Customer Service Automation

Our AI/ML driven Customer Service Automation framework is designed to transform the way organizations manage customer relationships by enhancing their customer experience and engagementACSA® is designed to help customer service leaders enhance their customer service operations and create more seamless experiences for their customers. One of the challenges that many customer service organizations face is that CX initiatives are often focused on internal benefits, such as improving operational efficiency, rather than external benefits for customers. This can lead to siloed, departmental approaches to managing customer relationships that do not deliver the best outcomes for customers.


ACSA® addresses this challenge by helping organizations determine the maturity of each element of customer service and support. It analyzes the current state of the customer journey and documents the changes required across people, processes, and technology to create more seamless experiences. It also works with marketing, sales, and product teams to build a picture of the customer context and identify where value can be applied to self- and assisted-service channels of engagement.

With ACSA®, organizations can tailor their CX strategies to the technology needed for maximum efficiency. Powerful systems of record, legacy methods and distributed applications are combined with APIs and custom application platforms - all working in perfect harmony towards a single goal: creating personalized experiences that reach customers at just the right moment without feeling intrusive. AI-driven data analytics drive every step along this journey, allowing businesses to make smarter decisions faster than ever before!

Inside-out and Outside-in View Connection

One of the key benefits of ACSA® is personalization. The personalization module allows contact center applications to tailor an experience or information specific to a user or segment of users based on information the company has learned about that individual or segment. ACSA® also offers personalized recommendations of the next best action, next best offer, and next best communication strategies, which can help improve customer retention, acquisition, and overall experience.

Customer Routing

In addition to its advanced automation capabilities, ACSA® also offers AI-Based Customer Routing, a feature that enhances the customer experience by intelligently routing customers to the best agent or resource for their specific needs. By utilizing data and machine learning models, ACSA® is capable of classifying customers based on a multitude of variables, including acoustical variables from previous conversations and personality profiles of both the customer and agents. This ensures the result in higher customer satisfaction, shorter call times, and improved business performance metrics, such as increased revenue and customer retention. With AI-Based Customer Routing, businesses can reduce customer service costs and efforts while ensuring their customers receive the personalized attention and service they deserve.

Ensuring Optimal User Experiences

ACSA® not only focuses on improving customer-agent interactions but also on ensuring optimal user experiences. With ACSA®, agents can receive guidance in real time, enabling them to make informed decisions while serving customers and carrying out administrative tasks. Furthermore, ACSA® empowers customers to solve their problems independently by using chatbots and conversational assistants. These assistants can handle simple to highly complex queries and offer a personalized experience that is tailored to the customer's needs. Additionally, ACSA® dynamically routes customers to the best resources for their issues, replacing the traditional "contact us" page. This approach scales customer support, reduces agent load, and provides 24/7 availability. Ultimately, ACSA® aims to improve the availability of agents for non-routine tasks and enhance the overall user experience.

Language Sentiment Analysis

ACSA® incorporates language sentiment analysis to provide insights into customer attitudes and emotions towards products and services. By using speech-to-text, NLP, NLU, rules, and domain models, ACSA® can analyze thousands of customer reviews, recorded calls, and service ticket requests to identify patterns in sentiment towards specific products or services. This allows businesses to identify areas where improvements can be made, ultimately leading to higher customer satisfaction and increased revenue. Overall, ACSA®'s language sentiment analysis capabilities help businesses better understand their customers' needs and preferences, allowing them to make more informed decisions to improve their products and services.

Process Improvement

Businesses can leverage ACSA® technology to drive efficiency and cost savings. Its Intelligent Document Processing (IDP) provides powerful automation for extracting data from the semi-structured or unstructured textual content, such as driver's licenses and customer documents. Utilizing useful text analytics, natural language understanding & processing, and computer vision breakthroughs, along with machine learning – organizations are now able to classify documents quickly while simultaneously capturing pertinent information faster than ever before! By implementing this innovative approach, companies are seeing great improvements - particularly in invoice/order extraction processing time, payment accelerations, warranty card scanning accuracy and labor reduction – paving the way forward into a brand new era of business growth opportunities.

Use-Case Descriptions

Agent workforce scheduling

Agent workforce scheduling is a powerful feature that optimizes your customer service resources by combining agent information with historical contact volumes. By integrating this data, it enables you to forecast demand accurately and make real-time adjustments to your workforce to better meet customer needs.

This feature leverages AI to gather insights into agent preferences and skills, along with past customer contact volumes across different channels, to create a comprehensive picture of your workforce. It then uses this data to help you make informed decisions about resource allocation, ensuring that you always have the right people available at the right time.

