After-call work provides a real-world example of how generative AI can have a huge impact on the efficiency and performance of your contact center agents. This includes removing manual tasks and freeing up time for agents to focus on more high-value work, such as engaging directly with customers.

How AI is Changing the Game in the Contact Center

Below are some examples of the benefits that generative AI can bring to your contact center, including:

• Eliminating manual after-call work by 75% or more by automatically generating call notes, dispositions, and follow-ups, and automating CRM system updates.
• Ensuring highly accurate data capture of customer interactions and what happens next – no human error!
• Improving agent engagement by removing manual tasks
• Making huge agent productivity gains → 18.75%

See the example below on how a 200-person call center can handle nearly 3,000 more calls per shift by eliminating after-call work:

• Each agent has 6-minute AHT per call, including ACW
• Agent performs 1.5 minutes of ACW per call
• Agent performs 108 minutes of ACW per day
• 75% reduction in ACW lowers to 27 minutes per day
• Agent gains 81 extra minutes per shift
• 18.75% increase in agent productivity
• 200-person call center       
• Productivity gain: 2,700 more calls per shift

4 Ways to Improve Contact Centers with AI

1. Agent productivity. Free up your agents by removing manual after-call tasks, such as creating call dispositions, updating CRM systems, and carrying out follows ups.

2. Consistency and accuracy. Capture a highly accurate transcript, call disposition, and record of every customer interaction with a high level of accuracy compared to manual inputs by agents, including key outcomes, questions & answers, and follow-ups.

3. Sentiment analysis. Detect emotional signals, such as frustration, excitement, and satisfaction, or other factors, such as politeness and call closing, to instantly identify quality management and improvement opportunities and agent training needs. 

4. Self-service. Analyze countless hours of human-to-human interactions using AI-powered conversation understanding to identify frequently asked questions and emerging issues. These insights can then be used to inform knowledge bases and self-service channels like chatbots to more quickly identify recurring customer questions and provide more precise answers while avoiding the need to engage agents.  

Drive better efficiency and accuracy after the call

Generative AI opens up new opportunities to optimize the performance of your contact center agents, freeing up their time to focus on customer engagement and delivering the best experiences possible. Drive improved agent productivity, better accuracy, and increased customer satisfaction in your contact center today!

Watch the complete webinar below, and contact us today to learn more and get a demo.

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What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is the process of analyzing and identifying the emotions and attitudes of customers towards a product, service, or brand. This analysis is performed using natural language processing (NLP) and generative AI techniques that help machines understand human language and its nuances. Sentiment analysis is widely used by businesses to gain insights into customer service and sales interactions, as well as for customer feedback.

Improve Engagement and Outcomes

1. Understanding Customer Emotions

Sentiment analysis can help you understand the emotions of your customers during conversations. By analyzing the tone, volume and language used by customers, you can identify whether the customer is happy, frustrated, or angry. You can deliver this insight to your contact center agents and salespeople in real-time to tailor your response and tone to better match the customer’s emotions, resulting in a more positive experience.

2. Improving Response Times

Sentiment analysis can also help you improve your response times for critical interactions. By analyzing the sentiment of customer conversations, sentiment analysis tools can identify urgent or critical issues that require immediate attention. This insight can help you prioritize your responses and ensure that you are addressing the most important issues first, such as a product defect.

3. Personalizing Customer Experience

Sentiment analysis can help you personalize the customer experience by understanding the emotions and preferences of your customers, especially during contact center interactions. By analyzing the sentiment of customer service calls, today’s tools can identify the customer’s interests, preferences, and pain points. This insight can help you tailor responses and recommendations to better match the customer’s needs, resulting in a more personalized experience.

4. Improving Customer Retention

Sentiment analysis can help you improve customer retention by identifying and addressing the underlying causes of customer dissatisfaction. By analyzing the sentiments of customer messages, businesses can identify common issues and trends that are causing customers to leave. This insight can help you take proactive measures to improve customer satisfaction and loyalty.

