What I learnt from Competing in a PM Challenge

The fastest-growing online services markets you haven't heard of, Redesigning a Natural Language AI search feature, and other lessons

Last week, I had the exciting opportunity to participate in a PM Challenge and am proud to announce that I emerged as one of the winners 🏆 out of 36 participants.

Even with three years of experience successfully launching and iterating a B2B data platform, I believe in the continuous development of my product management skills. Participating in case study challenges is an excellent way to achieve this.

Here are four valuable lessons I learned from the experience.

• Lesson 1: The Fastest-Growing Online Services Markets You Haven’t Heard Of

• Lesson 2: How to Improve Usability of Natural Language Search

• Lesson 3: How Much Time It Takes to Create an Entry for a PM Challenge

• Lesson 4: The Most Efficient Way to Improve Product Skills

What’s a PM Challenge?

A PM Challenge is a regular contest organised by PMSchool.io for their students and external PMs to practice their product skills by solving real-life product problems. It's free to enter, and winners can receive prizes like discounts for PMSchool courses.

Case Problem: Topmate.io

Topmate.io is a scheduling and appointment booking platform that enables expert mentors to connect with their audience and monetise their time via paid 1: calls. As Chief Product Officer, your task is to address user feedback and improve the mentee experience. Issues include:

• An unintuitive user experience

• Issues finding the right mentors

• Poor customer support in the case of mentor fraud

Disclaimer: I have no affiliation with PM School or Topmate.

Lesson 1: The Fastest-Growing Online Services Markets You Haven’t Heard Of

Participating in a PM Challenge is a hands-on way to learn about products and industries outside your professional experience. Acting as the CPO makes you feel instantly invested in the company.

Through industry and competitive analysis, I learnt about three rapidly growing industries that Topmate participates in:

• Online mentor matching services: Projected to become a multi-billion dollar industry by 2032, growing at a CAGR of 19.6%. (source)

• Creator economy (India only): Projected to become a 3.9 billion USD market by 2030, growing at CAGR of 22% (source)

• Appointment scheduling software: Projected to grow into a 1.5 billion USD market by 2032 at a CAGR of 16% (source)

These insights opened my eyes to the potential of these markets and have prepared me to network with professionals and explore job opportunities in these industries.

Lesson 2: How to Improve Usability of Natural Language Search

I don’t come across Natural Language search often in consumer-facing apps, especially for searching structured data. So I was intrigued by Topmate’s “AI Search”, which allows users to input descriptive queries and get back a list of suitable mentors.

The goal of this AI Search tool is to increase activation and monetisation among mentee users by encouraging them to make more bookings.

Here's a closer look:

Imagine I’m an aspiring software engineer seeking help to pass a Google interview.

Cool “AI Search” button (top right).

But who is the “AI Search” for?

Ok, the feature itself communicates that it’s meant for the mentees who want to connect with experts.

Let’s go ahead and search for “Software engineers working in Google to help me with interview prep”.

Hmm ok, the first expert is an Eng Lead at Google, who might be able to help me. But the 2nd and 3rd experts don’t seem to match my query.

Why did you show me these search results? Are they relevant or are they LLM hallucinations?

Pain Points of the Current Feature:

• Unclear target user for “AI Search” button

• Relevance of search results is confusing or inconsistent

The AI Search tool should:

• Communicate its purpose and benefits to the mentee user persona

• Provide relevant and accurate search results

User-Centric Redesign: Wireframes

Improvement 1: CTA button clearly states the purpose of the search

Improvements 2 & 3: State the features of each expert that make them relevant to search query and list the expert’s most relevant service(s)

Key Improvements

• Clear CTA button: Increase click-through rate to the tool

• Feature relevance: State the features that make each mentor relevant to the search query

• Service listing: List the mentor’s most relevant services

My Thoughts:

Search engines capable of understanding users’ natural language queries (such as “data scientists with experience working on AI/ML projects at Asian e-commerce companies”) have the potential to supercharge the degree of personalisation, engagement, and conversion in any platform or marketplace. It also enables businesses to show the most relevant products to new users without the cold-start problem and represents a massive business opportunity.

However, search with natural language capabilities (whether or not powered by Generative AI) is much more expensive than traditional search and indexing of structured datasets, both to setup and to operate. That makes it even more crucial to get the user experience right, so that the value delivered justifies the cost of investment.

We will likely see this sort of AI search for complex queries emerge on platforms that see a lot of high-value transactions, such as professional recruiting, real estate, and B2B sales.

Consumers may not be able to search Amazon for “witty slow-burn romance novels with a happy ending set in any Asian country” anytime soon, but perhaps they will be able to search PropertyGuru for “2-bedroom apartments with a large kitchen, no west sun and within a 10-minute walk to a yoga studio” to discover their next home!

Lesson 3: How Much Time It Takes to Create an Entry for a PM Challenge

Creating the deliverables for a PM Challenge requires significant effort. Be prepared to invest at least eight hours into the process, including industry research, brainstorming solutions, creating wireframes, creating the slide deck, and iterating based on feedback.

Lesson 4: The Most Efficient Way to Improve Product Skills

Feedback I got from the PM School judges

While the feedback from the PM School judges was insightful, it lacked the depth I needed for targeted improvement. For personalised guidance on enhancing your product skills, consider:

• Taking live courses with mentor interactions (e.g., PM School Flagship course)

• Organising group peer feedback sessions within product communities

• Engaging a mentor for 1:1 mentorship sessions (e.g., ADPList, MentorCruise, Topmate)

In my case, this experience actually inspired me to seek out a Product Management mentor for feedback. Stay tuned for my takeaways in a future post!

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Have you ever competed in a PM Challenge or a similar competition? What were your key takeaways? Also, I'm curious about your thoughts on my proposed redesigns for the AI search tool. Do you see value in these changes? Share your thoughts and experiences in the comments!