By leveraging Agent workforce scheduling, you can increase efficiency and productivity in your contact center, reducing wait times for customers and improving the overall service quality. With the ability to forecast demand and adjust schedules in real-time, you can avoid overstaffing or understafing, and allocate resources effectively across different channels.

Conversational customer assistants for self-service

With our conversational customer assistants, customers can get the help they need quickly and easily without having to wait on hold or navigate through complicated phone menus. These assistants use natural language processing and understanding to interact with customers via text or speech, identifying and addressing customer intents to provide personalized assistance.

For example, if a customer needs to obtain account information, they can simply ask the assistant via text or spoken request, and the assistant will understand what they're asking for and provide the relevant information. This type of self-service option can save customers time and reduce frustration, as they don't have to navigate through multiple menus or wait for a representative to become available.

Our conversational customer assistants can also help businesses save time and resources, as they can handle a variety of customer inquiries and issues without requiring human intervention. This means that representatives can focus on more complex or high-priority issues while the assistants handle routine inquiries.

Customer emotion detection

Customer emotion detection is a powerful feature that uses emotion AI technologies to analyze the emotional state of a customer. This can be done through a variety of methods, including computer vision, audio/voice input, sensors and/or logic. For instance, a retailer might use a camera to analyze facial expressions to identify which products are most attractive to customers. By understanding the emotional state of the customer, businesses can tailor their interactions and offerings to better meet their needs.

The technology behind customer emotion detection varies depending on the specific implementation but generally involves analyzing inputs such as facial expressions, tone of voice, and body language to identify the emotional state of the customer. This data can then be used to personalize interactions, products and services for that customer, which can lead to improved customer satisfaction and loyalty.

The benefits of customer emotion detection are numerous. By understanding customers' emotional states, businesses can tailor their interactions and offers to improve customer satisfaction and loyalty. This feature helps companies identify potential issues before they escalate, allowing them to address customer concerns quickly and efficiently. Additionally, by identifying which products or services elicit positive emotions from customers, businesses can better target their marketing efforts and increase sales.

Customer journey analytics for next best action mapping

This feature helps businesses optimize the customer experience across different channels. It works by tracking and analyzing how customers and prospects interact with a company, from their first touchpoint to the final purchase or conversion. By analyzing this data, the system can identify where a customer may get stuck or disengaged, and suggest the best way to move the interaction forward.

For example, let's say a customer has abandoned their shopping cart on an e-commerce website. The system can analyze their behavior and suggest a personalized incentive, such as a coupon or free shipping offer, to encourage them to complete the purchase. This not only improves the chances of a successful sale but also enhances the customer's experience by providing relevant and timely offers.

Using customer journey analytics for next best action mapping can help businesses improve their customer experience by identifying and addressing areas where customers may get stuck or frustrated. By tracking and analyzing customer behavior across channels, businesses can gain insights into customer preferences and needs and use this information to suggest the best course of action to move the interaction forward. This can lead to increased customer satisfaction and loyalty, as well as higher conversion rates and revenue. Additionally, by offering targeted incentives or coupons based on customer behavior, businesses can improve customer retention and drive repeat business. Overall, customer journey analytics for next best action mapping can help businesses optimize their customer interactions and improve their bottom line.

Customer segmentation

Unlock the power of customer segmentation and tailor your services to fit each individual. Using advanced analytics, break down customers into different groups based on demographics, geography and behavior -- making sure to keep up with their interests in real-time or according to specific business goals. For instance, a bank could reward long-term clientele by offering exclusive rates that cater directly to their needs – now that’s personalization!

The feature works by analyzing large amounts of customer data to identify patterns and behaviors that differentiate groups of customers. These groups can then be targeted with customized marketing campaigns, product offerings, or service levels that are designed to meet their specific needs and preferences. By tailoring your approach to each customer segment, you can improve customer satisfaction, increase engagement, and ultimately drive more revenue for your business.

By dividing your customers into different groups, you can create more personalized experiences that resonate with each individual. This can help you build stronger relationships with your customers, improve loyalty, and reduce churn. Additionally, customer segmentation can help you identify new opportunities for growth by uncovering untapped segments or needs within your customer base. Overall, customer segmentation is a powerful tool that can help you improve your bottom line and build a more customer-centric business.

