Whenever a customer completes a transaction, writes a product review on your website or mentions your company on social media, there is always a feeling connected to their deeds. As a forward-thinking business person, however, you should understand not only how your customers feel (angry, neutral, happy) at the moment of purchase, but also what actions taken by you or your employees cause the sentiment.
Here’s where sentiment analysis — which is also referred to as “opinion mining” — comes in useful.
Sentiment — i.e., the whole specter of emotions behind a consumer’s interactions with a brand — lies at the heart of measuring and improving the effectiveness of a company’s customer engagement strategy.
By tracking and analyzing customer sentiment, you can achieve three major goals:
- Measure overall customer satisfaction.
- Estimate your customers’ loyalty & find ways to increase it.
- Make insight-driven customer engagement forecasts.
The issue with traditional sentiment analysis tools — including customer interviews and social monitoring, as well as ratings and polling, enabled on an eCommerce website or in a shopping app, — is that they can hardly process the 2.5 exabytes of raw data produced by the digital society on a daily basis.
AI algorithms, on the other hand, can analyze large volumes of customer data faster, eliminate bias and identify the smallest shifts in buyer behavior, thus enabling enterprises to continuously optimize marketing efforts. It doesn’t come as a surprise that the global emotion recognition and sentiment analysis market is projected to grow from just $123 million in 2017 to $3.8 billion in 2025!
The question is, will the implementation of smart sentiment analysis tools will give your eCommerce company superpowers?
Customer Sentiment Analysis in the AI Era: Making Sense of Unstructured Information
With today’s AI-powered opinion mining platforms (Lexalytics, Call Sumo, Cogito, etc.), as well as cloud services with advanced Natural Language Processing (NLP) capabilities such as Amazon Comprehend, Microsoft Azure Text Analytics API and Google Cloud Natural Language API, businesses can automatically capture and measure customer feedback — i.e., text reviews, comments, hashtags, and reactions — across a variety of channels including social media, forums, and aggregator websites. Some solutions — for example, InMoment, Google Cloud NL API and Amazon Recognition — can also be applied to analyze audio and video content.
How do AI-enabled Customer Sentiment Analysis Tools Work under the Hood?
Technology-wise, there are two ways to conduct customer sentiment analysis:
- Linguistic approach. Basic NLP opinion mining solutions scan documents, sentences or parts of sentences in search of keywords — i.e., emotionally colored words and phrases that characterize a customer’s general attitude towards a company or product. The approach has its limits, as keyword-triggered algorithms primarily operate within the “positive-negative” range and often fail to identify two opposite emotions expressed in a single sentence — for instance, independent clauses joined by the conjunction but or conditional sentences. Recurrent neural networks which compress sentences into vectors and consider word order (as opposed to assigning “weight” to each word separately) tend to display higher accuracy rates.
- Machine Learning (ML) approach. In order to conduct ML-driven customer sentiment analysis, pre-trained models — similar to those offered by cloud managed services providers including Amazon and Google — are required.
Aided by NLP algorithms, such models match input text against classifiers and evaluate it based on historical data.
Opinion mining solutions can be integrated into an existing eCommerce website, CRM system or mobile application back-end on the API level, which makes it relatively easy for online retailers to jump on the opinion mining bandwagon.
Having said that, the information intended for algorithm analysis should be gathered and prepared by data specialists. To manage the research data and act on it, you might also need custom dashboards and notification systems tailored to meet your specific needs. Furthermore, out-of-the-box customer sentiment analysis tools’ accuracy might vary depending on context: for instance, up to 75% of “negative” words may not at all convey a negative message when used in financial texts. In case no ready-made solution yields the desired results for your company, you may consider hiring professionals with the relevant eCommerce development experience to craft custom software.
How Businesses Use Smart Customer Sentiment Analysis to Increase KPIs
Samsung is one of the many customer-centric brands that leverage opinion mining tools for product research. According to Amy Vetter, the company’s Senior European Digital Insights Manager, the decision to monitor product launches via the Crimson Hexagon AI platform helps Samsung understand how consumers use their products and what features they like the most. The knowledge — as well as competitor analysis carried out with the help of the social listening platform — enables Samsung to enhance product functionality and tweak their marketing campaigns to maximize sales.
