Startup entrepreneurs assume what risks when pitching?

Last week I signed four NDAs.

Four!

NDA stands for non-disclosure agreement. A legal document given to someone receiving confidential information. By signing, the person pledges not to share the information for a certain time. If they do, they may be in breach of contract and face legal action.

Companies use NDAs to protect trade secrets and confidential internal information from employees and contractors. Appropriate. If you manage a huge, successful firm, you don't want your employees selling their information to your competitors. To be true, business NDAs don't always prevent corporate espionage, but they usually make employees and contractors think twice before sharing.

I understand employee and contractor NDAs, but I wasn't asked to sign one. I counsel entrepreneurs, thus the NDAs I signed last week were from startups that wanted my feedback on their concepts.

I’m not a startup investor. I give startup guidance online. Despite that, four entrepreneurs thought their company ideas were so important they wanted me to sign a generically written legal form they probably acquired from a shady, spam-filled legal templates website before we could chat.

False. One company tried to get me to sign their NDA a few days after our conversation. I gently rejected, but their tenacity encouraged me. I considered sending retroactive NDAs to everyone I've ever talked to about one of my startups in case they establish a successful company based on something I said.

Two of the other three NDAs were from nearly identical companies. Good thing I didn't sign an NDA for the first one, else they may have sued me for talking to the second one as though I control the firms people pitch me.

I wasn't talking to the fourth NDA company. Instead, I received an unsolicited email from someone who wanted comments on their fundraising pitch deck but required me to sign an NDA before sending it.

That's right, before I could read a random Internet stranger's unsolicited pitch deck, I had to sign his NDA, potentially limiting my ability to discuss what was in it.

You should understand. Advisors, mentors, investors, etc. talk to hundreds of businesses each year. They cannot manage all the companies they deal with, thus they cannot risk legal trouble by talking to someone. Well, if I signed NDAs for all the startups I spoke with, half of the 300+ articles I've written on Medium over the past several years could get me sued into the next century because I've undoubtedly addressed topics in my articles that I discussed with them.

The four NDAs I received last week are part of a recent trend of entrepreneurs sending out NDAs before meetings, despite the practical and legal issues. They act like asking someone to sign away their right to talk about all they see and hear in a day is as straightforward as asking for a glass of water.

Given this inflow of NDAs, I wanted to briefly remind entrepreneurs reading this blog about the merits and cons of requesting investors (or others in the startup ecosystem) to sign your NDA.

Benefits of having investors sign your NDA include:

None. Zero. Nothing.

Disadvantages of requesting investor NDAs:

• You'll come off as an amateur who has no idea what it takes to launch a successful firm.

• Investors won't trust you with their money since you appear to be a complete amateur.

• Printing NDAs will be a waste of paper because no genuine entrepreneur will ever sign one.

I apologize for missing any cons. Please leave your remarks.

Niches are rich, as the adage goes.

Micro SaaS requires a great micro-niche; otherwise, it's merely plain old SaaS with a large audience.

Instead of targeting broad markets with few identifying qualities, specialise down to a micro-niche. How would you target these users?

Better go tiny. You'll locate and engage new consumers more readily and serve them better with a customized solution.

Imagine you're a real estate lawyer looking for a case management solution. Because it's so specific to you, you'd be lured to this link:

instead of below:

Next, locate mini SaaS niches that could work for you. You're not yet looking at the problems/solutions in these areas, merely shortlisting them.

The market should be growing, not shrinking

We shouldn't design apps for a declining niche. We intend to target stable or growing niches for the next 5 to 10 years.

If it's a developing market, you may be able to claim a stake early. You must balance this strategy with safer, longer-established niches (accountancy, law, health, etc).

First Micro SaaS apps I designed were for Merch By Amazon creators, a burgeoning niche. I found this niche when searching for passive income.

Graphic designers and entrepreneurs post their art to Amazon to sell on clothes. When Amazon sells their design, they get a royalty. Since 2015, this platform and specialty have grown dramatically.

