Varun Milind Kulkarni: Digital Product Management Strategies for AI-Based Reporting and Analytics in Cloud Contact Centers

With global society firmly in the digital age, integrating AI-based reporting and analytics in cloud contact centers continues redefining how businesses engage with customers. In particular, contact centers are at the center of the AI-powered transformation, as emphasized by McKinsey Insights' prediction that by 2025, AI will power 95% of all customer engagement interactions.

While organizations have much to gain by incorporating these new tools, digital product management strategies must be in place to reap the benefits.

In this article, I will explore the key components of effective digital product management strategies used to build cutting-edge Contact Centers that use next-gen reporting and analytics. I will also discuss strategies, challenges, advanced product management frameworks, and practical use cases based on personal experiences to help product leaders understand how to manage or define their contact center vision.

Key Components of Digital Product Management Strategies

Integrating AI-based reporting and analytics in cloud contact centers has transformed how businesses engage with customers. According to Gartner, AI-based Reporting and Analytics will reduce global Contact Center costs by $80Bn by 2026. However, digital product management strategies need to be in place for organizations to reap the benefits of these tools. These strategies have the following key components, which product managers need to integrate for customer-centric product solutions.

1. Business Objectives and KPIs: Effective digital product management strategies for AI-based reporting and analytics must be backed by clear business objectives and KPIs. This involves identifying the organization's desired outcomes, such as reducing customer wait times or improving agent productivity by a specific percentage. Once these objectives have been defined, the organization can identify the KPIs that will measure progress toward these goals and create OKRs

2. Data Collection and Analysis: Each organization should ensure that it has access to the data required to analyze customer interactions effectively. Collecting data from multiple sources, such as phone calls, chats, and emails, and using AI-powered tools to analyze this data are all important. In doing so, organizations can gain valuable insights into customer behavior and preferences, creating omnichannel experiences which can inform decision-making

3. Reporting and Visualization: Developing effective reporting and visualization tools should be prioritized and involves creating dashboards that provide real-time KPIs, such as agent statistics or performance, agent utilization rates, contacts handled, and queues engaged. Effective reporting and visualization tools enable contact center supervisors and business owners to identify trends and make data-driven decisions quickly and efficiently. This helps further improve operational processes

4. Product-Led Growth: Focus on creating products that are so compelling that they drive customer acquisition, retention, and expansion without the need for extensive marketing or sales efforts

5. Customer Success: Ensure customers achieve their desired outcomes using reporting and analytics features. Provide proactive data frameworks, ultimately helping them to increase customer satisfaction, reduce churn, and drive revenue growth

Benefits of Effective Digital Product Management Strategies

Implementing these digital product management strategies, offers several benefits for optimal Contact Center reporting and analytics:

• Improved Customer Experiences: One of the most significant changes is that organizations can personalize interactions and anticipate customer needs more effectively. By leveraging these tools, they are delivering exceptional customer experiences that drive customer loyalty and satisfaction, a significant win

• Increased Operational Efficiency: Organizations have also optimized staffing levels, improved agent productivity, and reduced wait times. This, in turn, means that they can operate more efficiently and reduce costs

• Better Decision-Making: Managers use real-time insights into customer interactions to make informed, data-driven decisions. They have the confidence to act more quickly and decisively on crucial questions and know that they will achieve the best possible outcomes

Challenges of Implementing Digital Product Management Strategies

Cloud contact centers will inevitably encounter challenges when using AI-based reporting and analytics, though they can be overcome.

• Data Quality and Integration: The quality of data collected from different sources can vary, making it hard to integrate data effectively. Organizations need to ensure that they have the tools and processes in place to manage, analyze, evaluate, and integrate data from multiple sources

• Technical Expertise: Data science and machine learning heavily influence AI-based reporting and analytics. Organizations need to ensure that they have the necessary expertise in-house or partner with vendors that can provide these skills

• Organizational Alignment: Implementing digital product management strategies requires alignment across the organization, including IT, marketing, and operations. By fostering coordination and cooperation among these departments, organizations can optimize their digital product management efforts, enhance customer experiences, and drive business growth in the digital landscape

As product managers leverage these strategies, they should also integrate the following digital product management framework to optimize, improve and continuously refine AI-based reporting and analytics for their Contact Centers. By combining this framework, organizations can increase customer engagement, facilitate operational efficiency, and make data-driven decisions that drive business growth.

Framework for Implementing Strategies

1. Customer-centric Approach: Focus on creating products that solve specific customer problems. Prioritize features based on customer needs and preferences, ensuring that the reporting and analytics tools provide valuable insights to the end-users. Feature prioritization can be done based on any appropriate methodology like Weighted Shortest Job First (WSJF) or the Kano Model

2. Agile Development Methodologies: Adopt an Agile approach to quickly respond to changing customer needs and feedback

3. Data-driven Decision-Making: Normalize data-driven decisions based on insights and feedback from customers and stakeholders. Use data to identify areas for improvement for reporting and analytics as well as informed product development

4. Continuous Testing and Validation: Practice continuous testing and validation of reporting and analytics features to ensure they meet customer needs and expectations. Ensure AI-based reporting and analytics in cloud contact centers, provide valuable insights, and are user-friendly without complex workflows

Finally, I would like to highlight some use cases where I ideated, designed, and implemented digital product management strategies to build and launch AI-based reporting and analytics for cloud contact centers, significantly impacting B2B, enterprise customers.

Examples of Use-Cases I led as a Digital Product Manager

Conclusion and Key Learnings

The correct digital product management strategies are crucial in improving AI-based reporting and analytics in cloud contact centers. Organizations can optimize customer engagement and drive business growth by adopting customer-centric approaches, utilizing agile development methodologies, making data-driven decisions, promoting collaboration, and employing continuous testing and validation. These positive outcomes demonstrate the potential for leveraging advanced AI-based reporting and analytics for generating significant return on investment (ROI), next-gen customer experiences, and business success.

About Varun Kulkarni

Varun is a Senior Product Manager at Cisco. He leads their Webex B2B Reporting & Analytics as well as Cloud Contact Center Data Modernization. Previously, Varun worked as a Senior Consultant at Deloitte Consulting LLP. At Deloitte, he supervised multiple cross-functional teams to lead end-to-end Product Management and Management Consulting efforts for Fortune 500 and public-sector clients.

Varun has an MBA from the University of Toronto's Rotman School of Management and an MS from Northwestern University. He is a Product Leader with seven years of experience across cutting-edge AI/ML, Cloud, Digital Transformation technologies across the Technology, Healthcare and Consumer Services sectors. He also focuses on advising Startups and is keen on delivering next-gen customer centric digital experiences.

Varun is also an Alumni Mentor for Northwestern University, a University of Washington MBA EDGE Mentor, and a Criya Featured Expert for Mentoring. To connect with Varun, please reach out to him via LinkedIn.