Product Management


Kanal geosi va tili: ko‘rsatilmagan, Inglizcha


Let’s build truly excellent products.

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Here's a tip to become a great Product Manager.


Build something approach.


Find a problem, chat with 10-20 people about it, just ask questions, and organize all the feedback. I prefer to use the Opportunity Solution Tree to visualize my thinking and the best path to my desired outcome.

Now, use some cool no-code like Bubble.io, WordPress, or AI tools and create a small product yourself. Maybe even a landing page to bring the idea to life and invite some users to get to the waiting list if they see the value in your product before it even goes live.

Keep it simple and share it with the people you talked about it before and gather their thoughts again.

You can also get some users from your social media, ProductHunt, Startup Stash, etc

Repeat this process with a few more ideas! Don’t give up if people don’t see the value. It’s part of the learning process.

You will know:
- How to evaluate your product without spending a lot of time and money on it.
- How to do user interviews like a pro.
- How to test your idea or build MVP without a team of engineers.

Trust me, practice is the key. It's easier to learn by actively creating products than by just listening to podcasts or reading cool books without taking any action.

hashtag#product hashtag#productmanagement hashtag#productdevelopment hashtag#productmanager


Product team cases where a #productteams improved content discovery

Case: Netflix and Personalized Content Recommendations

Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.

Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.

Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.

Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).





Case: Spotify and Music Discovery

Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.

Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.

Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).


Let's talk AI basics - Prompt Engineering vs. Blind Prompting

Do you want to get the most out of AI? There are two main approaches: prompt engineering and blind prompting.

Prompt engineering is a technique where you carefully craft a prompt that guides the Large language models (LLM) to generate the desired output. For example, if you want the LLM to write a list of places to go with kids, you might give it a prompt like "Create a list of 10 #ai free outdoor places I can go for a day with kids in New York"

Blind prompting is a technique where you give the LLM a very general prompt and let it generate whatever it wants. For example, you might give the LLM the prompt "Things To Do In New York With Kids"

So, which approach is better?

Prompt engineering is a more reliable way to get the LLM to generate high-quality output. Blind prompting can sometimes produce surprising and interesting results, but it is also more likely to produce gibberish or unrelated content.
However, blind prompting can be a more creative process than prompt engineering. It can be fun to see what the LLM will come up with when you give it a very open-ended prompt.

The best approach for using AIs depends on your goals and preferences. If you want to be sure that your output is high-quality, then prompt engineering is the way to go. If you want to experiment and see what the LLM can do, then blind prompting can be a lot of fun.

Here's one of the great tools to supercharge your AI skills with prompt templates. https://lnkd.in/gXyFYEgj #ai #productmanagement #pm #engineering #productdevelopment


Product management tools & tech stack

Tools for capturing qualitative user feedback

▪ Zoom and Chorus.ai for conducting interviews
▪ Slack, Google Docs, or Notion for making notes of key points and action items
▪ Zendesk and Intercom for meeting customers where they are
▪ Saleforce for better understanding the needs and pain points of prospects

Tools for capturing quantitative feedback
▪ FullStory records how people flow through your product
▪ Amplitude and Mixpanel help you dive deep into product analytics.
▪ SurveyMonkey allows you to measure your NPS score, get a sense of product/market fit, and understand how happy/unhappy people are with your product. 

Tools for validating the problem
▪ Miro enables virtual whiteboarding to communicate your ideas visually.
▪ Loom lets you record a screen capture along with audio, making it easy to walk people through your thinking

Product management tools for exploring the solution space
Tools for ideating solutions

▪  Figma lets you create interactive prototypes
Tools for prioritizing your work
▪ Productboard helps you decide which projects and features to move forward with, what sort of timeline to address them in, and how to keep stakeholders in the loop.




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Huge company, slim margins

Few industries are as competitive as making and selling cars — and Ford's financials are a prime example of this. Despite being a giant, Ford ekes out a slender profit margin. Since 2012 Ford's average net profit margin has averaged just 2.7%. With revenues regularly north of $150bn, that still translates into a sizeable profit of $4bn a year (on average) — but it doesn't leave much room for error if sales fall, costs rise, or both.






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Marketer's daily dan repost
The Telegram advertising market is about to start blowing

Pavel Durov announced the launch of Telegram's own advertising platform that will be based not on collecting the data from users, but on the channels' topics – users' interests. And is it said in today's visionary WSJ article, “Telegram intends to demonstrate that precise, context-based advertising is not only an ethical alternative to targeted advertising, but can be just as efficient,”, but I think we shouldn't hesitate about it and should predict that it's going to be *more effective.

The Telegram advertising market in Russia is already more than $200M. And it is scaling worldwide besides the official marketing platform is not launched yet.

Native ads can be incredibly powerful and it also requires many native-ads-specialists hence brings people a big opportunity in the next years.


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Hey family! Thank you for following my group here. I'm so thankful for each of you! Its a great pleasure to share some tips and insights with you here.

I want to stay closer with each of you and welcome you to my personal Instagram profile - https://www.instagram.com/thesanex
I share some parenting tips and family travel ideas. Connect with me there!

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