Predict and Improve CSAT Ratings Using ML Model with 82% Accuracy
Customers today expect a seamless and hassle-free interaction with their Digital Service Provider (DSP). A dissatisfied & frustrated customer quickly opts to switch. For DSPs, it’s of utmost importance to monitor customer satisfaction (CSAT) levels for every interaction they have with the customer. Among different touchpoints, Virtual Agents (VA) play a vital role in shaping the customer experience. According to Gartner, by 2020, 85% of customer interactions will be managed without a human. Thus, getting a CSAT response is important to retrain virtual agents and improve customer support service.
However, research shows that only 15-20% of customers respond to the CSAT survey after their interactions with the customer support service. A rule-based approach to get the remaining CSAT scores involves tedious and complex steps and results in a system that is neither scalable nor reusable. Thus, most innovative DSPs are trying to address this problem with the Machine Learning approach.
This insight details on how DSPs can build an ML model to predict CSAT scores using customer and agent chat utterances. Further, it also talks about how these predicted scores can be leveraged to bring continuous improvement in customer service operating procedures.
Fig: Key steps towards building ML Model for CSAT Prediction and Improvement
Building ML model, as mentioned in this insight, helps DSP in accurately predicting CSAT for the remaining 80-85% of the customer base (in terms of them being a promoter, neutral, or detractor). It also provides details in building key functionalities such as text summarizer, topic modeling, text readability scoring, and word cloud analysis to find insights on multiple fronts.
Mining this valuable information can help DSPs to retrain their Virtual Agents (VA), improving overall customer engagement, and reducing the churn rate.
- Sumit Thakur
- Prashanth Suresh Babu
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