Customer Sentiment Analysis + AI: A Perfect Match for Improved Marketing Strategy
Brands have often used focus group, survey and feedback methods to understand their buyer persona and responsiveness to product/service for creating engaging customer experiences. However, with the emergence of social media and growing digital footprints of customers, it became a challenge for brands to understand the dynamics of buyer behaviour, especially in reference to the underlying emotional tone and context in their text and conversations. Even content creators and marketers have realized the need to migrate from run-of-the-mill and cold content to empathetic content to engage their audience.
The need for a technological tool that could decode the emotions of the digital audience became apparent. That’s when sentiment analysis began to gain momentum and went on to become a buzzword in content optimization and consumer engagement marketing.
The global sentiment analysis software market is projected to reach USD4.3 billion by 2027 at a CAGR of 15.5%. This indicates that more brands will be shifting their focus to sentiment analysis in the coming years to strengthen their digital marketing and content strategy.
Understanding Sentiment Analysis
Sentiment analysis is a technological tool to analyse the reactions, attitude or emotions of the users towards your product, service or any content format.
You may think that it is easy to do that. Well, if only it was! Let’s give you an example.
- If the users have ‘liked’ a social media post about your product, it most likely means that they approve and are happy about it. It could also mean that they are potential buyers and interested to know more.
- If the users have posted a comment ‘not worth buying’, then it is obvious that they have some kind of dissatisfaction level. But, do you know the reason? Did they find some flaws with the technical or functional specifications of the product or they liked the product but there are some other issues like poor customer service?
- If the user has posted a comment ‘the product is so wonderful that you won’t buy it’, it gets trickier. On one hand, the term ‘wonderful’ has positive association as if the user is recommending the product to others. On the other hand, the whole sentence has a negative and sarcastic tone.
- If you are a blogger or an author who has received a comment ‘your piece of writing isn’t that bad’ or ‘useful information, but not great stuff’, then it could be difficult to comprehend what your readers are trying to tell you on the face of it.
It can take several hours every day sorting through all comments, tweets, reactions and other social media conversations to gauge customer sentiments. It is not only a laborious and cost-intensive job but also not give you effective and quick results.
Say hello to sentiment analysis. Since it is an automated, software-driven process, it can scan through huge volumes of online customer communication or blog mentions across all your social media and other digital channels within minutes. It can present you useful insights in real-time so that you can take the next course of preventive or corrective action promptly.
Types of Sentiment Analysis
You need to know about different types of sentiment analysis. This will enable you to determine the right one to suit your brand requirements.
- Emotion Detection
Emotion detection recognizes emotions in the text and segregates the words in positive or negative.
- Intent analysis
Intent analysis helps to understand the intent of the target audience – whether they will like the product, service or content.
- Aspect-Based Analysis
Aspect-based analysis is useful to get a deep insight into user behaviour to a product or service. In the case of content, it is useful to gauge reader behaviour.
- Fine-Grained Analysis
Fine-grained analysis gives feedback on positive or negative reactions to a product, service or content.
How Artificial Intelligence Can Simplify Sentiment Analysis
In a strange paradox, sentiment analysis (human behaviour) leverages AI (machine) to do its work. Sentiment analysis uses two subfields of AI – Natural Language Processing and Machine Learning (ML).
NLP uses linguistic algorithms to assign numerical values to positive, negative or neutral text. It converts emotions into datasets for analysis. It makes human language coherent for machines. Then, ML processes these datasets to identify patterns and trends over a period of time. This analysis acts as the base to predict customer behaviour or audience reaction.
In a nutshell, sentiment analysis AI helps to weed off unstructured, irrelevant and incomplete data to present in the analytical and sensible form to deduce accurate information. When you know what sentiments is the product, service or content likely to evoke in terms of data, it can act as a powerful enabler to improve the content quality, marketing strategy and user experience.
Use Cases of Sentiment Analysis in Marketing
Brands across the world are using sentiment analysis to their advantage in several ways:
- Brand Reputation Monitoring
Today’s audience has no inhibitions in expressing their opinions and tagging brands on social media. While this user behaviour may put your brand at risk in case of negative publicity, it is also useful to understand what people are saying about your brand. Sentiment analysis can monitor every single mention of your brand on all websites, blogs, forums, social media handles and digital touchpoints. You can do damage control to your brand reputation in real-time.
- Market and Competition Research
Sentiment analysis can help you to understand competition and market intelligence. It can answer some critical questions for you. Why customers are not buying my product? Has the demand for the product declined for all brands in the market? Is the competitor offering a better value for money? Is the competitor’s content more robust than yours? Based on the insights provided by sentiment analysis, you can improvise on your consumer engagement marketing strategy as well as identify new business opportunities.
- Prompt Customer Service
Sentiment analysis enables you to identify audience comments or queries that need priority attention. For example, if a customer mentions ‘pathetic product and service’ about your brand on social media, you can proactively respond to that customer to understand their pain points and offer timely resolutions. This type of customer feedback can also go a long way in improving the design and pricing of your products.
- Measure Campaign Effectiveness
One of the reasons why brands love digital marketing is that it is easy to measure the ROI on marketing campaigns. Sentiment analysis allows you to analyse every like, dislike, comment and mention of the campaign, and audience emotions behind them. You can easily tweak your content and messaging to increase the positive sentiments and thereby, boost the ROI.
- SEO Improvement
Sentiment analysis can be highly impactful in finding content ideas that are relevant to the audience. It can be useful in identifying the context of the keyword and the meaning behind it to relate with the audience the way you intend to. This will further help determine the keywords with positive sentiments, incorporate them in the content and improve page ranking to draw more web traffic. The website or blog that has content with positive sentiments reflects its good market reputation, trustworthiness and authenticity. It means that the audience loves to visit it frequently for the feel-good factor or to experience something that resonates with their feelings.
Another area where sentiment analysis is expected to bring a breakthrough is the interpretation of backlinks by search engines. Currently, backlinks are used solely to improve a website’s visibility and improve its SEO profile. There is no focus on whether the backlinked webpage has positive or negative sentiments. However, sentiment analysis can enable SEO teams to recognize negative sentiments in backlinks and reduce the SEO score.
Unfortunately, Google which owns 92.18% of the search engine market share doesn’t support sentiment analysis. Surprisingly, Google’s close competitor Bing does! However, Google does offer Cloud API product which has advanced models of NLP that can be used for sentiment analysis.
- Incorporate Emotions in Content
SEO can yield fruitful results only when content is good. Here, the definition of ‘good’ generally means that content should be useful, relevant, authentic and plagiarism-spelling-grammar error-free. But did you know that emotional connect is emerging as a key parameter in creating engaging content? Today’s audience expects the content to mirror their sentiments to be able to find it relatable.
A study of 65,000 articles on two news sites found that content that provided a powerful emotional experience is more likely to go viral. Emotional content brings higher engagement from the audience which in turn can increase conversions in terms of sales, followers, subscriptions, or whatever your content creation/marketing goal is.
Now, the question is how to build emotions or add empathy to your content. Would it suffice to include positive words or infuse elements of surprise, awe, joy, or humor in content? Would you be able to predict whether your content will trigger the right emotions? This sounds challenging, right?
Sentiment analysis AI is still in the early stage of evolution but it is gaining traction at a rapid pace. It would reap you great advantages in the long term if you start leveraging it from today!
About the author:
Sharmin Ali, Founder & CEO, Instoried