Closely monitoring customer sentiment helps in understanding the customers’ needs. This article discusses how Twitter sentiment analysis helps social media marketers.
This era is about consumers. The habits and behaviors of consumers are closely watched. Social media has brought about a digital transformation in this regard. It is easier to gauge the sentiments of customers on social media. It helps to devise marketing strategies accordingly. This is specifically the reason marketers use social media listening strategies.
Closely monitoring customer sentiment helps in understanding the needs of customers. It helps marketers understand the motivating factors for customers. These are essentially sentiments. Incorporating into a product marketing scheme aligns with customers’ psychological needs. They invariably purchase such products and services.
Sentiment analysis is a data point that helps assess the positive or negative response to a product or service. All types of customer feelings and sentiments towards a product help in making changes to a product or service. It helps companies make necessary changes to improve their customer experience marketing strategies.
What is sentiment analysis?
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When people’s opinions are identified, processed, and scored, it becomes data. People’s opinions may be informed, uninformed, pleasant, unpleasant, sporadic, thoughtful, thoughtless, diplomatic, unrehearsed, etc. On a social media platform such as Twitter, tweets are generally informal. But the level of informality differs. On Twitter handles of famous celebrities, government organizations, or law enforcement agencies, the tweets might be formal.
But on Twitter accounts of the general population, the tweets might be highly personal opinions. Sentiment analysis is the accumulation of such textual content, its analysis, interpretation, and derivation of insights. Text analytics is a major part of sentiment analysis. Text analysis of social media posts, customer service tickets, survey answers, and live chat logs provide data for sentiment analysis.
7 top reasons why sentiment analysis is used by social media marketers?
Social media marketers use sentiment analysis due to the below-mentioned top 7 reasons:
- Audience analysis
Most marketers make the mistake of focusing on compliments and not on criticism. They must be ready to take the bouquets as much as the brickbats. One way to check what their audience is talking about their product is through Twitter sentiment analysis. For example, a film production company can better understand its audience from tweets on its released film. This way, in the next installment of the film, if there is one, the film can factor in these comments. Sentiment in a movie, especially when a majority of people feel the same way, is nothing but the truth. Even if the filmmakers feel otherwise, the general population’s sentiment analysis should be considered.
- Actionable data
Twitter feeds and tweets are powerful and rich sources of actionable data. Instead of sending customer surveys and emails, analyzing Twitter feeds is a better way to understand the sentiments of people. Social media platforms such as Twitter add more users by the hour, so marketers can expect to keep getting tweets and reviews on a product or service. Twitter comments can come from people across industries, professions, and walks of life. It makes for a versatile database of comments. Marketers can use these tweets as data to make their products specific to their audience types or generalized to cater to all customer segment groups.
- Direct customer engagement
Social media marketers need not go to the doorsteps of the customers or audience to get feedback. They can engage with their customers wherever they are – via a rich engagement platform such as Twitter. Negative feedback from a customer can be publicly responded to. It adds to the transparency and trustworthiness of the product and product makers. A positive comment about a product and service is available for everyone to see. It also promotes the product directly from the customer’s point of view. The customer does this voluntarily. There is no requirement for a company to spend any extra money on promoting its product, as positive tweets do all of this more robustly and convincingly.
- Stepping up customer service
Customer engagement and customer service are two different aspects of dealing with customers. Customer service deals with the maintenance of products and services of customers. It deals with eliciting feedback and working on them. Product upgrades, enhancements, and modifications are part of customer service. Tweets from customers are a source of information for customer service. It is easy for customer service personnel to fix issues as and when customers tweet. Companies can use the highly available infrastructure of their Twitter accounts for this. They do not need to have their own software systems for this. They can simply interact via their Twitter accounts with customers. They can even resolve any large impacting issues and communicate the same over their Twitter accounts.
- Upselling opportunities
When customers are happy, they are happy to receive updates on a product upgrade or a new service. Sometimes, customers might convey certain sentiments about their keenness to use a particular feature. They might post it on Twitter. These sentiments can be collated, and these very customers are approached for upselling any related product. This way, marketing is not intrusive – it only reaches interested people. Also, the marketing spend is less – as only potential and most promising customers are approached. Using a sentiment analysis tool, it is possible to tap into such customers. Without a sentiment analysis tool, the marketing effort is long-winded.
- Chatbot training
Sentiment analysis of Twitter feeds provide vital data to train chatbots. Without such qualitative data, the chatbot can only use batched data, reports, or historical data. Twitter data is generated dynamically. Chatbots can be trained to interpret customers’ sentiments as and when it happens. Artificial Intelligence and machine learning technologies can build specific data models from the data. The data modeling can be tweaked dynamically when sentiment analysis data is fed on the fly. This is a powerful way to train a chatbot that receives actual real-world unaltered data. For a start, companies can use a simple data model and then progress to more comprehensive ones.
- Cut down on customer churn
Sentiment analysis can help companies retain and regain customers. Dissatisfied customers can be identified early in the game. Satisfied customers can be identified and further nourished. Social media marketers can use the wisdom of people on Twitter to identify a product’s or service’s problems or potential. They can then tweak their marketing messages accordingly. It isn’t easy to sell to customers without understanding their inner feelings and aspirations. That is why Twitter sentiment analysis creates more opportunities for sales, promotions, upselling, and customer experience.
What are the aspects of a good Twitter sentiment analysis system?
Although sentimental analysis for social media marketing on Twitter can be done in a rudimentary fashion, it is best to use automation. Using a sentiment analysis tool powered by Artificial Intelligence and machine learning is the best way forward.
- The tool should have a self-explanatory interface
The interface must be intuitive and self-explanatory. Technical and non-technical users must be able to use it. The user interface should be based on drag-and-drop declarative functionality. The user should be able to share and collaborate with others using the interface. The interface should handle all aspects of the sentiment analysis, from start to finish.
- Support for various data types
A sentiment analysis tool should be able to process multiple types of structured and unstructured data. At the foundational level, the tool should be able to process textual content. But the tool should also be able to process PDFs and images. The underlying AI and ML engine should be able to learn from this data and modify its data models accordingly.
- Trainable
The tool, powered by AI and ML, should be trainable. All types of users must be able to train the tool. The training model and interfaces must be easy to understand for both the human user and the tool. There should not be any cap on the amount of training that the tool can handle. The tool must be infinitely trainable. The end goal is to provide insights based on data and using some of the sophisticated discretion of humans.
- Connectable
A sentimental analysis tool for social media marketers on Twitter must be integrateable. It should support inbound and outbound data. The tool must be able to support a wide number of integrations. Other systems must be able to easily send their data to the tool and receive data too. The tool’s integration capabilities must be based on open standards and technologies. The tool must provide an API toolkit for developers to create integrations.
- Testable data
The sentiment analysis data must be measurable. The opportunity to measure the performance of data can be part of the tool. It can be outside of the tool as well, but the data should be actionable. The data can be in the form of XML, charts, CSV, etc.
Final Thoughts
Twitter is a powerful source of data for social media marketers. Sentiments analysis of this data provides rich insights. The way the sentiment analysis of Twitter tweets is done matters. The best way to do it is through a good sentiment analysis tool for Twitter.
