In today's contact center business, a company's position is affected by a few keystrokes anywhere in the world. The way a contact center works has completely changed after the coronavirus pandemic, as global companies have shifted to remote work.
Contact center operations are based on transactional systems that generate a large amount of data and are typically stored independently.
Every day we collect more information about our customers, both in person and in aggregated form across multiple channels. Analytics provides the ability to perform behavioral analysis of customers based on this data. Let us take an insurance company as an example, where a wealth of data is generated:
- Policies information
- Pattern of use
- When do our customer purchase policies?
- What channels are they using?
- What are the sentiments of the customer?
Analytics provide insights that help organizations solve problems in managing the right number of agents, queues, and call distribution, and identify the root cause of the triggered interaction
At the same time, contact center agents are trained to interact with customers not just over calls, but over multiple channels, making them more versatile than your standard call center agent. Demand for support across multiple channels such as SMS, email and social platforms is also increasing, requiring heavy investment. However, the growth rate remains low and managing the volume is a challenge.
This is where contact center analytics can provide great value to contact center process. Analytics in the contact center can help organizations gain deep insights into the customer journey that lead to a better customer experience. It measures and manages the effectiveness of call center agents in handling customer calls (outbound or inbound). It is a set of tools that help in capacity planning and ensure that the number of agents working at the same time matches the call volume during those hours, without compromising call quality and customer satisfaction. Companies are also moving from a reactive contact center operation to a proactive operation that transforms from a simple receive and respond model to one that analyzes, predicts, monitors and optimizes customer interactions, resulting in lower costs, happier customers and higher revenue.
Principles based on analytics that can help an organization in decision making:
- Cause of interaction initiated by the customer
Even for a simple reason, when a customer initiates an interaction, this source of information is valuable for the contact center to find the root cause of the problem and focus on ways to leverage self-service options or low-cost channels. Root cause analytics identifies problems and solutions that can help reduce interaction volume and AHT and increase customer satisfaction.
- Need for automated AI-based self-service
Customers are looking for self-service tools that reduce turnaround time and improve response time, but due to lack of AI, fail to deliver the right answer and leave the customer frustrated. Our AI.1 offering allows the business to complete services without the use of a human in most cases. The virtual agents gather most of the information on behalf of the human and help the agent serve more customers with better quality.
HCLTech introduced conversational assistant for Banking 4.0. Ziva is an NLU-driven conversation for all channels and media – voice, chat, social, mobile and integrates seamlessly with CRM and CC platforms via API-Hub to deliver voice banking services.
- Entrust and transform your agents
In today's contact center industry, agents have a high turnover due to a stressful environment. Contact center agents should be viewed as employees who can earn the trust and loyalty of customers, as this can lead to positive business results. Our Agent.X offering empowers and transforms agents via desktop analytics. It is a systematic approach to detecting agent activity on the desktop and uses this information to provide real-time analytics and guidance, to improve the agent and customer experience while increasing productivity.
- Acquire customer interactions with contact center predictive analytics
Most service interactions in the contact center are inbound. If we capture these and resolve the issue before the customer gets in touch, it reduces call volume and delights the customer. Our Predict.X offering enables the business to improve customer journey and revenue using predictive analytics. It monitors self-service interactions and alerts customers who may be experiencing similar issues, even if they have not yet been contacted about the problem.
For example, if contact center analytics show that customers who had issue A also had issue B, then a customer who contacted with issue A would be prompted to review and resolve issue B during the same interaction, reducing repeat calls and improving FCR and customer satisfaction. Monitoring self-service interactions can also identify and alert customers with similar issues, even if they have not been contacted.
- Monitor and inform the customer journey across channels
Today, contact center solutions provide customers with an omni-channel experience, but often lack the necessary, effective and rapid analysis of data. Without analytics, companies miss out on not only resolving customer inquiries, but also future needs and questions. Many companies are moving from multi-channel experiences to true omni-channel solutions, but their legacy systems often struggle to adopt analytics.
Companies can deploy a CCaaS solution (Contact Center as a service) that provides three insights. First, enable businesses to get lots of real-time data analytics from multi-channel and enterprise systems that help customers on their customer journey. Second, ‘Get Predictive’. The predictive analytics engine can predict the reason of interaction, predict the next interaction, optimize offers, etc. This is to deliver a better response according to customer behavior and history. Finally, you need to ensure that all channels are connected to provide a better experience to the customer.
Our Sentiment.Quant offering allows the business to monitor the quality of conversations, from the agent level to the business metrics level, in a fully automated mechanism for most interactions. This allows the business to respond quickly to non-technical issues and ensure that customer experience is maintained at a manageable level.
Adopting contact center analytics drives business value and improves below metrics.
- Average Handle Time: Speech and non-interactive datasets include numerous unstructured datasets. With contact center sentiment analytics, companies can gain meaningful insights and recognize areas for improving AHT.
- First-Contact Resolution: Apply an analytic approach to analyze customer call patterns and provide first-contact solutions to improve customer satisfaction.
- Customer Satisfaction: The company can rely on the data analyzed by the analytics solution for Successful Interaction rate and customer satisfaction.
HCLTech has taken a comprehensive view of analytics and built a product to cover every use case possible. For more details, please reach out to us at Contact.FluidCC@hcl.com