Overview

Understanding the key drivers of customer interactions and gaining valuable insights into complex business inquiries can be a challenging task. With the help of AI contact driver analysis, it is possible to analyze 100% of customer-agent conversations to identify the primary reasons why customers reach out to your brand. This approach can uncover opportunities to streamline your customer service and eliminate unnecessary contacts.

By leveraging AI contact drivers, you can gain visibility into customer interactions and identify patterns that may have previously gone unnoticed. This data can provide valuable insights into the needs and pain points of your customers, allowing you to better understand how to improve their experience with your brand. By highlighting the biggest opportunities for reducing contact, you can optimize your customer service strategy and allocate resources where they will be most effective.

Contact driver analysis is an omnichannel capability that allows brands to identify and analyze the key reasons or drivers behind customer contacts across various communication channels.

Methods for Creating Contact Drivers in Sprinklr

​There are two main methods for creating contact drivers in Sprinklr:

1. Conversational AI Intent Models: The conversational AI intent model identifies contact drivers by detecting them within each message and then consolidating them into a case. To delve deeper into this approach, you can click here.

2. Contact Driver Models: These models are specifically designed for analytics purposes and aim to identify the primary contact driver of a case by analyzing the context of the entire conversation. They consider both customer and agent messages to determine the contact drivers of a case. For further insights into this method, click here.

These methods offer distinct approaches to identifying and analyzing contact drivers within Sprinklr, catering to different use cases and requirements.