Boosting CX and agent productivity with Microsoft Copilot

With the accelerating embrace of Artificial Intelligence, financial services have a once-in-a-generation opportunity to re-imagine customer experiences. This includes hyper-personalizing contact center interactions to engage with customers in new ways. Consider the following story.

Every day since his promotion from customer service representative (CSR) to contact center manager for a global bank, Michael has faced the same challenge: Enabling representatives to deliver personalized customer service despite operating in a communications silo. Like so many financial services offering customer-facing omnichannel experiences, his institution struggles to orchestrate end-to-end experiences that encourage customers to engage in the channel of their choice while providing easy movement between those channels and equipping CSRs with the context from all those interactions.

As a financial services customer himself, Michael has personally experienced the frustration of starting a financial journey at a branch and, later, when he had follow-up questions, explaining the entire situation over the phone multiple times as he was transferred to various CSRs for answers.

As a former agent, Michael knows all too well the limitations imposed by the lack of comprehensive customer visibility. He often found himself wishing for a 360-degree view of the customer, encompassing their previous financial services interactions across various channels—whether in-person visits, phone calls, chatbot conversations, or any other channel utilized by the bank. Without this holistic perspective, Michael and his fellow agents were often forced to transfer customers to other departments or locations, prolonging resolution times and diminishing the overall customer experience.

This lack of visibility not only hampered the efficiency of the customer service process but also prevented agents from providing personalized and proactive assistance to customers, ultimately hindering the bank’s efforts to deliver exceptional service. Moreover, inadequate knowledge retrieval mechanisms and lack of real-time assistance led to delays in obtaining necessary information, resulting in frustrated customers and low satisfaction levels.

Now that Michael is a manager, he’s also thinking about how to develop and retain his team. Staff attrition poses challenges, as it typically takes months of training for a new representative to fully contribute. With contact center attrition still above pre-pandemic norms and the competition for qualified talent remaining strong, minimizing the disruptions caused by turnover is a strategic priority.

In combination, these obstacles not only impact customer satisfaction and the employee experience, but also make it harder for CSRs to offer customers the additional services that drive revenue.

The result? Yet more stress for Michael, who ponders these challenges constantly. How can he help his team overcome the lack of customer insights that prevent delivering the best experiences and address competitive pressures? How can he best engage, coach, and develop his employees?

Increasingly, the path forward is leveraging artificial intelligence for customer engagement channels.

Personalization hurdles hold financial services back

70% of a CSR’s work day is consumed by repetitive manual tasks.

As expectations for hyper-personalized customer experiences (CX) continue to grow and the macroeconomic environment fluctuates, financial services contact centers like Michael’s are increasingly called upon to improve the interactions they deliver.

This imperative is particularly acute for establishing brand loyalty and reducing churn among Gen Z customers. Early in their financial journeys, this personalization-focused generation is becoming a significant banking growth engine worldwide as it will inherit around $30 trillion from their parents through 2030.

Recognizing the importance of building a future-focused customer pipeline, financial services are including contact centers in their digital transformation roadmaps. According to Capgemini’s World Retail Banking Report 2024, 70% of bank CXOs plan to increase digital transformation investments by up to 10% in 2024.

This means tackling challenges such as:

Delivering personalized experiences to each customer: Today’s customer service expectations demand a 360-degree view of each customer, a challenge acknowledged by CSRs. Agents require easy access to comprehensive customer details and history across multiple platforms, including chatbots, apps, and social media. This necessitates robust customer relationship management systems that enable tailoring interactions to everyone’s unique needs and preferences. By empowering agents, a holistic view, financial services can improve service quality, reduce resolution times, and foster stronger customer relationships, positioning themselves as leaders in delivering personalized assistance in the modern era.

Navigating complex questions and interactions: As financial service relationships are frequently complex; CSRs must be adept and agile at rapidly understanding customer questions and answering them succinctly. To do so representatives need readily available data and timely, useful suggestions for guiding conversations. By 2026, conversational artificial intelligence deployments within contact centers will reduce agent labor costs by $80 billion.

Turning massive pools of data into insights: Although siloed customer data is a ubiquitous financial services hurdle, the challenge goes beyond connecting enormous and disparate data pools. The sheer volume of data is too great for humans to review and synthesize quickly enough to turn data into insights. AI provides a path to creating such insights in real time.

Eliminating repetitive manual processes: According to Capgemini’s World Retail Banking Report 2024, repetitive manual tasks consume about 70% of a CSRs workday, leaving only 30% for focusing on customers and just 18% for selling new products and services. Using AI-driven tools to summarize customer interactions increases accuracy and saves time, freeing staff to add value.

Overcoming staff attrition: With all the challenges front line workers face, financial services must do more to reduce turnover-related stresses. Delivering better tools to support customers increases CSR retention, as they enhance the employee experience. Such tools also provide contact center managers with capabilities for directing customer inquiries to the best available representative and enable managers to improve their coaching.

