According a recent McKinsey study, after a positive customer experience, more than 85% of customers purchased more. After a negative experience, more than 70% purchased less.
Companies have been using various tools and metrics to quantify customer satisfaction and rectify negative customer experiences. With the proliferation of digital platforms however, where everyone can voice their satisfaction or dissatisfaction for a brand or a product, effectively monitoring all communication channels is becoming ever more challenging. Similarly, analysing and understanding the sentiment within customer reviews can have several technical difficulties that might prevent you from capturing important information about your business:
- Nuance detection: A sentiment analysis solution should be able to accurately detect, classify and understand sentiment while at the same time, also be able to detect the multi-faceted nature of text language like context, jargon, ambiguity, sarcasm etc. Traditional tools, may fail to capture underlying nuances like sarcasm and irony:
- Proliferation of sources and input: Today consumers have the power to express their opinions across a large number of platforms -some provided by the company (email, forms, call centre etc) but many others outside your control (market places, social media, comparison websites etc). Sentiment analysis solutions should be able to continuously, ingest, store and analyse customer reviews without being limited by the volume, source, or format of these reviews.
The Machine Learning advantage
Machine Learning enables you to effectively capture and analyse the nuances and underlying opinion in customer reviews across different channels.
With a Machine Learning approach to sentiment analysis, you can aggregate all textual references across internal and external channels -website, customer service department, outsourced call centre, marketplaces, social media etc. This information is analysed by machine learning algorithms in real time, to highlight important feedback in accordance with your queries.
Another major advantage of Machine Learning is the ability to ‘train’ the algorithms. Using Natural Language Processing (NLP) alongside sentiment libraries, sentiment corpora and other human-annotated sentiment rules, algorithms are continuously enriched, becoming faster and more accurate.
The benefits of using Machine Learning for sentiment analysis and opinion monitoring are several:
- Speed: With Machine Learning you can automate the sentiment analysis of millions of product reviews without the need of involving human reviewers, thereby reducing the time and effort involved in collecting, storing and classifying customer reviews.
- Accuracy: The use of Machine Learning algorithms, allows you to classify the sentiment of a customer review with precision and accuracy without the limitations of traditional sentiment analysis tools.
- Agility: By identifying trends and changes in customer sentiment in near real-time, you can become more proactive and competitive, with more opportunities to increase your customers’ satisfaction, improve their experiences and prevent attrition.
Increasingly, customer sentiment expressed in customer reviews can determine the success of a product and impact the reputation of a brand. For this reason, analysing and understanding sentiment in customer reviews is now a must for B2C companies across industries.
Introducing Machine Learning into your sentiment analysis and opinion monitoring process, will give you the peace of mind that your customers’ sentiment will not go undetected, enabling you to prevent escalation or unwanted ripple effects that can harm your reputation and your bottom line, or reward loyal and positively engaged customers.
If you would like to find out more about how Machine Learning could help you become more agile and more competitive, do give us a call at +44 (0)203 475 7980 or email us at firstname.lastname@example.org.
Other useful links:
How will Artificial Intelligence change the banking industry
Animation: Big Data made simple
Machine Learning for Competitive Intelligence in Retail – White Paper