See real-world examples of how businesses across industries use AI sentiment analysis to inform their decisions and marketing strategies.
Online customer reviews contain a wealth of insight into customer likes, dislikes, behavior, intent, and partialities. Just beneath the surface of your Google Business Profile lie any number of metrics which could be applied to higher-level strategy or task-specific projects, each and every day.
We all know this, and would love an efficient way to get our hands on (and heads around) this ever-tempting motherlode of invaluable information.
The vast quantity and unstructured format of this data, however, can pose challenges for businesses trying to analyze and act upon it effectively. This is where artificial intelligence (AI) can be your next most valuable company asset.
In this article, we’ll provide 10 multi-vertical examples of how real companies are using AI to identify real strategic opportunities based on the remarkable depth of insight available within feedback and sentiment. Here’s an overview of what lies ahead:
AI-powered tools simplify the review analysis process, analyzing thousands of reviews at once and identifying prevalent themes and sentiments that we otherwise could not.
We hope some of these potential applications, already in play, will leave you as enthusiastic about AI as we are.
Before we dive in to these AI customer sentiment analysis examples, a bit more on "sentiment analysis" in general. In short, it's a powerful tool/technique that uses machine learning to understand the emotional tone of text, such as reviews, customer feedback or social media posts.
And, as mentioned, a deeper understanding of the sentiment spectrum allows businesses to gain a valuable view into their customers' preferences, experiences and pain points as they relate to the company's products, services and reputation.
At the core of AI-powered sentiment analysis lies natural language processing (NLP) which allows for a scalable interpretation of human language. It can determine whether a piece of flat text is emoting a negative, positive, neutral or mixed opinion - making it the perfect tool for analyzing, and acting upon, said sentiment.
Widewail's 2023 Voice of the Customer Report analyzed over 1.5 million Google reviews from over 16,000 new-car dealerships to unlock trends in customer behavior and sentiment.
The sentiment data illustrates that a car dealership will likely receive more positive reviews if it has quality personnel and effectively communicates with customers. To maximize positive reviews, dealers should invest in staff and training, especially focused on helpfulness and friendliness in the sales department.
The topic analysis also uncovered that basic communication will likely minimize negative reviews. Taking action to prioritize effective communication, specifically in the service department, should result in more positive feedback. AI sentiment analysis allows dealerships to quickly analyze large quantities of reviews and extract actionable insights that can be used to drive product improvements and monitor brand reputation.
Widewail conducted a topic analysis of 400,000 reviews to understand what residents care about in the multifamily residential market. The sentiment data tells us that maintenance is the largest pain point for residents and this is the sort of actionable insight that property managers can use in their reputation and retention favor.. Turning multifamily resident maintenance requests into positive experiences will have a major impact on minimizing negative reviews.
AI-powered sentiment analysis gives multifamily operators detailed insight into what residents care about, allowing for data-driven decisions to help improve the residents' experience, while improving retention rates and overall facility reputation.
Marriott International, one of the world's largest hotel chains, uses AI analysis to process and analyze customer reviews across its 7,000+ properties. They also lean heavily on chatbots on the corporate booking site. By identifying common themes and sentiments in reviews, Marriott can quickly spot areas for improvement, such as room cleanliness, staff friendliness, or amenity quality.
This real-time feedback allows individual properties to take swift action to address issues and enhance the guest experience. At a corporate level, Marriott can benchmark performance across its portfolio, identify best practices from top-performing properties, and make strategic decisions to elevate its brand reputation.
Amazon leverages AI sentiment analysis to process millions of product reviews and gather information related to customer satisfaction. Through an omnipresent analysis of review sentiment at a product level, Amazon can identify which products are meeting or exceeding customer expectations and which are falling short.
This information can be used to optimize product listings, improve product descriptions, and even inform product development decisions.
For example, if reviews consistently mention a product's difficult assembly process, Amazon can work with the manufacturer to simplify instructions or offer assembly services.
McDonald's uses AI analysis to monitor customer sentiment across its 38,000+ locations. By scrutinizing reviews and social media mentions, the chain can quickly identify emerging issues, such as a new menu item receiving negative feedback or a particular location consistently underperforming.
This actionable sentiment data allows McDonald's to take proactive measures, such as adjusting recipes, providing additional staff training, or addressing supply chain issues.
Continuous monitoring and response to customer sentiment leaves McDonald's better positioned to maintain its brand reputation and foster customer loyalty.
Delta Air Lines employs AI sentiment analysis to process customer feedback from various sources, including reviews, surveys and social media. Analysis of sentiment trends helps the airline identify areas of the customer journey that are causing frustration; these might include long check-in lines, flight delays or poor in-flight service.
These micro-details allow Delta to prioritize improvements and allocate resources to the most critical pain points.
A practical application: if sentiment analysis reveals a high volume of negative feedback around baggage handling, Delta can invest in more efficient baggage systems and staff training to improve the customer experience.
The Cleveland Clinic, a renowned healthcare provider, uses AI sentiment analysis to gauge patient reviews and feedback. By identifying common themes and sentiments in reviews, the Cleveland Clinic can pinpoint areas for improvement in patient care, such as bedside manner, wait times or facility cleanliness.
This level of detail allows the Cleveland Clinic to provide targeted training to staff, optimize processes to reduce wait times and enhance facility management to improve patient satisfaction. Through constant automated monitoring of patient opinion, the Cleveland Clinic is providing the highest quality of care and maintaining its reputation as a top healthcare provider.
Coursera, an online learning platform, leverages AI sentiment analysis to process student reviews and feedback for its courses. Aggregating sentiment at a course level, helps Coursera identify which classes are providing the most value to students and which ones need improvement.
This information can be used to optimize course content, improve instructor performance, and inform the development of new courses. If sentiment analysis reveals that students consistently find a particular course's assignments too difficult, Coursera can work with the instructor to adjust the difficulty level or provide additional support resources.
Netflix, the streaming giant, uses AI sentiment analysis to process viewer reviews and social media mentions for its original content. Sentiment trend aggregation helps Netflix keep constant track of the popularity and reception of its shows and movies, helping inform decisions on which content to produce or acquire at scale in the future.
If sentiment analysis reveals that a particular genre or theme is resonating with viewers, Netflix can invest more in similar content. Conversely, if a show receives consistently negative sentiment, Netflix may decide to cancel or reshape the series.
Electronic Arts (EA), a leading video game publisher, employs AI sentiment analysis to process player reviews and feedback for its games. By analyzing sentiment at a game level, EA identifies which features players enjoy and which ones could use just a wee bit more improvement.
Potentially overlooked data like this can inform game updates, bug fixes, and the development of future titles. If feedback analysis reveals that players are frustrated with a particular title’s microtransaction system (a beyond-common pain point in the video game space) EA can adjust the system to be more player-friendly and improve opinions and third-party critic reviews overall.
It's probably obvious by now that it's a powerful tool allowing businesses to quickly process large volumes of feedback-related data. It fast tracks the ability to make data-driven decisions to improve products, services and the overall customer experience.
AI sentiment analysis is also applicable across a wide range of industries, from automotive and hospitality to e-commerce and entertainment. By continuously monitoring and responding to customers, businesses maintain a positive online reputation, drive customer loyalty and earn a competitive edge in their respective markets.
As AI technology advances, the potential for sentiment analysis to transform business intelligence and decision-making will only grow. Companies embracing this powerful tool will be well-positioned to meet the evolving needs and expectations of their customers in the digital age. In short - it's high time to do likewise.
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