ChatGPT Series - Episode 2
In continuation to the ChatGPT series, in the previous article, we have had discussed about "how product managers can leverage it's presence" [article link], herein, we are moving further in the series and talking about possible use cases and applicability which a product people and product management team can bring in their day-to-day work.
Introduction
Based on my prompting experience, I am sharing this article which is focused to bring in various possible use-cases as per my experiences to explore the potential of generative AI in the enterprise product management. Generative AI is emerging as a buzzword, moreover, it is a powerful tool that can assist product people operative within the large organizations.
While writing and sharing this article, I aimed to provide a comprehensive overview of the couple of scenarios / ways generative AI can be utilised by product people in enterprise orgs.
Let's create some scenarios which we as a product people see on daily basis and then, move further.
Stages of the Product Life Cycle
In our product life cycle stages, we work with various teams and perform various activities that help us to work towards improvement, growth, customer experience etc.
In the era before 2012, enterprise product organizations were creating user guide and documentations to assist their enterprise users. Sometimes, user guide documentation were part of the compliance.
Eventually, Rolling back to 2013-17, we [Teams and I] had started using Prompting, to work for user-guides, chat-support development, sales-interaction using NLP-NLU and ML models etc. helped us to work for our products / solutions as part of the product for enterprise organization.
Now, let's create some scenarios which we as a product people see on daily basis and then, move further:
Customer Support
Real-time assistance to support
Issues reported by users - error handling and troubleshooting
Feedbacks
Surveys
Coding
Onboarding and Tutorials for new users
Product Management Team(s)
Engineering Team
Marketing
Sales
We will NOT include Code-based usability in this article. Code-based application usabilities as - code generation, code completion, code review, bug fixing, code refactoring, code style checking.
Where does Product Management People spend more time on a daily basis?
Let me bring areas where as a Product Management person I was collaborating and coordinating with:
Idea Generation
Customer Engagement Analysis
Market Segmentation Analysis
Product Optimisation
Working with Sales people
Working with GROWTH Team
Working with Marketing folks
Working with Customer Success Team
Working with Customer Support team
How a Product Management Team can leverage Generative UI in an enterprise organizations?
In this section, I am sharing areas which we can use as Product Capability people in the product management space:
Idea Generation and Concept Development - Considering vast amount of market data, consumer preferences, user behaviour, application users touch points and trends. With the help of Generative AI, and by leveraging machine learning techniques, we can generate product ideas where Machine Learning concepts can help us to identify pattern and generate concepts that may assist product teams to explore new possibilities.
Design Team can use Generative AI to generate design variations based on specified criteria that will enable designers to explore multiple options quickly and iterate on different layouts, colour schemes or visual elements.
Rapid Prototyping - Generative UI tools can quickly generate interactive prototypes based on design specifications that will help product managers / product people to visualise and envision various interface concepts.
Design Exploration - By using Generative AI based tools, it can generate multiple design variations based on predefined parameters or user preferences. Eventually, Product Managers can work with design team to use these generated designs to explore different layout options, colour schemes or visual elements.
Analysis from Customer Support Data - We all know that, customer support data from L1 to L3, is the ocean of data that may assist a product manager / team to know about the challenges, queries, real-time challenges a user / application user foes through while using your product / services / solution as a product.
By using machine learning and generative AI based algorithms, it will help us to know about failure and success triggers promptly and effectively with a desired efficacy, gradually.
Let's take a short example of "Credit Card Support" team and what kind of data, trends etc. we can get using Generative AI:
Fraud Detection and Prevention: Generative AI algorithms can analyze transaction data, customer behavior patterns, and historical fraud cases to identify potential fraudulent activities. By learning from past instances, the AI system can generate insights and patterns that help detect suspicious transactions more accurately. It can also generate alerts for the support team to investigate further, reducing the risk of fraudulent activities and improving customer security. Customer Self-Service: As per me, Self-service needs a deeper sense of pattern and usage behaviour of the users. We can use generative AI that can assist in developing self-service options for customers, reducing the need for direct support interactions [although companies are using now prompt engineering concepts as chat bots, trained bots based on NLP / NLU understanding etc.]. By analyzing frequently asked questions, support ticket data, and customer behavior, the AI system can generate automated responses or provide relevant resources to assist customers in resolving common inquiries or issues. This reduces support team workload and empowers customers with quick and accessible solutions.
We can use generative AI that can assist in developing self-service options for customers, reducing the need for direct support interactions [although companies are using now prompt engineering concepts as chat bots, trained bots based on NLP / NLU understanding etc.].
Personalised Customer Support: Generative AI algorithms can analyze customer profiles, transaction history, and support interactions to personalize the support experience. The AI system can generate insights on individual customer preferences, such as preferred communication channels or specific support requirements. This allows the support team to tailor their interactions, provide relevant information, and offer personalized solutions, enhancing customer satisfaction and loyalty.
Customer Satisfaction Analysis: We can use algorithm techniques and leverage Generative AI that can analyze customer feedback data, such as survey responses, customer complaints, and support tickets, to generate insights on customer satisfaction.
As a Product Person, I will be more focused to get assistance from the AI system to know about the common pain points, recurring issues, and sentiment analysis from customer interactions. This information can help the product and relevant team to identify areas for improvement, prioritize problem resolution, and enhance the overall customer experience.
Details about Banking Products and Tutorials: Although, we had used prompting and have seen various examples of prompting using chatbots, say as an example - Order, Track order, Refund status, Shipment status, raising complaint etc., we can use generative AI and Incorporate ChatGPT to assist users during the searching behaviour, functional behaviour or when they need help with specific features. The chatbot can guide users step-by-step, answer their questions, and provide interactive tutorials, making it easier for users to get started and understand the application.
Feedback and Surveys in Conversational Way: We as product people can leverage ChatGPT to gather user feedback and conduct surveys in a conversational manner. The chatbot can ask targeted questions, capture user sentiment, and collect valuable insights to improve the UX based on user input.
About the Author
Anubhav Sinha is a Co-founder as well as the course developer of the Product Capability Uplift. In this role, Anubhav leads the development of the PMA as well as works as the product thinker of the Product Capability Uplift PMA.
Anubhav Sinha is a product coach, a product management practitioner and technology product geek with around one and half decade of the product management and development experience that ranges widely in the B2B and B2IB product space. He is known for contributing and creating products majorly in the start-up space, helping start-ups in their early stages and contributing industry product organisations as user-experience flow optimiser. He had served industry as Principal Product Owner [co-founder], Product and Design Thinking Coach, Product Owner and Transformation Coach.
Anubhav holds a Post-Graduation in Marketing - IB and Bachelor of Engineering in Electrical and Electronics.
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