the Contact Center might pick up the issue and the emergence of it in survey data took weeks.  

In our analysis of the CFPB data, we uncovered that there was a spike in comments for Mortgage providers, but in this instance the increases were driven by a single bank that had a system issue that withdrew monthly payments multiple times. Since the consumers tried to give feedback through other channels but weren’t receiving a response in a timely manner, they filed a complaint with the CFPB. Sentiment analytics could have prevented the escalation through chats or the Contact Center automatically flagging this urgent issue quicker.

     Mine for Competitive (Emotional) Intelligence

Keeping up with your own data is enough of a challenge without AI help, but keeping up with the Joneses is nearly impossible. Sentiment analytics can monitor your own social media mentions and product reviews to understand feelings towards your products and brand in the market; it can also be used to monitor your competition.  

Mining publicly available data like this gives your product and marketing teams valuable insights to consumer sentiment towards yours and your competitors’ products. It helps inform product direction—such as revealing improvements for your bank’s app—or give your marketing team competitive insights for crafting winning messaging in their consumer outreach.  

Protecting your brand in the market requires listening to everything; sentiment analytics surfaces key topics and the emotions related to those topics so you can respond quicker to issues.

What You Don’t Know Is Hurting Your Brand

We’ve reached the point where not analyzing your unstructured data is costing you. Sentiment analytics has improved to the point that it is a reliable way to synthesize large data sets while giving you the confidence you can home in on the right anomalies when needed. It frees up your team members to act, rather than sifting through data and making risky guesses about what’s really happening.

In our experience, sentiment analytics is a critical way for brands to uncover the data signals they were not looking for but need to find. Even when you don’t know what to look for, Stratifyd's Smart AI intelligently flags trending topics and emotions saving you time and money faster.

That’s also why we built a Smart AI-powered Experience Analytics Platform that can take in data from any source—because we know there are money-saving, efficiency-enhancing, and revenue-generating insights to uncover in every interaction. When you read our latest report, The State of FinServ Customer Experience 2021, you’ll see how sentiment analysis can uncover valuable CX insights anywhere.

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