Do you remember a time when you were always questioning what others were thinking about you? Jimmy just said my new shirt was neat, did he really mean that? Or was he joking? Should I change? You were likely advised that as long as you feel good, don’t worry about what others think. Although that is great advice for our personal lives, the same can’t be said for brands, products, services, and ad campaigns. In these cases, the opinions of others (aka your customers), is all that matters.
Luckily, customers often talk about products, services, brands, etc. on their social media pages. This lets companies have a direct connection to their customers and better understand their thoughts, opinions, and preferences. Up until recently, social media metrics such as engagement rates, likes, follows and mentions were the main ways companies determined how their consumers really felt. Now thanks to AI, there is a new wave of tools to help companies get to know their customers better.
Natural Language Processing
Before diving into these great marketing tools, lets first look at Natural Language Processing (NLP). NLP is a form of AI that uses machine learning to understand human language. By giving examples of human phrases, syntax, and semantics, computers can learn to understand human conversations. NLP has been very useful for marketers as it is the main technology powering the below tools.
Sentiment Analysis
Companies use sentiment analysis to determine the general attitude behind what someone has written online. Using NLP, computers learn to tie positive, negative or neutral emotions with certain words. For example, “amazing” would be tied to a positive emotion, and “horrible” would be tied to a negative one. With this knowledge, computers can run through social media channels and find posts, reviews, and discussions about a company and decide whether the comments are positive, negative or neutral.
Knowing in real-time whether consumers are happy with a new product or upset with a service is very helpful for companies. This lets them make effective changes quickly and be better in the future. Similarly, knowing how your customers feel about your competitor is great in helping to find opportunities for competitive advantages.
Intention Analysis
Remember that time we worried about what others thought of us? Often, one of our main worries was trying to decide if there was a hidden meaning behind the words of our peers. Luckily, when it comes to companies, AI can help. Intention analysis helps companies know whether the words of their customers show an intention to buy, ask, complain or suggest.
By understanding the meaning behind the words, companies are better positioned to use this information. For example, if someone is showing interest in buying a new TV on their social media pages, a television company can begin sending targeted ads with their latest products. Conversely, if many people are complaining of a faulty feature on a product, the company can begin making changes and fix the issue as fast as possible.
Too Good to Be True?
As we’ve discussed many times on the Sidepart Blog, data is king. The more insights a company has on its customers the better positioned they are for success. Therefore, sentiment and intention analysis are great tools for marketers as they both give immediate and actionable insights. However, this is not to say that these tools do not come without their flaws.
Like any technology, there is always room for improvement and issues can often occur. Language is complex, and sarcasm, misspelling, and slang can be confusing for NLP. This confusion can skew the data and make it hard for companies to get the accurate insights that they need. Therefore, it is helpful to mix many tools to get a complete look at your customers’ opinions and confirm all the data you’ve collected.
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References
Dayal, P. (n.d.). What is Intent analysis and how can it be used? Retrieved from https://www.newgenapps.com/blog/what-is-intent-analysis-how-can-it-be-used
Garbade, M. J. (2018, October 15). A Simple Introduction to Natural Language Processing. Retrieved from https://becominghuman.ai/a-simple-introduction-to-natural-language-processing-ea66a1747b32
Gupta, S. (2018, January 19). Sentiment Analysis: Concept, Analysis and Applications. Retrieved from https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17
James, M. (2019, April 5). Big Data Paves The Way For Fantastic New Social Listening Tools. Retrieved from https://www.smartdatacollective.com/big-data-paves-way-for-fantastic-new-social-listening-tools/
Pandey, P. (2019, August 6). Sentiment Analysis is difficult, but AI may have an answer. Retrieved from https://towardsdatascience.com/sentiment-analysis-is-difficult-but-ai-may-have-an-answer-a8c447110357
(n.d.). Sentiment Analysis: How Does It Work? Why Should We Use It? Retrieved from https://www.brandwatch.com/blog/understanding-sentiment-analysis/
(2019, August 30). What is Sentiment Analysis and How to Do It Yourself. Retrieved from https://brand24.com/blog/sentiment-analysis/
