Increase customer revenue by managing customer sentiment through the contact centre

Apart from the value prop of the card, one of the biggest drivers of customer satisfaction and loyalty is interaction on the contact center. On average 20-30% of calls result in negative sentiment. Most of such calls would be related to Fraud, Dispute, and Billing.

Previously agents would manually note such instances and a handful would be addressed, if at all. Also, agents would only have time to note a few words, and the context is lost.

These negative sentiments, if left unaddressed, can result in customer attrition and a decline in brand reputation. It also diminishes the chances of acquiring new customers through referrals. This scenario not only reduces revenue but also increases costs, as dissatisfied customers are more likely to contact support frequently.

With the advent of new technology, it has now become possible to measure sentiment at customer interaction levels. This reduces any bias or error from the agent’s side in noting down the interaction and the organization can get the context to act upon and improve in the future.

To effectively detect customer sentiments, organizations need to regularly analyze calls, chats, and email interactions. They should employ strategies to transform negative sentiments into neutral or positive ones over time. This can be accomplished using advanced analytics and AI/ML-driven solutions such as this.

This solution features an advanced sentiment analysis system that classifies each conversation as positive or negative based on text transcripts.

It can also be enhanced to capture the sentiment’s intensity, ranging from strong positive to strong negative. By integrating this solution with conversation topics (either pre-existing within the organization or custom-built), it can identify topics that consistently evoke positive or negative sentiments.

This helps the organization to plan and execute a strategy around it and control it through agent training, value prop enhancement, or technology integration to serve better.

This solution is designed for organizations that:

1. Have progressed significantly in their customer service journeys and have text recordings of customer interactions.
2. Aim to advance their customer service and brand image management.
3. Seek to invest in data mining and employ cutting-edge techniques to improve the customer experience.

Reducing complaints can lead to several benefits:

Increased revenue: Customers tend to disengage when they exhibit negative sentiment, which is a key indicator of potential attrition. By reducing complaints, organizations can retain customers and increase revenue.

Improved operational efficiency: Negative sentiment often reveals underlying issues with products and customer communication. Resolving these problems can lower service costs and enhance operational efficiency.

Agent training: Sentiment analysis can be used to evaluate agent performance and conversation quality. Training agents with high negative sentiment interactions can improve overall customer experience.

NPS & loyalty: Customers Favor organizations that help them maintain a positive experience, as measured through sentiment. This enhances the brand image and boosts customer loyalty.

The model incorporates the following characteristics:

1. Data from text transcripts
2. Chat data
3. Email conversation data
4. Conversation topic data (if available)

Lexicon-based methods are used to classify words.

To classify conversations, deep learning methods or support vector machines are applied.

N-gram graphs are used to visualize the results for business insights and to validate the findings.

1. On-prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. The final outputs may consist of tagging calls and interactions at the customer level with sentiment scores.
2. Batch files, including Excel files or flat files.
3. Outputs can be delivered via API requests. Send an API request with inputs and receive a response. This can be integrated into an existing application or a separate web application can be developed.