Welcome to the Tech for Non-Techies podcast

How To Validate Your Product

It's so tempting to dive into making a product the moment you have an idea. But, this is a sure way to lose time and money.

Before speaking to developers or designers, take a deep dive into the users and the problem you are aiming to solve. This is an ideal opportunity for non-techies to shine: user research and idea validation.

Listen to this episode to learn from Ronan Walsh, founder of Digital Trawler, a digital marketing agency, about how he helps his SaaS clients create better products.

Learning notes from this episode:

  • A product is a solution to a people problem. Before even contacting a product team, take time to investigate the people and the problem.
  • Marketers and designers go through a very similar process to validate the user problem at the start. This is because both are very user focussed.
  • User research is done in two ways: quantitative and qualitative. For qualitative research be careful not to use steering questions to feed answers to the research...
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Apple vs Android: An Introduction To The App Econom‪y‬

Worldwide spending on Google Play and the Apple App store is projected to reach $171 billion in 2024.

The opportunity in making apps and investing in them is huge, but what exactly is the app economy? Before you get started, you need to know the key differences between the Apple and Android ecosystems.

They cater to different audiences and make money in different ways. Listen to this episode to learn why.

Learning notes from this episode:

  • While there are more apps on the Google Play store, Apple apps account for 70% of all revenue generated on Apple and Google combined.
  • Apple users are wealthier than Android users. The median iPhone app user earns $85,000 per year, which is 40% more than the median Android phone user.
  • Most apps on Apple monetize via subscriptions and in-app purchases, while Android apps make money via advertising.

 

To join Sophia's FREE masterclass on mid March 2021 on Break Into Tech: Masterclass for Non-Technical Foundersregister here.

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How To Hire Product Teams: A Guide For Non-Technical Founders

In this episode, Sophia Matveeva covers how to hire product teams to make your tech idea come to life. As a non-technical founder, you will not be able to supervise developers directly, but it doesn't mean you need to rely on a CTO co-founder either.

If you have an idea for a tech based business and want to know how to hire the right people to build it, this episode is for you.

Learning notes from this episode: 

  • Work with designers first: your first hire should be a user experience designer, not a developer.
  • You don't need a CTO co-founder, but you do need a technical lead or advisor.
  • Work with a product studio, rather than freelance developers or a development shop at the start.

If you want to delve deeper, then check out the ebook: How to Hire Your Product Team & Go From Idea To App: Guide For Non-Technical Founders.

In the e-book you will get:

  • A list of product studios recommended by non-technical founders including a firm I’ve worked with
  • A...
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How I Built A Fintech AI Business As A Non-Technical Founder

Sophia Matveeva spoke to Jung Seok Kung (JS) founder of Aizen, a fintech company which uses AI to support decision making and manage risk for banks. JS is a non-technical founder, who now leads a company that processes 10,000+ algorithms in real time.

If you want to learn what AI is in practice and how it's changing business this episode is for you.

We cover how JS went from spotting a market opportunity to creating an algorithm using a spreadsheet, and the ups and downs of entrepreneurship.

Learning notes from this episode:

  • An algorithm is just a set of instructions that you put into a computer.
  • JS created a prototype of his algorithm using a spreadsheet. The aim of his algorithm was to give out loans to customers. He bought lending data from the Lending Club and tested it against a control group. This means one set of loan applications was assessed by the algorithm and the other was done manually. By doing this, JS proved that his algorithm was making correct decisions....
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What Is A Product?

When was the last time you said to yourself "today, I really want to use a glass?" Probably, never.

Yet, you've used a glass many times to solve the problem of bringing a liquid to your mouth.

In the context of technology, it’s so easy to think of a product as an app, or a site or an algorithm. But, that’s actually the backwards way of doing it.

Every product is a solution to a problem experienced by a human.

For example, if you want to take a sip of water, or this delicious looking cocktail, you use a glass. You don’t wake up thinking “today is a day I get to use a glass! Yey!”

If you do, then send me whatever you put in your cornflakes!

We can actually take this definition further and see an entire company as a solution to a problem. This is basically why companies get acquired: either because they make a solution or they are the solution.

This is the logic Walmart used when it acquired Jet.com for $3.3bn in 2016 to compete with Amazon.

