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How do our Customers use Voice Sentiment Analysis?

For the long-term success of a brand, it is not only the immediate sales that count; the brand image also plays a major role

The main thing here is how people from the target group talk about the brand – regardless of whether they have already bought the product or not.

This can be tracked well on different channels and large amounts of information can be evaluated efficiently with AI solutions. Even when monitoring social media channels, you can easily relieve your own employees with the right technology and answer important questions:

Does my brand fit into the trend of the time? Are marketing activities noticed by the desired target group and what is the reaction to them? Is my product perceived positively and how is my service? Is the own offer even completely ignored? How is the brand received by influencers? These are just some of the questions that provide marketing with important facts.

With link|that Ecco, we go one step further and enable sentiment analysis on all voice channels in addition to the automatic analysis of texts. This makes it easier for service and also sales departments to evaluate how well their offer is being received and on which topics they can respond even better to the customer.

What is Voice Sentiment Analysis?

The French word “sentiment” stands for sensation or feeling. A sentiment analysis is therefore intended to find out how the target group perceives and assesses one’s own brand. This form of sentiment recognition is based on the automated evaluation of user comments, through which it is to be determined whether the statements of the target group are more positive or more negative. In voice sentiment analysis, we can also take into account the tone and speech frequency of the interlocutors. After all, it is not only what is said that is exciting, but how we pronounce things. This can steer the content to the opposite.

How do our customers use Voice Sentiment Analysis?

If we take as an example all the conversations of a call center that have been recorded for quality assurance, it is almost impossible to evaluate all the conversations without technical support. With our AI solution, we track these recordings virtually in real time and can flexibly view the results.

What is the mood of the call partners on each of my products? What differences are there? Do my team members manage to end even initially difficult conversations on a positive note? Are there particularly common statements that we can incorporate into our product development or marketing? These are classic questions that our customers analyze with this solution.
With well-planned analyses, it is also possible to identify satisfied customers and send them suitable advertising or personal bonus promotions.

Voice sentiment analysis is also used by our customers to find the right answers to product reviews in general and in personal conversations with individual customers. This gives them the opportunity to better contextualize individual people’s opinions in relation to their general attitude towards a product. After all, responses are often read or retold by multiple people and the right “tone” is all the more important in personal responses.

What are the advantages of voice sentiment analysis over text analysis?

Simple analysis methods search the text for words that have been defined in advance, but natural language does not consist of positive and negative lists. While this method gives a rough overview of communicated content, it is not optimal for capturing actual sentiment. Another approach is to evaluate the frequency of individual terms. However, this also only leads to a meaningful result in very special cases.

For a successful sentiment analysis, it is advisable to draw on the possibilities of artificial intelligence and access any information you have available. Especially when it is not a matter of pure texts from social media channels or the evaluation of questionnaires, sentiments can be recognized and mapped much better this way. As an example, a single statement from a natural conversation: “Well great!”. A classic statistical evaluation will positively note the word “super” and can thus generate a result that does not correspond to reality. Especially this phrase gives a completely different meaning in context and also in the way it is pronounced. From “perfect, that’s much more than I expected” to “that doesn’t do anything for me”, any interpretation is conceivable. Not every company has the possibility to offer the customer a service on the phone. This makes it all the more important to evaluate any insights for improving your own offering when communicating directly with your own customers, and not to miss the details that make the known difference. AI-powered voice sentiment analysis has the advantage here of also being able to take into account changes in tone of voice and speech rate. This results in a greater number of recognizable sentiments as well as greater certainty of correctly classifying the statements.

If voice sentiment analysis is also exciting for your team and you have further questions on the topic, we would be happy to help you.

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