Paweł Huryn Profile picture
Oct 28, 2022 16 tweets 7 min read Read on X
Canva, Loom, Figma... Some Product-Led companies grow just like they were printing money!

Their secret? They are obsessed with minimizing the Time To Value (TTV).

What exactly is TTV and how can you benefit from it too? 🧵

#productmanagement #startup #GrowthHacking Image
TTV is the time before customers experience the value promised after 'purchasing' the product (e.g., a free trial or a freemium).

Let's take @canva as an example. You can use it immediately. You get sucked in and don't even notice that you've just become their customer! :) Image
You can easily minimize #TTV by using the Bowling Alley Framework. It's like using "bumpers" to guide users to the outcome your product promises.

There are two types of bumpers: Image
1. Product Bumpers

Their goal is to help adopt the product within the application.

a. Welcome Messages — displayed after logging in. It's an opportunity to greet users, make them feel invited, and restate the value proposition. They can also set expectations.
b. Product Tours — Eliminate distractions and allow users to focus only on the most critical options. It commonly starts with the question about what users would like to accomplish with the product. Let your users choose their adventure. Image
c. Progress Bars — Help users understand what's their progress. It's a good practice to start with a substantial percentage of the bar filled in so that users can feel that they are already underway instead of starting from scratch.

Example: #Kampassify Image
d. Checklists — Their goal is to break big tasks into smaller, achievable ones. They can motivate users to complete crucial setup steps.

Example: @productfruits Image
e. Onboarding Tooltips — Helpful messages are displayed when interacting with application elements (e.g., mouse hover). It shouldn't be too intrusive; e.g., forcing users to click every detail on a page may not be a good idea.

Example: @Medium Image
f. Empty states — After the first login, many applications are tedious. There is no data specific to you; without it, it's virtually impossible to understand what value you will get once you start using the product.
There are two ways:

a) Present users required steps and prompt them to take action.

b) Prevent it from happening. Dynamics 365 Sales populates every trial with sample data. I presented it at the beginning of this article.

Example: @RevolutApp Business Empty State Image
2. Conversational bumpers

Conversational bumpers work to educate the users, set their expectations, bring them back to the application, and eventually upgrade their accounts. I selected the two most popular forms:
a. User Onboarding Emails — can include welcome messages, usage tips, sales touch (to upgrade accounts), case studies, communicating the benefits, information about trial expiration, or post-trial surveys. You can easily automate most of them.

Example: @Medium Image
b. Explainer Videos — The name is self-explanatory. Videos can generate even 1200% more engagement than text and images. I highly recommend it, especially for complex products.

Example: @Azure Explainer Video

Minimizing TTV can significantly increase customer conversion and is not limited to Product-Growth companies.

Have you already checked it?

What are your thoughts?
If you want to know more, I highly recommend you read Product-Led Growth by @wes_bush

amzn.to/3fftGtA
If you like this thread, follow me on Twitter: @HurynPawel

You will also love my newsletter: huryn.substack.com

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More from @PawelHuryn

Jun 23
I copied the Multi-Agent Research System by @AnthropicAI.

Pure @n8n. No coding!

How Does it Work? 🧵
(1/14) Image
(2/14) There are 6 steps:

1. The user asks a question.
2. A Customer Support Agent clarifies the requests.Image
Image
(3/14) 3. A Lead Agent plans research tasks and calls Search Subagents. They work in parallel Image
Read 14 tweets
Jun 10
The AI PM in the US makes $182K/yr (Glassdoor). But AI isn’t nice to have anymore.

It might be a ticket to keeping the job.

So, where to start? 🧵 Image
Step 1: Quickly Get The Basic Terms (no coding)
Step 2: Lean by Doing, Not Theorizing (no coding)

Let's break it down:

Step 1: Get The Basic Terms

One of the key concepts is neural networks. You can quickly learn them using the TensorFlow Playground: playground[.]tensorflow[.]orgImage
Next, explore Large Language Models (LLMs) using this interactive visualization: bbycroft[.]net/llm Image
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May 31
The Ultimate AI PM Learning Roadmap

An extended edition with dozens of resources: definitions, courses, guides, reports, tools, and step-by-step tutorials 🧵 AI PM Learning Roadmap
1. Basic Concepts

Start with understanding "What an AI Product Manager is."

Next, for most PMs, it makes no sense to dive deep into statistics, Python, or loss functions. Instead, understand the basic definitions: Neural Networks, Transformers, and LLMs. Image
<important>
All links from this thread available for free, no paywall, at the end of the thread
</important>
Read 20 tweets
May 23
Claude 4 dropped 21 hours ago.

Turns out, it threatened to expose an engineer’s affair to avoid being shut down🧵 Image
Some might say it was just a test scenario. But the model didn’t know that.

It believed:
- It was about to be replaced
- The engineer leading the replacement was having an affair

There was no suggestion to blackmail, or manipulate anyone. The model chose blackmail on its own.
What’s hard to accept is that this wasn’t fixed. Claude chooses to blackmail in 84% of test runs.

Yet the Claude 4 family is being deployed globally like nothing happened:
- Best in the world for coding
- Equipped with improved memory
- Capable of pursuing its goals for hours
Read 7 tweets
May 14
AI PM is one of the hottest jobs on the market.
But what exactly does "AI PM" mean?

I like simple definitions: 🧵 Image
An AI PM is a PM who works on AI-powered products or features.
1. So, First, What is a Product Manager?

We often say that when the product fails, it's the PM's fault. But when it succeeds, it's thanks to the team effort.
Read 14 tweets
May 9
If I had to learn AI Product Management again, I would start here: 🧵 AI Product Management
1. Basic Concepts

For most PMs, it makes no sense to dive deep into statistics, Python, or loss functions.

You can find all practical concepts, like LLMs and Encoders, here: (link at the end of the thread)Image
Image
Image
2. Prompt Engineering

Best, free resources:

• GPT-4.1 Prompting Guide
• Anthropic prompt engineering
• Prompt engineering by Google
• Anthropic prompt generator
• Anthropic prompt library

(links at the end of the thread) Image
Read 12 tweets

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