Operationalizing AI for Customer Experience Management

Senior Product Manager & Lead Designer · 2025-Present

Responsibilities

  • Coordinating with stakeholders to drive sales
  • Supporting Technical Support, Engineers, QA while running product and design
  • Working directly with Enterprise clients to improve their CX programs

Growth

  • Profitable product within the product line with +$5 million in sales
  • Customers in more than 80 countries including Footlocker, Jack in the Box, Docusign, Zoom, and Glassdoor

KPIs

  • Purchased by PG Forsta in May 2025
  • Surveys Sent - 5+ billion

1. Framing the Problem: From Reactive CX to Predictive Intelligence

Traditional CX tools like NPS and CSAT act as lagging indicators, telling teams what already happened rather than what will happen next. Organizations struggle to extract value from unstructured feedback such as reviews, support tickets, and survey comments, which make up the majority of customer data.

This results in teams spending significant time manually analyzing feedback instead of taking action. The opportunity was to transform overwhelming qualitative feedback into predictive real-time decision-making tool.

PG Forsta

How to turn customer feedback into actionable results.

2. Product Challenge: AI-Powered Predictive CX (welcome Wootric CXI)

The goal was to evolve customer experience from measurement to prediction and action. Wootric CXI leverages AI, natural language processing, and large language models to unify structured and unstructured data, interpret feedback at scale, and forecast customer outcomes such as churn, loyalty, and conversion.

Rather than simply surfacing insights, the platform identifies key drivers of customer behavior and recommends actions. This enables teams to move beyond analysis and into proactive experience management.

3. Turning Data into Insight

A key challenge was making unstructured data usable. AI-powered summarization transforms large volumes of qualitative feedback into clear, digestible themes, while machine learning dynamically evolves topic detection over time.

The design prioritizes surfacing what matters most by highlighting key drivers impacting business metrics and ranking insights based on predicted impact rather than volume. This required careful design of human-AI collaboration, ensuring outputs are transparent, explainable, and actionable.

PG Forsta

An example of how AI analyses feedback to categorize and measure to find key insights.

4. From Insight to Action: Closing the Loop Automatically

The true value of AI comes from driving action. The platform integrates predictive insights directly into workflows, enabling automated responses, customer recovery actions, and intelligent segmentation based on predicted behavior.

This allows organizations to anticipate churn, prioritize high-impact improvements, and personalize customer engagement at scale. By using the full suite of tools, the gap between insight and action is significantly reduced.

PG Forsta

Auto generating responses to close the loop.

5. Outcomes & Impact

Clients are integrating these processes as we speak into their everyday workflow. Each organization is different but the biggest takeaway has been a dramatic decrease in the manual work that normally came with closing the loop and finding the insights. With this addition, customers have now also had the desire to be even more involved with their CX program. Wanting the refine the processes even further and measure the results.

Utilizing AI to help customer support, sales, and product spend more time working on the important KPIs and less time on the busy work, I think has been a great example of how to use AI effectively.