SMS Sentiment is the overall attitude expressed to the customer in the various channels.
In the context of an SMS/Email sent for Collections purposes it important that the correct sentiments are expressed to drive debtors to the desired action. It has been proven via A-B test that there is certainly an interactive effect between the sentiments expressed in text message and driving debtors to specific action.
Furthermore, as debtors/clients are composed of individuals with the own personality and preference it is important to realize that specific groups of individuals respond better to particular sentiments.
In the current environment there are significant amounts of data collected, which as mere humans, is impossible to analyse manually without any sort of error or bias. Thus, the requirement to develop and deploy a machine learning classification model to ingest data, determine relationships within the data and output the predicted optimal sentiment.
SMS Sentiment intelligence allows for improved debtor engagement and collections performance.
We are always looking for new ways to improve the collection process. To do this it is important to dive deeper into the conversations between agents and debtors to understand and analyse the huge amounts of incoming and historic data relevant to the agent-debtor dynamic.
How Traq’s SMS Sentiment intelligence can help you
Sentiment analysis is a procedure that enables users to comprehend human emotions and thoughts in all kinds of data. Historic data is analysed in text, voice, and video dialogues. Our AI uses this information to advise you regarding what approach would be most effective for each customer. Do they need just a gentle reminder or something slightly sterner to motivate them to make an immediate payment?
More about Sentiment analysis
You can gain insight into the wants and expectations of your customers by using sentiment analysis to better understand their voices. You can categorise customer feedback data from one or more sources into positive, neutral, or negative customer attitudes using sentiment analysis. By determining who is unsatisfied, and what needs to be addressed to remedy this. Accurate sentiment analysis can help you better satisfy the demands of your expanding customer base.
Things like voice segmentation level, which allows operators to detect a trend of changes in the sentiment during the flow of the conversation, can be targeted using sentiment analysis. NLP is how AI performs sentiment analysis on the interactions between agents and debtors.
Natural Language Processing (NLP): What is it?
An area of computer science called natural language processing (NLP) helps provide artificial intelligence (AI) with the means to comprehend the intent or meaning behind particular phrases. We are better able to interpret client data thanks to it.
Utilising Speech Analytics Technology to Enhance the Collection Process
Speech analytics technology is being used more and more to determine what works and what doesn’t during collection calls. Speech analytics software enables you to precisely determine how to modify training and best practices by automatically finding, categorising, and scoring every agent discussion.
Scoring is an effective solution for managers to track performance and reduce risk provided by speech analytics technology, you can make use of scoring both for the call and the agent. The AI automatically generates a score after every conversation, which has been analysed and catalogued by the speech analytics programme. Managers can drill down into the indicators that matter most to them by configuring searches and categories.
By utilising speech analytics, you get insightful information about the needs and preferences of your debtors, providing you with the means to enhance your billing procedures and payment schedules. Finding patterns of irritation in these situations may be especially beneficial for troubleshooting.
By utilising speech analytics, you gain a potent tool to reduce risk, increase debt collection, enhance customer satisfaction, and revolutionise the collection process for the future. AI analysis tools will play a significant role in the greater omnichannel customer experience as a result of their increased ability to help simplify complex billing and collections procedures.