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

Jul 29
Everyone in AI is talking about Context Engineering.

But just a few explain what the context is.

Save this template. It captures all scenarios and will help you maximize agents' performance: 🧵👇 Image
1. Instructions

Define:
→ Who: Encourage an LLM to act as a persona
→ Why is it important (motivation, larger goal, business value)
→ What are we trying to achieve (desired outcomes, deliverables, success criteria)

💡Providing strategic context beyond raw task specification improves AI autonomy arXiv:2401.04729
2. Requirements (How)

Define:
→ Steps to take (reasoning, tasks, actions)
→ Conventions (style/tone, coding rules, system-design)
→ Constraints (performance, security, test coverage, regulatory)
→ Response format (JSON, XML, plain text)
→ Examples (positive/negative, responses/behaviors)

💡Negative examples might help you address issues identified during error analysis
Read 10 tweets
Jul 28
Context engineering is the new prompt engineering.

And it’s becoming the most critical AI skill.

Together with @MiqJ (Product Lead at @OpenAI) we created a comprehensive guide.

Key insights: 🧵👇 Image
1. What Is Context Engineering

It is the art and science of building systems that fill LLM context window to improve their performance.

Unlike prompt engineering, context engineering is a broader term with many activities that happen also before the prompt is even created. Image
2. Types of Context

There are 6 types of context:
- Instructions
- Examples
- Knowledge
- Memory
- Tool results
- Tools Image
Read 11 tweets
Jul 25
I spent 12 hours testing 9 LLMs for building AI agents:

- You might easily save up to 83% on costs.
- Reasoning models are not the best.
- Autonomy break fast. A real moat is orchestration.

Here's everything you need to know: 🧵 Image
The task assigned to agents:

- Create a new list inside Kanban (1 Trello board available)
- Search the web to find the recent news about Amazon
- Add all the search results to the Kanban listImage
Completing this task required:

- Preparing a simple plan
- Using multiple tools in the right order
- Adjusting the plan if anything goes wrong
- Operating autonomously (the system prompt gave no clues about the process)

One of many tests (@n8n_io):
Read 12 tweets
Jul 15
RAG is the most critical part of context management in AI.

But doing it right is tough.

I created a free, interactive simulator that visualizes different variants: 🧵
1. Vanilla RAG

The simplest form of RAG that combines retrieval with generation in a straightforward pipeline. Image
2. Standard RAG

Enhanced RAG with query rewriting and reranking for improved retrieval quality. Image
Read 10 tweets
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
Read 9 tweets

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