Amazon doesn't publicize the amount of creators on the platform, but it's possible to approximate by looking at Facebook groups, Reddit channels, etc.

I could see the community growing week by week, with new members joining. Merch was an up-and-coming niche, and designers made money when their designs sold. All I had to do was create tools that let designers focus on making bestselling designs.

Look at the Google Trends graph below to see how this niche has evolved and when I released my apps and resigned my job.

Are the users able to afford the tools?

Who's your average user? Consumer or business? Is your solution budgeted?

If they're students, you'll struggle to convince them to subscribe to your study-system app (ahead of video games and beer).

Let's imagine you designed a Shopify plugin that emails customers when a product is restocked. If your plugin just needs 5 product sales a month to justify its cost, everyone wins (just be mindful that one day Shopify could potentially re-create your plugins functionality within its core offering making your app redundant ).

Do specialized users buy tools? If so, that's comforting. If not, you'd better have a compelling value proposition for your end customer if you're the first.

This should include how much time or money your program can save or make the user.

Are you able to understand the Micro SaaS market?

Ideally, you're already familiar about the industry/niche. Maybe you're fixing a challenge from your day job or freelance work.

If not, evaluate how long it would take to learn the niche's users. Health & Fitness is easier to relate to and understand than hedge fund derivatives trading.

Competing in these complex (and profitable) fields might offer you an edge.

B2C, B2M, or B2B?

Consider your user base's demographics. Will you target businesses, consumers, or both? Let's examine the different consumer types:

• B2B refers to business-to-business transactions where customers are other businesses. UpVoty, Plutio, Slingshot, Salesforce, Atlassian, and Hubspot are a few examples of SaaS, ranging from Micro SaaS to SaaS.

• Business to Consumer (B2C), in which your clients are people who buy things. For instance, Duolingo, Canva, and Nomad List.

• For instance, my tool KDP Wizard has a mixed user base of publishing enterprises and also entrepreneurial consumers selling low-content books on Amazon. This is a case of business to many (B2M), where your users are a mixture of businesses and consumers. There is a large SaaS called Dropbox that offers both personal and business plans.

Targeting a B2B vs. B2C niche is very different. The sales cycle differs.

• A B2B sales staff must make cold calls to potential clients' companies. Long sales, legal, and contractual conversations are typically required for each business to get the go-ahead. The cost of obtaining a new customer is substantially more than it is for B2C, despite the fact that the recurring fees are significantly higher.

• Since there is typically only one individual making the purchasing decision, B2C signups are virtually always self-service with reduced recurring fees. Since there is typically no outbound sales staff in B2C, acquisition costs are significantly lower than in B2B.

User Characteristics for B2B vs. B2C

Consider where your niche's users congregate if you don't already have a presence there.

B2B users frequent LinkedIn and Twitter. B2C users are on Facebook/Instagram/Reddit/Twitter, etc.

Churn is higher in B2C because consumers haven't gone through all the hoops of a B2B sale. Consumers are more unpredictable than businesses since they let their bank cards exceed limitations or don't update them when they expire.

With a B2B solution, there's a contractual arrangement and the firm will pay the subscription as long as they need it.

Depending on how you feel about the above (sales team vs. income vs. churn vs. targeting), you'll know which niches to pursue.

You ought to respect potential customers.

Would you hang out with customers?

You'll connect with users at conferences (in-person or virtual), webinars, seminars, screenshares, Facebook groups, emails, support calls, support tickets, etc.

If talking to a niche's user base makes you shudder, you're in for a tough road. Whether they're demanding or dull, avoid them if possible.

Merch users are mostly graphic designers, side hustlers, and entrepreneurs. These laid-back users embrace technologies that assist develop their Merch business.

I discovered there was only one annual conference for this specialty, held in Seattle, USA. I decided to organize a conference for UK/European Merch designers, despite never having done so before.

