Product School

How Product Managers Can Use Gen AI to Prioritize Customers

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Carlos González De Villaumbrosia

February 22, 2024 - 7 min read

Updated: May 6, 2024 - 7 min read

The integration of Artificial Intelligence (AI), especially Generative AI, is revolutionizing product management. This article provides guidance for Product Managers who want to leverage AI but aren’t sure where to start. It will cover several areas where AI can accelerate PMs' performance, followed by a deep dive into how to use AI for data analysis in the customer experience space.

The first step in leveraging AI in the product development process is to identify where it can make the most significant impact. AI-powered tools can analyze customer feedback, streamline operations, and even predict future market trends. By assessing your current data and tool landscape, you can pinpoint where AI integration can offer the most value.

Leveraging AI for Customer Insights

Improving customer experience is the most profitable way for many organizations to use generative AI tools. According to McKinsey's Economic Potential of Generative AI report, companies stand to see a 30-45% increase in productivity and improve customer experiences using generative AI. 

Elsewhere on the blog, we’ve talked about creating AI-powered chatbots using LLMs. Here, we’ll explore how PMs can tackle the overwhelming amount of customer support data and feedback from users to improve and develop customer-centered products.

Enhancing your customer experience with personalization

Analyzing Customer Feedback with Generative AI

Generative AI models, powered by machine learning and large language models, offer unprecedented capabilities in understanding and processing vast amounts of customer data in real time. This technological advancement enables product teams to identify market trends, understand customer pain points, and enhance user experiences more efficiently than ever before.

Understanding customer feedback is crucial for refining products and enhancing user satisfaction. Generative AI, particularly when combined with Natural Language Processing (NLP) techniques, offers a powerful tool for extracting meaningful insights from diverse customer feedback channels. Here’s a step-by-step guide on how to effectively analyze customer feedback using AI:

Step 1: Aggregating Customer Feedback

Start by collecting feedback from all available sources. This includes support tickets, product reviews, customer surveys, social media comments, and forum discussions. The goal is to have a comprehensive dataset that represents the voice of your customer base.

  1. Support Tickets: Compile tickets from your customer support platform.

  2. Product Reviews: Gather reviews from your website, app stores, and any other platforms where your product is listed.

  3. Surveys: Collect responses from recent customer satisfaction or product feedback surveys.

  4. Social Media & Forums: Aggregate comments and discussions about your product from social media platforms and relevant forums.

Step 2: Preparing the Data

Consolidate the collected feedback into a structured format suitable for analysis. This might involve cleaning the data to remove irrelevant information, correcting typos, and standardizing formats to ensure consistency across different sources.

Step 3: Using NLP for Initial Analysis

Utilize NLP techniques to process the text data. This involves:

  1. Sentiment Analysis: Determine the overall sentiment (positive, negative, neutral) of each piece of feedback to gauge customer satisfaction.

  2. Keyword Extraction: Identify key terms and phrases frequently mentioned in the feedback to highlight common themes.

  3. Entity Recognition: Detect and categorize important entities such as product features, issues, or specific services mentioned by customers.

Step 4: Summarizing with Generative AI

Input the processed data into a generative AI model designed to summarize and highlight key issues. The model can be programmed to:

  1. Summarize Individual Threads: Provide concise summaries of long customer emails or support ticket threads, focusing on the main points and concerns raised.

  2. Aggregate Insights: Generate a report summarizing common themes and sentiments across all collected feedback, categorizing them into areas like product features, usability, customer service quality, etc.

Step 5: Identifying Key Issues and Opportunities

Analyze the AI-generated summaries to identify the most frequently mentioned pain points and areas for improvement. Look for patterns that suggest systemic issues or opportunities for significant enhancements.

Example: Analyzing Customer Support Emails

Imagine you have a month's worth of customer support emails that you want to analyze for common issues and themes. Here's how you could approach it using generative AI:

  1. Data Preparation: Compile all customer emails received over the last month into a single dataset. Ensure the data is clean and formatted consistently.

  2. NLP Processing: Use NLP to analyze each email, extracting key phrases, sentiment, and categorizing the type of issues mentioned (e.g., technical problems, billing inquiries, usability feedback).

  3. AI Summarization: Input the NLP-processed data into a generative AI model to generate summaries for each email thread. Configure the AI to focus on extracting the essence of the customer's problem or feedback.

  4. Aggregate Analysis: Have the AI model provide an overarching summary that highlights the most common issues, positive feedback, and suggestions for improvement based on the entire dataset.

  5. Review and Action: Review the AI-generated summaries and overarching analysis to identify the top priorities for action. This could include fixing prevalent bugs, addressing common usability concerns, or enhancing customer support for specific issues.

By following these steps, product teams can transform raw customer feedback into actionable insights, enabling data-driven decisions that enhance product development and customer experience. This methodical approach ensures that every piece of feedback is valued and considered in the continuous improvement of the product.

Other GenAI applications for PMs

Streamlining Idea Generation

Product teams can use generative AI to facilitate brainstorming sessions. Inputting the summarized data into an AI model can generate a range of product improvement ideas, encouraging creative thinking and innovation within the team.

Evaluating Product Concepts

AI-driven tools can also assist in evaluating the viability of new product concepts. By leveraging historical data and current market trends, generative AI models can predict the potential success of new ideas, allowing teams to prioritize those with the highest market fit and feasibility.

Optimizing Product Design and Development

In the realm of product design, AI technology offers tools for prototyping and user experience testing that can drastically reduce the time and resources required. Generative design AI, in particular, allows for the exploration of numerous design variations, optimizing for both aesthetics and functionality based on user feedback.

Accelerating Development with AI Automation

AI automation plays a pivotal role in accelerating the product development process. AI-driven project management tools can optimize workflows, and resource allocation, and even handle repetitive tasks, enabling the creative and strategic capabilities of product teams to flourish.

Enhancing Market Research

Generative AI models can also transform the way product teams conduct market research. By analyzing competitor activities, social media buzz, and other market indicators, AI tools provide a real-time overview of the landscape, helping teams to identify gaps and opportunities in the market trend swiftly.

Implementing AI Responsibly

While the benefits of integrating AI in product management are vast, it's crucial to approach its implementation responsibly. Ethical considerations, data privacy, security, and bias mitigation are paramount to ensure that AI-driven product development respects user rights and societal norms.

Further Resources

For PMs eager to delve deeper into leveraging AI, numerous tools, platforms, and educational resources are available to enrich your understanding and skills in applying AI within product management. Embracing AI is not just about keeping up with technology; it's about leading the charge in innovation and user-centric product development.

Check out Product School’s AI Product Certification to learn more about what you can do to accelerate AI readiness for you and your team.

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Updated: May 6, 2024

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