Human-in-the-loop (HITL) intent training

Have you ever interacted with a chatbot but found it difficult to get the information you needed? That's because chatbots are powered by natural language understanding (NLU) models that need to be trained to recognize different types of customer intents. Our HITL intent training feature uses a unique integration between NLU models and human experts to help train these models in real-time. When the NLU model is unsure about a customer's intent, the interaction is routed to a human expert who can quickly classify the intent and provide feedback to the NLU model. Over time, the NLU model gets better and better at understanding customer intents, leading to more accurate and personalized chatbot interactions.

The benefits of HITL intent training are numerous. For one, it leads to more accurate and personalized chatbot interactions, which can help improve customer satisfaction and engagement. By leveraging human expertise to improve the NLU model, you can also reduce the amount of time and effort needed to manually label customer intents. This feature also allows you to continuously improve and update your chatbot, ensuring that it stays up-to-date with changing customer needs and preferences. Overall, HITL intent training is a powerful tool that can help improve the accuracy and effectiveness of your chatbot interactions, leading to better customer experiences and stronger business outcomes.

Hyperautomation to improve fulfillment

Hyperautomation is revolutionizing the customer experience, allowing businesses to deliver orders with unbeatable speed and accuracy. Leveraging the latest artificial intelligence technologies like robotic process automation (RPA) and workflow integrations, companies can automate complex business processes for faster order-to-shipping times while minimizing any errors or delays along the way. A centralized customer engagement hub effectively redesigns fulfillment systems from top to bottom - helping customers get what they need when they need it!

Hyperautomation involves the use of algorithms and machine learning models to identify and automate routine and repetitive tasks, reducing the likelihood of errors and freeing up valuable time for employees to focus on more complex and value-added activities. This integration of AI technologies also enables better tracking and monitoring of processes, providing real-time insights and allowing for proactive interventions to prevent issues before they occur.

Hyperautomation provides a range of benefits, including increased operational efficiency, faster cycle times, and improved customer satisfaction. By automating repetitive and time-consuming tasks, organizations can reduce errors and improve the accuracy and consistency of their processes. This not only saves time and reduces costs, but it also allows employees to focus on higher-value tasks, such as building customer relationships and creating new business opportunities. Overall, hyperautomation enables organizations to achieve greater agility and responsiveness, enhancing their ability to meet customer needs and stay ahead of the competition.

Intelligent contact routing

Intelligent contact routing provides customers with an enhanced experience by utilizing predictive methods to match them up with the perfect agent or resource. By leveraging customer purchasing information, previous interaction history, and more advanced approaches like personality classification and behavioral pairing, this feature is able to provide a tailored solution that best suits each individual's needs; all while ensuring they receive service from the most effective person available!

Intelligent contact routing is a feature that uses advanced predictive algorithms to match customer requests with the most appropriate resource. By analyzing customer data and agent characteristics, this technology can determine the best agent for each specific customer request. This may include agent-personality classification or behavioral pairing based on historical data. For example, an agent may be chosen based on a customer's previous purchase history. This ensures that customers are quickly connected with the right agent who can provide the best possible service.

By matching customers with the most suitable agent, companies can provide personalized and efficient service that meets the unique needs of each customer. This can improve customer satisfaction, increase agent productivity, and reduce wait times, leading to greater customer loyalty and retention. With intelligent contact routing, companies can streamline their customer service operations and deliver exceptional experiences that set them apart from the competition.

Knowledge graphs to optimize chatbot conversations

Knowledge graphs are revolutionizing the way chatbots interact with customers. By leveraging graph methods to store a variety of data about its domain, such as individuals, products, prices and locations - conversational accuracy is dramatically increased. This allows for more sophisticated questions from users to be answered quickly and accurately; deepening customer engagement no matter how complex their inquiry may be!

The knowledge graph is constantly updated and refined based on customer interactions, ensuring that the chatbot is always equipped with the most up-to-date information. Additionally, the knowledge graph can be used to identify patterns and trends in customer interactions, which can be used to improve the chatbot's functionality over time. With this feature, businesses can provide faster, more accurate responses to customer inquiries, improving overall customer satisfaction and loyalty.

With the help of knowledge graphs, chatbots can now quickly and accurately provide relevant answers to customers' complex inquiries. This process tool utilizes advanced graph methods to store structured information about a wide range of topics such as people, places, products and prices – allowing it to swiftly identify customer queries in order answer with precision. For instance, an automated assistant may be able leverage its stored data on product compatibility with different operating systems when faced with a query from someone looking for that kind of specific advice - delivering faster results than ever before!

