A large UAE Insurance provider experiences a large volume of calls daily in its call centre.

The client was looking for opportunities where the caller is a potential upsell or cross-sell candidate. The idea is to pitch the right product at the right time to the right customer and check whether the agent is approaching that appropriately in terms of their dialogue, their persuasive selling ability and how they overcome objections. Large volumes of calls make data analysis through manual sampling and listening virtually impossible.

1. Using speech analytics language model and machine learning. Use it to extract insurance critical fields from a conversation transcript. Critical fields like, product name, model number, call type, location, area code, design type, or any other important entities specific to a business. Industries can achieve their revenue goals on the basis of speech analytics and the value it provides in product cross-sell and up-sell sales opportunities.

2. Using voice analytics to focus on the content of customer conversations (how it was said) to derive the context, tonality of conversation pertaining to product or services.

3. Using sentiment analytics to focus on the current disposition of the customer to derive the happiness factor of the customer (positive, neutral or negative). It considers any interaction, demographic, or engagement life cycle information about the issues that matters most to the business.

4. Using customer segmentation to group customers based on extracted demographics data, buying behavior or some other patterns which are further used in marketing. 

Using all above insights to identify, which similar and related products can be cross-selled as per the customer segmentation.