Conversational marketing has been in the mainstream for quite some time now; the reason is the need for real-time interactions with your customers. Chatbots are the pivot of conversational marketing, having been built on the principles of Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG).
However, the era of big data means that you may need more than just Chatbots to ensure that your customers don’t churn. It’s not just all about conversation; how much do you know about your customers?
You need to understand the pain points of your customers, their sentiments about your product or service, and how they rate your product as good, bad, or neutral. While conversational marketing has a lot of merits, fusing it with sentiment analysis can be very productive.
Your customers live and revolve around data; studies have shown that your customers will create 463 exabytes of data each day globally by 2025. Part of this data will be what and how they feel about your product, which you must do everything to know about if you want to stay relevant in the business world.
You can use any of the following channels to discover what your customers say about your product or service.
1. Review sites
You may want to use sites that collect public reviews of different products to source what customers feel or make use of eCommerce sites like Amazon and eBay where people leave reviews about their experience with products. You must, however, understand that reviews from these sites are unstructured and not easy to understand.
You may end up putting a lot of manual labor to bring the data into a structured format and analyze the data.
2. Social Media
Social media platforms allow consumers to freely comment on products and services. Apart from these, you can also get reviews from forums and Q&A sites that allow consumers and the public to share their feelings on specific topics.
While these are channels you can use to collect data and views from consumers about your product, you must understand that these sources may not be authentic, and it may not be easy to categorize the sentiments into positive, negative, or neutral.
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Sentiment analysis of customer product reviews using AI
Since the data you collect from review sites and social channels are in an unstructured format and difficult to analyze, Natural Language Processing and machine learning become indispensable here.
With machine learning tools, you can spot the difference between context, sarcasm, and misapplied words. This is possible because of the integration of technological advancements,
several techniques and complex algorithms such as Linear Regression, Naive Bayes, and Support Vector Machines (SVM), you can use to detect user sentiments.
An interesting case study is how the Consumer Insights and Product Development Teams at TTI made use of insights gathered by Revuze’s AI to identify customer pain points for mid-range household carpet washers.
The product development team made use of these insights to design, develop, and release a special add-on to one of their best-selling products in this category. The changes which the TTI’s marketing team carried out on its product description page (PDP) bring to focus the power and impact of sentiment analysis of customer product reviews using AI.
The result was an improvement in customer satisfaction and the average star rating of a product that sells more than 300,000 units per year in the North American market alone.
Ryan Caycova, Senior Product Manager of TTI has this to say, “Revuze Explorer allows us to make decisions based on consumer analytics insights data and understand what customers want, how to improve existing products, and gain a competitive edge by creating better products with added value to consumers.”
If you have insights that outline consumer pain points, they enable you to:
- Comprehend what your customers relish and hate about your product.
- Compare your product reviews with your competitors.
- Obtain real-time product insights anytime.
By deploying machine learning into sentiment analysis, you enable the use of computational linguistics that has far-reaching effects than the mere detection of words in a sentence. You can also use it to match sentiments to entities as well as identify sarcasm to accurately recognize the emotional tone behind a sentence.
High-level programming languages such as Java, Ruby, and Python are what you use in sentiment analysis to enhance advanced programs for data acquisition, processing, feature extraction, supervised learning, and result in classification.
Apart from using review text data to conduct sentiment analysis, you can use it to increase your business’s local search engine results. Consumers don’t readily depend on only word-of-mouth again, they go online to check out reviews before purchasing.
Google recognizes how important this is and uses the same data to provide its users with the most relevant result. Google can filter out the local businesses with bad reviews or lower ratings and displays only the best brand, products, or services in the user’s locality.
Sentiment analysis makes use of machine learning tools that have been designed to read beyond mere definitions. It can detect the exact feeling in the text and tag them accordingly.
The competition is getting fiercer daily, therefore, you must turn to sentiment analysis to stay relevant. Even the big brands in the market are making use of this technique to improve the customer experience.
The better you understand each customer, the more you will give a personalized experience. Even if your product is relatively new in the market or popular, you can’t help without using sentiment analysis.
If you improve customer experience, you are certain of being ahead of the competitors.