We attended the Women In Product conference recently and here are some key learnings. We also shared some resources obtained from the conference for the benefit of our readers.
The main takeaways are:
- Always start with the customer
- Intersection of AI/ML and Product Management
Always start with the customer:
The biggest take-away from the conference for me was the importance of starting with your customers, and how it can be broadly applied to more than just the product that you are managing.
In my favorite session Presenting to Senior Leaders by Susannah Baldwin, Ph.D., she shared how it can be applied to presentations. If we treat our Presentations as our products, then the way we prepare and deliver our presentation should all center around our audience. The first step to prepare for a presentation is to ask ourselves what our audience wants, and not what we know/want to talk about.
I naturally start each presentation with an agenda because it’s easy. However, knowing my audience has a short attention span and has very limited time, it’s much more powerful to start with an executive summary that quickly gets to the bottom line. Similarly, it’s easy to make a lot of slides with all the information that I know, it’s much harder but also more powerful to think exactly what the audience needs/wants to know and reduce slides to only 10, not 35.
Due to my fear of public speaking, I usually want to deliver the presentation as fast as possible, ideally without interruptions. However, if I start with my audience, especially if my audience are senior leaders, I would try to leave room for discussion because this way they can seek out any additional information that they need to make a decision.
The other areas we can apply this product thinking and start with our customers include the job market and our day-to-day work.
In Day 2’s keynote by phyl terry, he talked about treating ourselves as a product and the importance of establishing candidate-market fit, similar to how we try to find product-market fit.
In the session by Prajakta Joshi, she talked about being a good report. We spend a lot of time discussing what makes a great manager but we rarely talk about how we can also reduce friction for our managers in our work relationship.
Focus on the customer has also been echoed by Ami Vora in her keynote. She also suggested some tactical suggestions to learn more about our customers. These include
1) Search for reviews and instructional videos, 2) Look at internal customer support tickets, 3) Try to use your product in another language, and 4) Test how fast you can complete the most important task.
Intersection of AI/ML and product management:
AI is highly talked about in the media and the tech industry currently, thanks to chatGPT. Three takeaways on AI product management are:
(1) Embrace AI, don’t fear it. AI can automate monotonous parts of a PM job such as some project management tasks, writing/editing articles, reading long documents. This way, PMs will have more time to focus on product intuition and strategic thinking
(2) In large companies, data research precedes product development lifecycle and it’s the PM’s job to balance research quality and user value. Compared to a consumer tech PM, AI PMs sometimes have to balance more expensive resources such as research scientists and GPU costs
(3) Although Large Language Models (LLMs) such as chatGPT are gaining popularity now, AI/ML PMs have existed for a while in business areas such as Trust & Safety, Ranking , Recommendations and Fintech risk prediction to name a few. Depending on the business area and the layer of the technology you’re interested in ( application vs data layer) you might not need deep ML technical skills to break into AI product management.
On Product Management:
- Build by Tony Fadell
- Outcome over output by Josh Seiden
- Women, Language, and Power by Susannah Baldwin