5. Improving Sales Results

By analyzing the sentiment of sales outreach and conversations, sales leaders can gain deep insights into customer preferences, pain points, and emotions, as well as the performance of their salespeople. This can help sales teams tailor their approach to each customer, building stronger relationships and increasing the likelihood of closing deals. Sentiment analysis can also help sales leaders identify patterns in customer sentiment over time, allowing them to make data-driven decisions about sales strategy and tactics. Ultimately, sentiment analysis can help sales teams create more effective and personalized conversations, leading to increased customer satisfaction and improved sales results.

How Symbl.ai Sentiment Analysis Works

Symbl.ai’s uses natural language processing (NLP) techniques to analyze text and identify the emotions and attitudes expressed by customers. Symbl.ai’s sentiment analysis model is designed to handle the complexities of human language and can accurately identify the sentiment of human conversations with high accuracy.

The model is based on a deep learning architecture that uses a combination of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to process text. The model is trained on a large corpus of text data, which allows it to identify patterns and correlations between language and sentiment.

Once the sentiment analysis model has analyzed the text, it generates a sentiment score for each sentence or phrase. The sentiment score is a number between 0 and 1, where 0 represents a negative sentiment and 1 represents a positive sentiment. Symbl.ai’s sentiment analysis also provides a confidence score for each sentiment score, which indicates the level of confidence that the model has in its prediction.

In the contact center interaction example below, Symbl.ai enables real-time sentiment during the call to assist the agent in managing the conversation – in this case checking delivery status and cancelling an order. Then the overall sentiment analysis is delivered along with additional interaction intelligence post call, including a summary, key topics, questions, action items and follow ups.

Symbl.ai provides a powerful tool for businesses that want to gain insights into customer emotions and attitudes during sales and customer service conversations. By leveraging the latest NLP and deep learning techniques, Symbl.ai can help businesses create more personalized and engaging customer experiences that help increase sales and drive customer loyalty and retention.

Conclusion

Sentiment analysis is a rapidly evolving field, and Symbl.ai is hard at work developing breakthrough solutions that can help businesses leverage its power for sales and customer service conversations. With Symbl.ai’s advanced sentiment analysis capabilities, you can gain deeper insights into customer emotions and attitudes, and use these insights to create more personalized and engaging conversations to achieve better outcomes – in real-time and over time.

So why wait? Contact us today to learn how we can help you unlock the full potential of sentiment analysis for your business.

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1. AI-generated sales content to support entire sales cycle

The sales cycle often involves a number of conversations and substantial amounts of content generation, from email templates and sales scripts to marketing collateral and case studies to personalized offers and services. Creating this content can be time-consuming and resource-intensive, especially when attempting to tailor it to specific clients or industries.

Generative AI can streamline this process by creating personalized, targeted content quickly and efficiently, and identifying the right moments to use it for better sales engagement and outcomes. By inputting a few key parameters or prompts, sales teams can use LLMs to generate a wide array of content across the sales cycle, including:

• Customized email templates that address clients’ unique pain points and interests.
• Sales scripts that weave in relevant industry trends and challenges – or personalized content.
• Marketing collateral that showcases the specific benefits of your product or service for a given client.
• Case studies that demonstrate how your solution has helped similar organizations overcome obstacles.

By using AI-generated conversation intelligence and content, sales teams can save time, reduce the burden on content creators, and focus on building closer relationships with clients and prospects.

2. Building personalized sales coaching plans based on ongoing conversations

Sales coaching is an essential aspect of sales team development and success, as it helps to identify areas for improvement and fine-tune sales strategies. Generative AI can play a significant role in developing personalized coaching plans by analyzing ongoing conversations between sales reps and clients or prospects.

By using LLMs to process the text or audio data from these conversations, organizations can gain valuable insights into:

• Communication styles and techniques that resonate with clients.
• Common objections and concerns that arise during the sales process.
• The most effective ways to address these objections and concerns.