Hosting a conference for over 80 people was stressful, and it turned out to be much bigger than expected, with attendees from the US, Europe, and the UK.

I met many specialized users, built relationships, gained trust, and picked their brains in person. Many of the attendees were already Merch Wizard users, so hearing their feedback and ideas for future features was invaluable.

focused and specific

Instead of building for a generic, hard-to-reach market, target a specific group.

I liken it to fishing in a little, hidden pond. This small pond has only one species of fish, so you learn what bait it likes. Contrast that with trawling for hours to catch as many fish as possible, even if some aren't what you want.

In the case management scenario, it's difficult to target leads because several niches could use the app. Where do your potential customers hang out? Your generic solution: No.

It's easier to join a community of Real Estate Lawyers and see if your software can answer their pain points.

My Success with Micro SaaS

In my case, my Micro SaaS apps have been my chrome extensions. Since I launched them, they've earned me an average $10k MRR, allowing me to quit my lousy full-time job years ago.

I sold my apps after scaling them for a life-changing lump amount. Since then, I've helped unfulfilled software developers escape the 9-5 through Micro SaaS.

Whether it's a profitable side hustle or a liferaft to quit their job and become their own Micro SaaS boss.

Having built my apps to the point where I could quit my job, then scaled and sold them, I feel I can share my skills with software developers worldwide.

Read my free guide on self-funded SaaS to discover more about Micro SaaS, or download your own copy. 12 chapters cover everything from Idea to Exit.

Watch my YouTube video to learn how to construct a Micro SaaS app in 10 steps.

Weekend project: limitless design service. It was fun to see whether I could start a business quickly.

I use no-code apps to save time and resources.

TL;DR I started a business utilizing EditorX for my website, Notion for client project management, and a few favors to finish my portfolio.

First step: research (Day 1)

I got this concept from a Kimp Instagram ad. The Minimalist Hustler Daily newsletter mentioned a similar and cheaper service (Graphically).

I Googled other unlimited design companies. Many provide different costs and services. Some supplied solely graphic design, web development, or copywriting.

Step 2: Brainstorming (Day 1)

I did something simple.

• What benefits and services to provide

• Price to charge

Since it's a one-person performance (for now), I'm focusing on graphic design. I can charge less.

So I don't overwhelm myself and can accommodate budget-conscious clientele.

Step 3: Construction (Day 1 & 2)

This project includes a management tool, a website, and a team procedure.

I built a project management tool and flow first. Once I had the flow and a Notion board, I tested it with design volunteers. They fake-designed while I built the website.

Tool for Project Management

I modified a Notion template. My goal is to keep clients and designers happy.

Team Approach

My sister, my partner, and I kept this business lean. I tweaked the Notion board to make the process smooth. By the end of Sunday, I’d say it’s perfect!

Website

I created the website after they finished the fake design demands. EditorX's drag-and-drop builder attracted me. I didn't need to learn code, and there are templates.

I used a template wireframe.

This project's hardest aspect is developing the site. It's my first time using EditorX and I'm no developer.

People answer all your inquiries in a large community forum.

As a first-time user developing a site in two days, I think I performed OK. Here's the site for feedback.

4th step: testing (Day 2)

Testing is frustrating because it works or doesn't. My testing day was split in two.

• testing the workflow from payment to onboarding to the website

• the demand being tested

It's working so far. If someone gets the trial, they can request design work.

I've gotten a couple of inquiries about demand. I’ll be working with them as a start.

Completion

Finally! I built my side project in one weekend. It's too early to tell if this is successful. I liked that I didn't squander months of resources testing out an idea.

He kept it under wraps for years until he legally couldn’t.

His shirt is incomplete. I can’t stop thinking about this…

Actually, ignore the article. Look at it. JUST LOOK at it… It’s quite disturbing, isn’t it?