Enhancing fraud detection: Although fraud detection systems continue to improve, the Capgemini World Retail Banking Report 2024 revealed that bank employees are keenly interested in better automation for identifying suspicious data or activities. As AI-driven contact center tools can rapidly sift through multiple data pools, pinpoint anomalous behaviors, surface insights, and make recommendations in real-time, potential fraud can be uncovered early and staff alerted to act.

Ensuring continuous technology evolution: Financial services have a long history of adopting new technologies, only to have them become legacy anvils. For a modern CX solution to be effective, it must constantly evolve to solve next generation demands as they arise. Cloud-based platforms deliver continuous innovation and ensure that financial services can stay up to date with technological advancements.

Unveiling Microsoft Dynamics 365 Contact Center: The artificial intelligence tool transforming customer experience

We are seeing significant interest in and adoption of Microsoft’s contact center solutions by customers in the financial services industry, a testament to our deep investment in AI, including the integration of Nuance into our platform. We are pleased to work with Capgemini to help drive this innovation within our mutual banking, insurance, and capital markets customers.

Jeff Comstock
Corporate Vice President for Microsoft Dynamics 365 Customer Service, Microsoft

Owning massive pools of data is not the same as providing contact center staff with actionable, real-time insights. Solving this challenge requires next-generation technology that non-disruptively layers on top of existing systems to extract data from multiple silos, analyze it, apply predictive modeling, and present actionable, rapidly consumable recommendations to contact center staff. The technology must also continuously learn to hone future results.

Enter Microsoft Copilot, a cutting-edge AI-powered assistant designed to revolutionize productivity and efficiency in the workplace. Copilot leverages advanced AI and machine learning capabilities to automate routine tasks, provide real-time insights, and enhance overall user experience.

Recently, Microsoft announced Microsoft Dynamics 365 Contact Center, a Copilot-first contact center solution. This product is a standalone contact center as a service solution (CCaaS) enabling customer service teams to modernize their service experiences with artificial intelligence.

In the context of customer service and contact centers, Microsoft Dynamics 365 Contact Center plays a pivotal role by providing real-time assistance to customer service representatives (CSRs). It enhances interactions through sentiment analysis, intelligent routing, and a 360-degree view of the customer. Integrating with existing CRM systems, it provides a unified and efficient workflow with extensive scalability. By doing so, it not only improves operational efficiency but also elevates customer satisfaction through more personalized and timely responses.

As businesses increasingly adopt AI-driven solutions, Microsoft Copilots stand out as a versatile tool that adapts to various industry needs, offering scalable and customizable features that drive digital transformation and innovation.

70% productivity surge expected from generative Microsoft Copilots

Transforming Contact Centers with Microsoft Copilot: Elevating Customer Experience

In today’s fast-paced, customer-centric world, the transformation of contact centers is pivotal for businesses seeking to deliver superior customer service. Copilot is equipped with large language models (LLMs), sophisticated algorithms, and other machine learning technologies, and offers a comprehensive solution to revolutionize contact center operations. Microsoft Dynamics 365 Contact Center is a Copilot first tool, and this article explores how this can enhance customer interactions, improve efficiency, personalize experiences, and support financial services in providing dynamic solutions to their customers.

Enhanced Customer Interaction

Real-Time Assistance: Providing real-time suggestions and information to Customer Service Representatives (CSRs) during customer interactions. This ensures accurate and timely responses, significantly enhancing the quality of service.

Sentiment Analysis: By analyzing customer tone and sentiment in real-time, the pre-integrated Copilots for digital and voice channels guide CSRs on the best approach to manage conversations, leading to improved customer satisfaction.

Multichannel Support: Assisting in managing interactions across various channels such as phone, chat, email, and social media, providing a unified and consistent customer experience.

Improved Operational Efficiency and Productivity

Automated Routine Tasks: Automating repetitive tasks including data entry, call logging, and customer information retrieval. This allows CSRs to focus on resolving more complex customer issues.

Knowledge Base Integration: With quick access to and retrieval of relevant information from the organization’s knowledge base, this tool helps CSRs resolve queries faster and with greater accuracy.

Workflow Optimization: By analyzing interaction patterns and workflows to create artificial intelligence based real-time reporting, Copilot allows service leaders to enhance operational efficiency across all support teams.

Personalized Customer Experience

Customer Insights: Analyzing historical customer data to provide CSRs with insights into customer preferences, past interactions, and purchase history, enabling a more personalized service approach.

Proactive Engagement: Utilizing predictive analytics, potential issues are identified before they arise and prompts CSRs to take proactive measures, thereby enhancing customer satisfaction. Such conversation tools include sentiment analysis, translation, conversation summary and transcription.

