Every business wants to know how its customers and prospects feel about its brand. This is where sentiment analysis comes in, as a valuable tool that decodes individual attitudes and feelings towards a concept.
It’s also known as Opinion Mining and has derived from Neuro-Linguistic Programming (NLP) approaches. NLP uses written language to understand human emotion, but sentiment analysis takes it a step further and works out the feelings, opinions and attitudes of the person from that text. It can be used for any type of written communication, from a blog post to a tweet.
The analysis is carried out using complex software which uses algorithms to map emotions and then assesses the strength of those emotions. It was originally developed for use in research when online reviews were becoming popular and businesses were keen to mine what their customers were actually saying and to put those reviews into a broader market research context.
Today’s solutions use a variety of techniques and tools, such as text processing and keyword tools, social media monitoring, computational linguistics, human assessment and a variety of NLP theories.
Ultimately, it is able to gather actionable user insights which businesses can apply to their marketing efforts. It is particularly useful for those firms which are looking to create better customer experiences and user journeys. Here are some examples as to where it can add value:
– By providing rich data
Online customers leave behind a powerful footprint of data in the form of comments, emails, messages, reviews, tags, social media posts and more. The sentiment analysis methodology is applied to this to assess shared emotions within customer groups. This provides insight for strategy and to help businesses understand where customer journeys can be adapted and improved.
– By assessing campaign success
The approach can review campaigns and see how target audiences respond to them. By evaluating customer reactions, marketers can see whether campaigns need any adjustment or whether they need rethinking entirely to hit the mark.
– For trend analysis
The technology can also help to predict trends as they emerge, identifying changing customer expectations and determining how feelings are changing around the brand in order to respond with the right marcomms activity.
– Customer satisfaction measurement
Customer opinions can be affected by an array of factors which include service, brand engagement, price changes and product launches. With sentiment analysis, companies can see where customers are dissatisfied and evaluate the extent of the problem.
In short, sentiment analysis is a growing field which helps businesses to learn how their customers feel about their business and their brand, using a wealth of online digital footprint data. Although it is not yet able to pick up subtleties such as sarcasm, the technology is already able to provide valuable insights which help companies to better predicate their marketing activity based upon rich data.
For most brands, sentiment analysis will almost certainly provide value and help to support better decision making in the longer-term.
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