Predict Net Promoter Scores and identify whether your customer is potentially a promoter, neutral, or detractor. Take corrective actions timely to improve customer service.
Customers today expect a seamless and hassle-free interaction with their service providers. A dissatisfied and frustrated customer will quickly opt to switch. Thus, for the service provider, it becomes very crucial to understand the customer experience and promptly take corrective measures if it lags. One key metric to understand this is using the Net Promoter Score (NPS). It provides customer loyalty and satisfaction measurement by asking customers how likely they are to recommend your product or service to others on a scale of 0-10.
To capture NPS, service providers share the survey forms with their customers. But do customers respond to such surveys? Research shows that only 15-20% of customers respond to the NPS survey after their interactions with customer support. Does it mean the service provider should not take any action for the remaining 80-85%, assuming they would have a good experience? There is a high possibility that a customer not satisfied with the service would have already decided to opt out without taking any effort to respond to the survey.
Most innovative service providers are trying to address this problem with a machine learning (ML) approach.
Fig: Key steps towards building ML Model for CSAT Prediction and Improvement
NPS provides customer loyalty and satisfaction measurement by asking customers how likely they are to recommend your product or service to others
- Sumit Thakur
- Prashanth Suresh Babu