Training and Development

Skill Development: Offers real-time feedback and coaching to CSRs, aiding continuous improvement in their skills and knowledge.

Scenario Simulation: Through the simulation of various customer interaction scenarios, this tool aids in training new CSRs, preparing them effectively for real-life situations.

Data-Driven Decision Making

Performance Analytics: Copilot generates detailed reports and analytics on CSR performance, customer interactions, and overall contact center efficiency, empowering managers to make informed decisions.

Customer Feedback Analysis: By analyzing customer feedback and interaction data, this tool identifies trends and areas for improvement, supporting strategic planning and service enhancement.

An omnichannel contact center powered by artificial intelligence will need a market-centric data layer as its foundation to ensure seamless and interconnected service across all interaction channels. Leveraging Microsoft Dataverse with Copilot will integrate the data and drive both personalized customer experience & operational efficiency.

Chandramouli Venkatesan
Vice President & Head of Customer Experience CoE, Capgemini Financial Services

Enhanced Security and Compliance

Sensitive Data Handling: Ensuring that customer data is handled securely and in compliance with relevant regulations, mitigating the risk of data breaches and compliance violations.

Fraud Detection: Monitoring transactions and interactions, detecting and preventing fraudulent activities, ensuring the security of customer accounts and information.

Application in the Financial Services Sector

For financial services, Copilot offers tailored solutions to enhance customer service:

• Personalized Financial Advice: Leveraging customer data and predictive analytics, assisting CSRs in offering personalized financial advice and product recommendations.
• Loan and Credit Processing: Streamlining loan and credit application processes by automating document verification and providing real-time status updates to customers.
• Fraud Prevention: By analyzing transaction patterns and detecting anomalies, this tool helps financial services prevent fraud and protect customer accounts.
• Regulatory Compliance: Ensuring that all customer interactions and transactions comply with financial services regulations, reducing the risk of non-compliance penalties.

Microsoft Copilots aren’t about replacing people. They’re about empowering contact center staff.

Key Steps for Successful Microsoft Dynamics 365 Contact Center Implementation

Implementing Microsoft Dynamics 365 Contact Center demands a strategic approach to ensure a seamless transition and maximize performance. Here’s a streamlined guide to achieving success:

1. Assessment and Planning
   Evaluate Current Systems and Processes: Conduct a comprehensive review of existing contact center operations, technology infrastructure, and workflows. Identify critical pain points where the pre-integrated Copilots can enhance efficiency.
   
   Define Objectives and KPIs: Of the six most critical AI copilot key performance indicators, the Capgemini World Retail Banking Report 2024 found that less than 6% of financial services have developed, and are tracking, KPIs appropriately. For transformation success, it’s imperative that financial services make KPI development and monitoring a priority. Set clear, measurable goals such as reducing average handle time and boosting customer satisfaction. Establish key performance indicators (KPIs) to track progress and success.
   
   
2. Stakeholder Engagement
   Secure Executive Buy-In: Gain executive support by articulating the benefits of Copilot, securing necessary budgets and resources.
   
   Engage Frontline Staff: Involve customer service representatives (CSRs) and managers early on to gather their input and address any concerns, ensuring their commitment to the implementation.
   
   
3. Customization and Integration
   Tailor Features: Customize pre-integrated Copilots for digital and voice channels to address the specific needs of your contact center by configuring use cases and integrating with existing CRM systems or custom apps.
   
   Seamless Integration: Ensure Copilots integrates smoothly with existing tools, minimizing disruption and maintaining workflow continuity.
   
   
4. Training and Onboarding
   Comprehensive Training Programs: Develop detailed training programs for CSRs and managers, incorporating hands-on sessions, user manuals, and support resources.
   
   Pilot Programs: Initiate a pilot implementation with a select group of users to test and refine features before a full-scale rollout.
   
   
5. Monitoring and Optimization
   Continuous Monitoring: Regularly assess Microsoft Dynamics 365 Contact Center performance using predefined KPIs, analyzing data to gauge its impact on efficiency and customer satisfaction.
   
   Feedback Loops: Establish feedback channels for CSRs to share their experiences and suggestions, using this input to make necessary adjustments and enhancements.
   
   
6. Scalability and Expansion
   Scale Gradually: Based on pilot results, progressively expand the implementation across the contact center, ensuring each phase is meticulously managed and supported.
   
   Future proofing: Stay updated with Microsoft’s latest features and advancements in AI, continuously enhancing contact center operations.
   
   
7. Security and Compliance
   Data Security: Ensure the implementation complies with data security standards, safeguarding customer data and handling sensitive information securely.
   
   Regulatory Compliance: Adhere strictly to industry regulations and compliance requirements, ensuring the ethical use of AI in customer interactions.

92% of financial services lag in developing KPIs to gauge the actual performance and accuracy of AI solutions