How AI-Powered Customer Sentiment Analysis Software Enhances Brand CX


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The internet never closes, even if your physical locations do. While employees might shut off the lights and lock the doors at the end of a long day, consumers and potential consumers engage with your brand online 24/7 — posting and reading reviews about your brand’s CX — the great, the so-so, and the downright ugly. 

From Yelp to Google to OpenTable, every rating, and review is on full display, shouting out to the public — and potential buyers — exactly how past customers recently felt about a business. That reality alone is enough to keep most marketing teams and brand leaders up at night pondering how to enhance their brand’s CX at every location for every touchpoint along the customer journey. 

Keeping up with reputation management and review response IS a daunting task, especially for large, multi-location businesses. But it doesn’t have to be quite so herculean thanks to the many excellent review and reputation management platforms. These solutions streamline the reputation management process and make it much more straightforward for brands to engage with their consumers online. 

If you’re looking for online reputation management support, consider these essential features as you embark on your search: 

  • Multi-channel review aggregation feeds
  • Bulk response features
  • Suggested or AI-generated review responses
  • Review response template libraries
  • Data analysis and VoC feedback to inform business strategy

To keep up with the competition and rapidly changing market trends, companies must listen deeply to the wants, needs, and expectations of their customers — and it’s within online reviews that brands can find illuminating revenue-boosting insights.

As AI and Natural Language Processing (NLP) technology advances, more organizations are leaning into programs designed to uncover the insights that matter most with deep listening technology. By leveraging these toolsets, brands of all sizes and industries can seamlessly mine the plethora of structured and unstructured data hiding inside hundreds, thousands, or even millions of online feedback responses. 

With programs designed to pull, analyze, and organize every bit of the “chatter that matters,” organizations can use data-backed results and reporting to guide smarter business decisions. 

Level Up Your Reputation Management Program to Exceed the Competition with Sentiment Analysis

AI and NLP technology gives depth, context, and layered meaning to every online review. Reputation management software combined with AI- and NLP-powered customer sentiment analysis allows brands to better understand how customers feel about every step of the buying experience. 

This is the deep data that brand leaders rely on to quickly gather CX context and nuance that enable them to craft data-backed strategies that impact the customer’s journey for the better. 

This fast-evolving tech helps businesses surface the raw, unvarnished sentiment that customers think about a company’s products, customer service, atmosphere, friendliness, and efficiencies — anything and everything along the buying journey. With these precise insights, brick-and-mortar brands become more agile and can take highly targeted actions to drive more foot traffic, boost customer satisfaction, and increase loyalty.

Small businesses and enterprise brands alike can use sentiment analysis software for:

  • Identifying repeat customer experience issues
  • Uncovering location-based wins and pain points
  • Validating the successes of trial products or services
  • Discovering new and exciting business opportunities
  • Improving local and brand-wide customer satisfaction

Sentiment analysis solutions also save time and resources. Instead of sifting through countless reviews for clues, or making assumptions based only on a smattering of customer feedback, all the VoC data is surfaced on a single, easy-to-understand, and fully-customizable dashboard and/or delivered as a report. 

Many products offer options like filtering by topics, viewing topic frequency, and seeing how often people post topics with each sentiment. This helps brands identify areas of concern and get to the root of each issue fast. 

How One West Coast Retail Chain Improved Their CX Through Deep Listening 

For a real-world example, a popular clothing chain based on the West Coast noticed a significant drop in its online reputation. After years of consistently positive online reviews, their star ratings dropped from a consistent 4.4 to a troubling 3.7 over a few months. 

The corporate marketing team used its reputation management and customer sentiment analysis platform to dig into trending keywords consistently tagged with negative sentiment. For the first time, keywords for “staff” and “employees” were associated with more negative sentiments than positive ones.

Drilling down further with deep listening technology, the team found dozens of customer reviews within a specific region complaining about unprofessional and inattentive staff and service. The marketing team informed company leadership of their findings, providing hard data to back them up. The leaders took action, restructured staff within that region, and implemented additional brand-wide employee training for CX improvement. 

Within six weeks of those changes, negative reviews began trending down, positive sentiments for customer service-related keywords again were trending, and 5-star ratings increased across the enterprise.  

Without sentiment analysis and deep listening tools, marketing teams must manually read, analyze, and understand every review to gain modest insights into customer pains and praises. With it, organizations can instantly access in-depth intelligence and confidently make smarter, strategic, and data-driven business decisions.

Rachel Meyers-Hawk
Rachel Meyers-Hawk is a Denver-based content marketer at Chatmeter specializing in SaaS and B2B strategy. Her diverse work strives to support brands and clients in understanding and leveraging today's most innovative technology for lasting success.


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