"At Raiffeisenbank, we have long been interested in the voice of the customer, we try to obtain feedback in various ways and use it to improve our services but also to motivate our employees. This approach provides us with extensive data from clients, their comments, remarks, and ratings. And because we don't want any opinions to get bogged down, we’ve deployed SentiSquare AI on top of that data to help us process it. SentiSquare AI can structure the unstructured data into ready-made categories so that our employees, CX experts, and managers can see what resonates with our clients. It can also catch sentiment from comments or clients who require our help and need to be contacted immediately to resolve their issue. We also use the system for cases where clients are reacting irritably or it’s clear from their response that it wouldn’t be appropriate to contact them further, and we also adjust our client outreach policy as a result. Deploying SentiSquare AI has saved us a lot of capacity and time when addressing some needs manually or through simpler mechanisms. It has also increased the interest in client feedback outputs, as categorizations and structuring have made it more digestible and clearer for the end recipients. Moreover, everything is done in a purely automated way, so there’s no need to worry about the whole mechanism in any dramatic way.”
Raiffeisenbank faced the mammoth task of handling 10,000 SMS messages monthly, each containing vital feedback from their valued clients. The challenge was to effectively analyze and derive actionable insights from this treasure trove of data. The Need for Analysis and Automation: Understanding the critical need for analysis and automation, Raiffeisenbank sought a solution to streamline their SMS feedback processing workflow, ensuring accurate insights and swift responses to customer concerns. Enter SentiSquare AI – an analytical tool for feedback analytics.
Deploying SentiSquare AI: A tailor-made categorization model that automatically categorizes feedback into business categories. The categories, sentiment and topics are determined. Integration on the OpenOne solution. Customers make mistakes in the numerical scores in the feedback (confusing 1 and 10 on the scale, etc.). AI detects and corrects these errors. This allows managers to work with more accurate and correct scores. Detection of "inappropriate language", detection of customer contact ban (the goal is to not send an SMS to an angry customer).
Results with SentiSquare:
Deployed a custom AI model that autonomously categorizes feedback into business-relevant categories, unraveling the nuances within client sentiments and topics.
Seamlessly integrated SentiSquare AI into the OpenOne solution.
Recognizing that customers might make errors in numerical scores, our AI swiftly detects and corrects these discrepancies. This ensures CX managers work with precise and accurate feedback scores.
Implemented advanced features to identify inappropriate language and detect customers who prefer not to be contacted via SMS.