By expanding the...

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What Developers Do: Front End vs Back End

tech terms explained Feb 03, 2021

In this episode Sophia covers two tech terms you'll hear if you build apps and sites: the back end and the front end. Most apps and sites need to have both to function.

Listen to this episode for a simple non-techie explanation of the back end and front end, what they do and who should build them. This is a useful episode for non-technical founders, product managers, designers and investors.

Learning notes:

  • Developers write computer code to make the design come alive. The vast majority of apps will need to have a front end and a back end.
  • Front end developers create computers that interact with humans. If you can see it, touch it or speak to it, it is a front end. 

  • Back end developers create computers that interact with other computers.

When you are working with a product team, the work cycle will go as follows:

  1. Designers create your prototype
  2. Back end developers build the back end
  3. Front end developers build the front end

 

 

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How To Transition Into Venture Capital

A career in venture capital is lucrative and exciting. Yet, it is also hard to get into, and once you're in, staying successful isn't easy.

Sophia Matveeva spoke to Silicon Valley venture investor Nilesh Trivedi principal investor at J Ventures and Chicago Booth MBA.

They talked about what makes a good VC, the different pathways into venture capital and why many early stage VCs aren't as rich as you think. This is an especially useful episode for angel investors, VCs and founders.

Learning notes:

  • Good tech investors have an understanding of technology, but they do not have to have technical backgrounds. Learning how apps, sites and algorithms get built is a key skill, but learning to code is not.
  • Venture capitalists are investors in startups, but they also have to raise money from investors. Investors that invest into VC funds are called Limited Partners, or LPs.
  • VCs have to spend time growing their brand and network because the competition to invest in good startups is fierce.
  • ...
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How to Transition into Product Management for Non-Techies

Product Management is one of the best ways for non-technical professionals to join the tech boom. But, making a career transition is hard and often you need experience to get experience.

For this episode, Sophia Matveeva spoke to Juliet Eysenck, Product Manager at The Telegraph Media Group. Juliet began her career in journalism and then transitioned into a tech role.

Juliet shared what the PM job entails and her very practical tips to help you transition into tech. 

 Learning notes:

  • Product managers define "the what, the when and the why of the product," - what the product is, what the priorities are within the product and why it needs to exist.
  • Product managers do not need to code, but taking some basic coding courses can help product managers work better with developers.
  • Constant learning is a feature of a good product manager's job: learn via industry newsletters, online courses and books.
  • To make a career transition, don't only do courses, but also get some practical...
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The Business of AI with Harvard Business School Prof Marco Iansiti

ai and big data Dec 16, 2020

Harvard Business School professor of AI and digital innovation Marco Iansiti talks about what AI is, how it gets made and how it impacts organisations. Listen to get a case study on Ant Financial and learn how AI adoption has accelerated during COVID.

 

Key learning points:

  • AI is already changing industries and the economy by making simple operations run faster
  • A business model is how a company aims to create and capture value
  • An operating model is how the company delivers that value to the customer. Prof Iansiti calls this “the hard part”
  • AI is already being used by companies like Ant Financial to take humans and cost out of their operating model, by putting an AI factory in the core of their business
  • COVID has accelerated the adoption of AI across industries

 

 

Listen here on Apple Podcasts

Listen here on Spotify.

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Do you have a brilliant app idea and no tech knowledge to build it? Get your FREE guide here.

 

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Intro to Algorithms and Big Data

In this episode tech entrepreneur and Chicago Booth MBA Sophia Matveeva covers what algorithms are, how they are made and when rubbish data creates rubbish algorithms. Hear how Amazon’s recruiting algorithm experiment went horribly wrong.

Don't let the term algorithm scare you. It's just another word for a set of rules.

Key learning points:

  • Algorithms are just sets of rules for computers to follow
  • Algorithms need data to be useful
  • Data is by definition historic because it is information on what has already happened
  • A combination of AI and algorithms can create better user experiences, which can result in more usage, which then results in even more data and happy customers
  • Algorithms amplify the data. If there are biases in the data, they will grow as a result of the algorithm. Rubbish data = rubbish algorithms = useless product

 

 

Listen here on Apple Podcasts

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