Ughh…

Me: “Hey, what up?” Friend: “All good, watching lord of the rings on amazon prime video.” Me: “Oh, do you know how Amazon grew and became famous?” Friend: “Geek alert…Can I just watch in peace?” Me: “But… Bezos?” Friend: “Let it go, just let it go…”

I can question you, the reader, and start answering instantly without his consent. This far.

Reader, how did Amazon succeed? You'll say, Of course, it was an internet bookstore, then it sold everything.

Mistaken. They moved from zero to one because of this. How did they get from one to thousand? AWS-some. Understand? It's geeky and lame. If not, I'll explain my geekiness.

Over an extended period of time, Amazon was not profitable.

Business basics. You want customers if you own a bakery, right?

Well, 100 clients per day order $5 cheesecakes (because cheesecakes are awesome.)

$5 x 100 consumers x 30 days Equals $15,000 monthly revenue. You proudly work here.

Now you have to pay the barista (unless ChatGPT is doing it haha? Nope..)

• The barista is requesting $5000 a month.

• Each cheesecake costs the cheesecake maker $2.5 ($2.5 × 100 x 30 = $7500).

• The monthly cost of running your bakery, including power, is about $5000.

Assume no extra charges. Your operating costs are $17,500.

Just $15,000? You have income but no profit. You might make money selling coffee with your cheesecake next month.

Is losing money bad? You're broke. Losing money. It's bad for financial statements.

It's almost a business ultimatum. Most startups fail. Amazon took nine years.

I'm reading Amazon Unbound: Jeff Bezos and the Creation of a Global Empire to comprehend how a company has a $1 trillion market cap.

Many things made Amazon big. The book claims that Bezos and Amazon kept a specific product secret for a long period.

Clouds above the bald head.

In 2006, Bezos started a cloud computing initiative. They believed many firms like Snapchat would pay for reliable servers.

In 2006, cloud computing was not what it is today. I'll simplify. 2006 had no iPhone.

Bezos invested in Amazon Web Services (AWS) without disclosing its revenue. That's permitted till a certain degree.

Google and Microsoft would realize Amazon is heavily investing in this market and worry.

Bezos anticipated high demand for this product. Microsoft built its cloud in 2010, and Google in 2008.

If you managed Google or Microsoft, you wouldn't know how much Amazon makes from their cloud computing service. It's enough. Yet, Amazon is an internet store, so they'll focus on that.

All but Bezos were wrong.

Time to come clean now.

They revealed AWS revenue in 2015. Two things were apparent:

1. Bezos made the proper decision to bet on the cloud and keep it a secret.

2. In this race, Amazon is in the lead.

They continued. Let me list some AWS users today.

• Netflix

• Airbnb

• Twitch

More. Amazon was unprofitable for nine years, remember? This article's main graph.

AWS accounted for 74% of Amazon's profit in 2021. This 74% might not exist if they hadn't invested in AWS.

Bring this with you home.

Amazon predated AWS. Yet, it helped the giant reach $1 trillion. Bezos' secrecy? Perhaps, until a time machine is invented (they might host the time machine software on AWS, though.)

Without AWS, Amazon would have been profitable but unimpressive. They may have invested in anything else that would have returned more (like crypto? No? Ok.)

Bezos has business flaws. His success. His failures include:

• introducing the Fire Phone and suffering a $170 million loss.

• Amazon's failure in China In 2011, Amazon had a about 15% market share in China. 2019 saw a decrease of about 1%.

• not offering a higher price to persuade the creator of Netflix to sell the company to him. He offered a rather reasonable $15 million in his proposal. But what if he had offered $30 million instead (Amazon had over $100 million in revenue at the time)? He might have owned Netflix, which has a $156 billion market valuation (and saved billions rather than invest in Amazon Prime Video).

Some he could control. Some were uncontrollable. Nonetheless, every action he made in the foregoing circumstances led him to invest in AWS.

Don't say this in public!

Want to wow your audience? Want to deliver a successful presentation? Do you want practical takeaways from your presentation?

Then avoid these phrases.

Public speaking is difficult. People fear public speaking, according to research.