Marketing Campaign Optimization
To elaborate on the subject a bit further, let us analyze the infamous Hitchcock violin advertising campaign launched by Expedia Canada in 2014. In an attempt to capitalize on our hatred towards winter, the travel company chose a dreadful soundtrack for their ad, which resulted in a heavy backlash on social media. By automating sentiment analysis, Expedia managed to address the negative response in a timely manner: the company recorded two brand-new videos where the actor who starred in the original commercial and one of Twitter users who wasn’t keen on the ad smashed the awful violin into pieces.
Back in 2017 Ryanair, an airline company known for its affordable tickets and a meaningful relationship with the media found creativity amid a PR crisis caused by administrative problems and subsequent flight cancellations. To manage reputational risks more effectively, the air carrier addressed Aylien, a startup that that specializes in AI-driven text analysis. Having processed over 600 news stories and 30 thousand tweets published following the announcement of the cancellation (Ryanair did not issue a press release until Friday evening although they’d started canceling flights in the morning), the company managed to identify the locations most affected by the crisis, reschedule flights in a timely manner and measure the effectiveness of the old-school Friday evening announcement trick.
Walmart, the retail tycoon that reportedly processes 2.5 petabytes of customer data every hour, set up a dedicated analytics hub called Data Cafe to examine the information coming from Walmart’s 20 thousand US locations. One of its stores, for instance, registered a steady decline in sales in a particular product category; by analyzing transactional data, the Walmart researchers discovered that pricing miscalculations had been made, leading to the products being sold at a higher price! According to Naveen Peddamail, Senior Software Engineer at Walmart Labs, the smart approach to data processing allows the retailer to gain insights on customer and sales activities over a given period in just 30 minutes — compared to several weeks if the task is performed manually.
Effective Customer Service
Ocado, a British retail supermarket that serves half a million customers weekly, knows how to leverage Machine Learning to achieve the near real-time response rates. The company registered an increasing number of customer support tickets during holidays and severe weather and needed a new customer sentiment analysis tool to segment the emails into negative, neutral, and positive. Ocado developed a custom TensorFlow-based opinion mining solution, trained the program on three million unique customer emails, and equipped its customer support team with a powerful tool to prioritize customer issues. The company went on to incorporate smart algorithms into its warehouse management system to streamline picking and packing operations.
Other possible applications of the cutting-edge technology include live chat enhancement and conversational guidance platforms designed to help support and sales specialists evaluate a customer’s emotional state based on unconscious signals.
AI-assisted Customer Sentiment Analysis: New Frontier for Online Retailers
Recent studies show that retailers lose close to $10 billion worth in revenue annually due to the inability to capitalize on the insights gathered from social media, eCommerce aggregators, forums, and review websites.
Although 70% of enterprises have increased spending on customer analytics tools in the last five years, only a small fraction of companies admit being effective at capturing, processing, and acting on consumer data.
AI-powered sentiment analysis tools offer an incredible opportunity to revitalize your brand — provided you seek professional assistance, choose the right technology stack, and give context to the data acquired through sentiment analysis. Even though the accuracy of ML-assisted opinion mining solutions seldom exceeds 80%, you should not underestimate the value of automation: the traditional approach to customer sentiment monitoring is a time-consuming task, which contradicts the very principle of today’s nearly real-time eCommerce.
Author Bio: Andrei Klubnikin is a Content Marketer at R-Style Lab. He creates articles in collaboration with IoT experts and highlights the benefits of adopting cutting-edge technologies in business. Andrei writes for Clutch.co, DZone, IoT Evolution, IoT for All, StartUs Magazine, etc.
Opinion mining solutions can be integrated into an existing eCommerce website, CRM system, or mobile application back-end on the API level, which makes it relatively easy for online retailers to jump on the opinion mining bandwagon.