Turn Online Reviews into Actionable Insights with Customer Sentiment Analysis from Uberall

Artificial Intelligence (AI), machine learning (ML), and natural language processing (NLP) have seen tremendous growth in recent years. Together they offer actionable insights to businesses that can help them make profitable decisions. However, when harnessed correctly, these technological advancements can enable customer sentiment analysis to "understand" and scale at a rate and accuracy never before seen.

What is Customer Sentiment Analysis?

For those less familiar, customer sentiment analysis is the interpretation and classification of emotions (positive, negative, and neutral) within the text. It helps brands discover what customers are saying about them in countless online reviews and posts. But, the added value of customer sentiment analysis is that it can provide marketers with both quantitative and qualitative research that goes beyond the "what" to help them understand the "why." For example, if a particular location has an overall star rating of three (quantitative data), I can drill into the "why" and might find that while customer sentiment is positive around the quality of the food, poor customer service is a common theme in negative and neutral reviews (qualitative data).

For customer sentiment analysis to be truly valuable to marketers, the tone and context of a consumer's words must be accurately understood. That means you need to teach a machine to not only learn, but apply its learning correctly to endless different scenarios. That is, it must analyze and label data well enough to enable it to identify patterns and sub-classifications, such as industry terms, slang, and even sarcasm. If a sentiment analysis engine isn't designed with this level of sophistication, it will deliver low value. Marketers need to trust that their sentiment analysis tool will know whether "cold soup" is a bad or good thing.

Extracting Insights from Unstructured Data

As consumers place more trust in user-generated content like reviews, brands need an easy way to efficiently and accurately parse the good from the bad. It's much easier said than done. Reviews, comments, and other data points on social media are large unstructured pieces of text that are not easy to analyze or quantify. In an age of data and data-driven businesses, brands must stay on top of this information and extract insights that are quantifiable and actionable.

We see this in particular with COVID-19. Consumer behavior has changed and many now look to social media first for up-to-date information and answers. Brick-and-mortar brands have had to adapt to meet changing consumer expectations and preferences – offering everything from curbside pickup to special hours for seniors. Sentiment analysis can play an important role in helping brands monitor and track how they're doing in relation to these new expectations.

Uncovering Customer Experience Trends

To help brands easily monitor customers' experiences and adapt quickly, Uberall has designed a breakthrough Sentiment Analysis engine, developed by Google and Uberall, and trained to understand the nuances of your customers' reviews.

It goes deep to deliver exceptional, industry-specific insight and contextual accuracy - all at-a-glance and at scale. It leverages state-of-the-art machine learning algorithms to transform unstructured, hard-to-analyze information into a collective understanding of whether a business is succeeding or failing according to its customers.

Multi-location brands, in particular, will find Uberall's Sentiment Analysis essential for uncovering trends, digging into specific location reviews, and determining how adjustments affect a brand or location over time.

Importantly, during the pandemic, Uberall's Sentiment Analysis engine included a COVID-19 theme to help businesses see, at-a-glance, which locations are carrying out COVID safety best practices and which ones are falling short.

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