"Public speaking is people's biggest fear, according to studies. Number two is death. "Sounds right?" — Comedian Jerry Seinfeld

Yes, public speaking is scary. These words and phrases will make your presentation harder.

Using unnecessary words can weaken your message.

You may have prepared well for your presentation and feel confident. During your presentation, you may freeze up. You may blank or forget.

Effective delivery is even more important than skillful public speaking.

Here are 10 presentation pitfalls.

1. I or Me

Presentations are about the audience, not you. Replace "I or me" with "you, we, or us." Focus on your audience. Reward them with expertise and intriguing views about your issue.

Serve your audience actionable items during your presentation, and you'll do well. Your audience will have a harder time listening and engaging if you're self-centered.

2. Sorry if/for

Your presentation is fine. These phrases make you sound insecure and unprepared. Don't pressure the audience to tell you not to apologize. Your audience should focus on your presentation and essential messages.

3. Excuse the Eye Chart, or This slide's busy

Why add this slide if you're utilizing these phrases? If you don't like this slide, change it before presenting. After the presentation, extra data can be provided.

Don't apologize for unclear slides. Hide or delete a broken PowerPoint slide. If so, divide your message into multiple slides or remove the "business" slide.

4. Sorry I'm Nervous

Some think expressing yourself will win over the audience. Nerves are horrible. Even public speakers are nervous.

Nerves aren't noticeable. What's the point? Let the audience judge your nervousness. Please don't make this obvious.

5. I'm not a speaker or I've never done this before.

These phrases destroy credibility. People won't listen and will check their phones or computers.

Why present if you use these phrases?

Good speakers aren't necessarily public speakers. Be confident in what you say. When you're confident, many people will like your presentation.

6. Our Key Differentiators Are

Overused term. It's widely utilized. This seems "salesy," and your "important differentiators" are probably like a competitor's.

This statement has been diluted; say, "what makes us different is..."

7. Next Slide

Many slides or stories? Your presentation needs transitions. They help your viewers understand your argument.

You didn't transition well when you said "next slide." Think about organic transitions.

8. I Didn’t Have Enough Time, or I’m Running Out of Time

The phrase "I didn't have enough time" implies that you didn't care about your presentation. This shows the viewers you rushed and didn't care.

Saying "I'm out of time" shows poor time management. It means you didn't rehearse enough and plan your time well.

9. I've been asked to speak on

This phrase is used to emphasize your importance. This phrase conveys conceit.

When you say this sentence, you tell others you're intelligent, skilled, and appealing. Don't utilize this term; focus on your topic.

10. Moving On, or All I Have

These phrases don't consider your transitions or presentation's end. People recall a presentation's beginning and end.

How you end your discussion affects how people remember it. You must end your presentation strongly and use natural transitions.

Conclusion

10 phrases to avoid in a presentation. I or me, sorry if or sorry for, pardon the Eye Chart or this busy slide, forgive me if I appear worried, or I'm really nervous, and I'm not good at public speaking, I'm not a speaker, or I've never done this before.

Please don't use these phrases: next slide, I didn't have enough time, I've been asked to speak about, or that's all I have.

We shouldn't make public speaking more difficult than it is. We shouldn't exacerbate a difficult issue. Better public speakers avoid these words and phrases.

“Remember not only to say the right thing in the right place, but far more difficult still, to leave unsaid the wrong thing at the tempting moment.” — Benjamin Franklin, Founding Father

This is a summary. See the original post here.

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

• It is erratic.

• It has a significant impact.

• Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

• Every action is a procedure.

• Meetings are a medium of work

• Manage short-term goals in accordance with long-term strategies.

• Mission-oriented teams accelerate while functional teams increase leverage.

• Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

• Select the ideal market for your products.

• Connect an audience to the value of your goods right away.

• Take use of three positioning philosophies.

• Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

• Determine the optimal course of action for your ML project.

• Create software that is more effective than people.

• Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

• Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.