How consumers feel about a brand can’t be discovered in a boardroom but online. Finding social media posts and reviews that mention a brand or product is beneficial, and the most efficient way to collect data is through a sentiment analysis tool. Getting to know customers through sentiment analysis provides clues on what direction to take brand development.
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What Is Sentiment Analysis?
Sentiment analysis in our case, refers to how consumers feel about a company or brand. These feelings can be complex and change over time; consumer insights always evolve. This requires an ongoing strategy to collect consumer data from social media posts, reviews, and other forms of user-generated content.
Sentiment analysis involves using AI tools to interpret the emotions and attitudes in a passage. Texts are rated on a negative to very positive scale and shades in between. Advanced sentiment analysis tools can pick up on subtleties of tone and may even be attuned to nuance or irony.
Data needs a good marketing campaign. The raw materials are on web pages that provide text that reflects consumer sentiment. These include the kind of content most of us produce every day, such as social media posts, online comments on forums, and reviews.
To discover what people are saying about your brand, it is too time-consuming to do endless searches and try to keep track of various platforms. Using tools that create notifications when a brand or product is mentioned or a competitor is discussed makes the job easier.
Once the information is found, retrieving and storing it is the next phase. Using a web scraper that collects and stores texts eliminates laborious cutting and pasting. A web scraper can be created with some coding knowledge and software or there are tools ready to use for this job. Along with a web scraper using a web proxy will disguise the user’s IP address and avoid being blocked while obtaining information.
There is nothing wrong with obtaining information such as reviews with open access to everyone. However many websites do not like the idea of competitors doing research and may block an IP.
In addition, a large number of actions at one time, as often are required by web scraping can cause an IP to be blocked automatically. A web scraper bypasses these restrictions by hiding an IP address. Once the data is safely recovered, it can be stored in a database.
After gathering the data, it should be cleaned and prepared for analysis. Cleaning data refers to removing extra words and punctuation that may interfere with sentiment analysis. Tools can be used to clean up a text before it is evaluated for mood and tone.
Extraneous letters and symbols should be removed from the text. This includes punctuations, initials, stray letters indicating degrees or titles, and abbreviations. However, some punctuation marks that express emotion, such as exclamation and question marks, can be left in. Some prepositions can be deleted as well as capitalizations and any extra words in the text.
Opinion mining involves using data analysis tools that employ Natural Language Processing or NLP to interpret the emotional coloring of text. Some sentiment analysis tools rate texts according to a scale ranging from very negative to very positive, whereas others assign specific emotions to texts. Machine algorithms can learn more nuanced communication such as sarcasm or humor from a sample passage.
Opinion mining can reveal preferences customers have for products that may be unexpected. Discovering customer priorities is a key benefit of sentiment analysis.
Understanding the Results
After following the processes of gathering data, cleaning it, and opinion mining with sentiment analysis, what is the next step for translating the conclusions into action? The results of data analysis should provide concrete information about customer’s preferences. For instance, customers may be enthusiastic about a certain color or widely dislike an ad campaign. What to do here is clear, but what if the results aren’t so clear-cut?
It may not be that all of the marketing questions will be answered first. For instance, 54% of customers may love raspberry toothpaste and 46% hate it. Perhaps it may be comforting to know there is a majority that likes the main product, but given the high numbers on the other side, it may be time to develop another flavor in addition to raspberry.
The next stage of sentiment analysis could be reaching out to customers to discover what unusual toothpaste flavors they would like to see. Orange? Lemon? Pineapple? Or maybe they would like to suggest a flavor? The important thing is that the conversation with customers has started.
Know Your Brand and Customers
Getting to know customers is the best way to see a brand through fresh eyes. Data sheds light on brand reputation and provides insight into customer preferences and behaviors. Data gathering, cleaning, and opinion mining produces results that can be translated into action and inspire a new direction in brand development.