The benefits of using knowledge graphs in chatbots are numerous. Firstly, it enables the chatbot to provide more accurate and comprehensive responses to customer inquiries, improving the customer experience. Additionally, by using a knowledge graph, the chatbot can quickly adapt to changes in the domain, as new entities and relationships can be added to the graph in real time. Finally, by utilizing advanced graph methods, the chatbot can even infer new information from existing data, allowing for even more sophisticated interactions with customers.

Offer personalization

Customers expect a personalized experience when interacting with businesses in today's fast-paced world. Offer personalization is a module that leverages factors such as customer behavior, history, and customer journey stage to present real-time offers tailored to each customer. This means that businesses can offer personalized products, promotions, and discounts that are relevant and enticing to their customers.

Offer personalization uses sophisticated algorithms and machine learning techniques to analyze real-time customer data. This includes past purchases, browsing history, and even social media activity. This data then creates a detailed customer profile, including their interests, preferences, and buying habits. Using this information, the technology can suggest personalized offers that will most likely appeal to each customer.

Businesses can improve conversion rates and increase order value by offering personalized products and promotions. Customers are more likely to purchase when they feel the offer suits their needs and interests. Additionally, offering personalization can help to build customer loyalty by providing a unique and personalized experience that sets businesses apart from their competitors.

Postcall wrap up with text analytics

"Postcall wrap up with text analytics" is a useful feature that uses advanced technology to assist agents in their post-call wrap-up process. This feature helps to summarize conversations and analyze customer interactions using various tools like text analytics, text summarization, speech-to-text, domain model, and rule-based methods.

When a customer service agent is on a call, the feature monitors the conversation and transcribes it into text. At the end of the call, the text is summarized, and key points are highlighted. This helps the agent to quickly identify important issues that need to be addressed and to follow up with the customer accordingly.

The benefits of this feature are numerous. Firstly, it saves time for the agent by providing a summary of the conversation instead of forcing them to listen to the entire call again. This not only makes the wrap-up process more efficient but also reduces the risk of missing important details. Additionally, the feature helps to identify trends and patterns in customer interactions, which can be used to improve overall customer service and satisfaction. By analyzing the conversations, the feature can also provide insights into common issues and concerns that customers have, allowing businesses to proactively address these concerns and improve their customer experience.

Predicting customer lifetime value

Predicting customer lifetime value is a powerful tool that uses historical data to predict the future revenue or profit that each prospect or customer is likely to generate for a business. By analyzing factors such as customer spend, churn rate, and cost of sales and support, predictive models can be developed to identify the most valuable prospects and customers, allowing businesses to target their sales and retention efforts more effectively.

So how does this work exactly? The process starts by collecting and analyzing data on customer behavior and purchasing patterns. This data is then used to create predictive models that can estimate how much revenue or profit each customer is likely to generate over their lifetime with the business. By using these models, businesses can identify the customers with the highest potential value and focus their efforts on retaining and upselling those customers.

The benefits of predicting customer lifetime value are clear. By identifying high-value prospects and customers, businesses can allocate their resources more efficiently and effectively, ultimately driving revenue growth and profitability. Additionally, this approach can help businesses to personalize their marketing and sales efforts to better engage with their most valuable customers, leading to stronger relationships and higher customer satisfaction. In short, predicting customer lifetime value is an essential tool for any business looking to optimize their sales and retention efforts and drive long-term growth.

Real-time agent coaching

Real-time agent coaching is a powerful module that uses advanced technology to help agents improve their performance on the job. With this feature, agents can receive support and recommendations in real time, which can be incredibly helpful in difficult or complex situations.

The agent coach listens to a call in real-time and uses text analytics, speech-to-text, and rule-based methods to analyze the conversation. If the agent seems to be struggling or unable to find an answer, the coach can silently provide support or recommendations to help them through the call. This support can be in the form of suggested responses, information about the customer, or guidance on how to resolve the issue at hand.

The benefits of real-time agent coaching are numerous. By providing agents with immediate support and guidance, they are better equipped to handle difficult situations and resolve customer issues more efficiently. This can increase customer satisfaction, reduce call times, and improve agent performance overall. Additionally, the data collected by the coach can be used to identify areas for improvement and to develop targeted training programs to further enhance agent skills and performance.

Redacting personally identifiable information (PII)

In today's data-driven world, keeping customer information safe is of utmost importance. But sometimes, agents handling customer interactions may come across personal information that they shouldn't see. This is where redaction comes in.