Armed with this information, sales managers can create coaching plans tailored to each rep’s strengths and weaknesses, thereby improving overall sales performance.

3. Onboarding, real-time support, and continued training to make your sales team your product experts

A well-rounded sales team needs more than just technical product knowledge; they must also be skilled in presenting the product, handling objections, and building relationships. Generative AI can help organizations create comprehensive onboarding and training programs that empower sales reps to become true product evangelists. This includes:

• Onboarding training: Generative AI can be used to create customized training materials that cover the product, industry, and target markets. By using LLMs to generate training content based on the rep’s role, experience, and learning preferences, organizations can ensure that their sales teams hit the ground running.
• Real-time support: Sales reps often face unexpected questions or objections during client interactions. Generative AI can provide real-time support by quickly generating accurate, persuasive responses based on the context of the conversation. This ensures that sales reps are equipped to handle any situation that arises and can maintain momentum in the sales process.
• Continued product training: As your product evolves, it’s essential to keep your sales team up-to-date on new features and use cases. Generative AI can help facilitate this ongoing training by generating relevant, easy-to-understand content that keeps your team informed and engaged.

Choosing Between Using Existing LLMs and Building Your Own

Deciding whether to use an existing large language model or create an internal LLM strategy depends on factors such as resources, time to market, and the level of customization and control required. Some criteria to consider include:

• Cost: Building your own LLM can be expensive, both in terms of the computational resources required and the expertise needed to develop, train, and maintain the model. Using an existing LLM can be more cost-effective, especially for smaller organizations or those with limited AI expertise.
• Time to market: Developing and fine-tuning a custom LLM can be a lengthy process. If speed is a priority, utilizing an existing LLM may be the best option, as it allows you to quickly integrate the technology into your sales processes and start reaping the benefits.
• Customization: While existing LLMs are highly versatile and can be fine-tuned to some extent, building your own LLM allows for greater customization to meet your organization’s specific needs. If your sales processes or product offerings are highly specialized, a custom LLM may provide better results.
• Control: Building your own LLM affords you more control over the data used for training, which can be important for organizations operating in highly regulated industries or those with strict data privacy requirements. Using an existing LLM may require sharing your data with third-party providers, which could pose risks.
• Scalability: As your organization grows and your sales processes evolve, you may require an LLM that can scale accordingly. While existing LLMs can handle a wide range of tasks, building your own LLM may offer greater scalability and the ability to adapt to your organization’s changing needs.
• Integration: Utilizing an existing LLM may require less integration effort, as many providers offer APIs and other tools that allow for seamless integration with your existing sales and CRM systems. Building your own LLM may necessitate more extensive integration work, which could impact the overall cost and time to market.
• Support and maintenance: Maintaining and updating an LLM is an ongoing task. If you build your own LLM, your organization will need to allocate resources for this purpose. Using an existing LLM typically comes with support and maintenance from the provider, which can help ensure the model remains effective and up-to-date.

Ultimately, the decision to use an existing LLM or build your own will depend on your organization’s specific needs, resources, and goals. By carefully considering the factors outlined above, you can make an informed choice that sets your sales team up for success.

Conclusion

Generative AI has the potential to revolutionize the world of sales by providing valuable support throughout the sales cycle, generating sales intelligence and content, fostering personalized coaching plans, and enabling comprehensive onboarding and training programs. Whether you choose to use an existing LLM or build your own custom model, the benefits of leveraging AI to make your sales team smarter and more effective are clear. By embracing this technology, organizations can transform their sales teams into highly knowledgeable product evangelists that know how to effectively engage with customers, driving growth and success in the increasingly competitive sales landscape.

If you’d like to learn more, check out how Symbl.ai is helping Salesroom empower sales teams with real-time coaching and buyer engagement analysis in this recent blog post.

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