Redaction is a process of identifying and removing sensitive information from customer interactions or text sessions with agents. Redaction tools use natural language processing, named entity recognition, part-of-speech tagging, and rules to identify and redact text information that an agent should not see in the log. For example, redaction tools can identify names, addresses, phone numbers, and credit card numbers and remove them from the logs.

The benefit of redaction is that it helps businesses stay compliant with privacy laws and regulations while still allowing them to analyze and use customer interactions for business purposes. By redacting sensitive information, agents can focus on the customer's needs without the distraction of personal information. It also helps prevent data breaches and protects customer trust.

Speech analytics of sentiment or topics

Speech analytics of sentiment or topics is a powerful AI mechanism that provides valuable insights into customer conversations. It uses a combination of speech-to-text and text analytics to extract contextual insights from recorded or real-time voice streams and conversations. Businesses can analyze customer conversations to gain insights into topics, categories, emotional engagement, product feedback, competitive information, customer sentiment, and compliance.

The process begins with the AI tool transcribing the voice stream into text and then analyzing the text for specific keywords, topics, and categories. The AI system also analyzes the tone and sentiment of the conversation to understand the emotional engagement of the customer. Additionally, the system can identify feedback about products or services and gather information about competitors in the market. Finally, it checks for compliance with regulations and company policies.

It gives businesses a better understanding of their customers and needs, allowing them to make more informed decisions about product development, marketing strategies, and customer service. It also enables businesses to identify issues and opportunities in real time, allowing for quick response and action. Ultimately, speech analytics of sentiment or topics can help enterprises to improve customer satisfaction, increase revenue, and reduce costs.

Trusted agent passive voice biometrics

Trusted agent passive voice biometrics is a cutting-edge process that extracts unique features and characteristics of each agent's voice, creating a voiceprint that can be used to verify their identity throughout the workday. This technology can be particularly useful for work-at-home customer agents, ensuring that the person speaking throughout the day is indeed the actual agent and not someone who has replaced them.

This module regularly listens to the agent's voice during the day and creates a voiceprint that serves as a unique identifier for the agent. This voiceprint is created based on various characteristics of the agent's voice, including tone, pitch, and cadence. Then, the technology compares the voiceprint to a baseline recording made at the beginning of the agent's shift to ensure that the speaker is the same person.

One of the key benefits of this technology is that it helps maintain the security and integrity of customer interactions. By ensuring that the agent is who they say they are, this feature can help prevent fraud, data breaches, and other types of security threats. Additionally, it can help improve agent accountability, as agents will know that their voice is being monitored throughout the day. Finally, by providing an extra layer of security and identity verification, this technology can help build trust with customers, ensuring that they feel safe and protected when interacting with your company.

Virtual assistant for new agent onboarding

A virtual assistant for new agent onboarding is a chatbot designed to guide new agents through their first month on the job. It can integrate with back-end employee systems to provide personalized guidance and training for each new employee. For instance, the assistant can answer common questions and help new agents navigate their specific training and shadowing routines.

This feature uses trained algorithms to simulate human conversation through chatbots. It can provide personalized guidance and training to each new employee using their personal information from the back-end employee systems. The chatbot can answer common questions, provide step-by-step instructions, and offer tips and tricks for success.

Personalized guidance and support can help new employees feel more comfortable and confident in their new roles. It can also help to reduce the time and resources required for employee training, as the chatbot can guide multiple new employees simultaneously. This feature also improves the overall quality of customer service by ensuring that all new agents receive consistent training and support.

Visual search for customer sales

Visual search is revolutionizing the way customers find products, making it easier and faster than ever. With this cutting-edge technology, shoppers can locate what they are looking for by simply uploading an image or filtering based on specific visual characteristics like color and shape. Powered by powerful neural networks, these models recognize object context to deliver accurate results - giving consumers a smart shopping experience!

The system works by analyzing images uploaded by customers and extracting visual attributes such as color, texture, shape, and size. Then, the system uses machine learning algorithms to find products with similar visual attributes, matching the customer's preferences. This allows customers to easily find the products they want to buy, without having to spend a lot of time searching through long lists of items.

Visual search for customer sales provides numerous benefits to both customers and businesses. For customers, this technology makes shopping more convenient and enjoyable, as they can easily find the products they want to buy by simply uploading an image or using filters based on visual attributes. This saves them time and effort, leading to higher customer satisfaction and increased sales. For businesses, this technology helps to boost sales, as it allows customers to easily find and purchase products they might not have discovered otherwise. It also helps to improve customer loyalty by providing a unique and innovative shopping experience.

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