Starting a SaaS or micro-SaaS business can be challenging, especially when it comes to creating a landing page. As I recently discovered when building AI Feedback Pro, creating a landing page to validate customer interest can be a costly venture. However, I found a solution that cost just $19/year plus the cost of the domain, without sacrificing the essential features.

In this post, I’d like to share my experience and provide you with a list of components and services that will help you build an effective landing page on a budget. Whether you’re just starting out or looking for cost-saving measures, this guide is for you.

Just the list

Where the price isn’t mentioned - it is free on the landing page scale

• Landing page service: Carrd on Pro Standard plan for $19/year, can be used for 10 sites
  • Runner-up: EarlyBird.im for $8/month
• Theme: Carrd’s own template #95 free for Pro Plan users
  • Runners-up: template #106 also free and SaaSy template for $29
• Mailing list to subscribe interested people: EmailOctopus
• Cookieless Analytics: PanelBear for your first users, GoatCounter not too loose the track if landing page is suddenly successful
  • Runners-up: Clicky and Couner.dev
• Surveys: Tally.so
• Illustrations: Undraw + some PlaygroundAI modifications when you want to have something similar, but a little different
  • Runners-up: just google for “undraw alternatives” - there are lots of illustration services free for commercial use
• Logo: Namecheap LogoMaker
  • Runners-up: LogoAI for inspiration if you are fine with recreating it in SVG, BrandCrowd for $8 or TailorBrands for 8€
• Email service: Gandi, but you’ll need to buy domain from them in order to get it for free
• Support chat: Crisp chat
• Bonus: Your service name: ChatGPT as a discussion partner

Now to the details.

How to choose the appropriate level of statistical significance for an AB-test

Nowadays a lot of product managers have to confirm most of their decisions with AB-tests. Yet, it is far not always clear how to choose the parameters for the test. A particularly difficult parameter to tune is often the level of statistical significance. If we choose too high level - tests will fail even though improvements do exist. If we choose too low level - we’ll be getting lots of “confirmations” of false improvements.

When we make decisions based on AB-tests, once in a while we’ll be making mistakes. We can limit the losses caused by such mistakes by choosing the appropriate level of statistical significance.

Signup with the phone number

Imagine that you are a product manager in a company running a typical web service.

You are working on the ways for increasing the amount of signups. You examine the signup process steps and notice that many visitors stop when they need to enter their email address. So you decide to try asking for a phone number instead.

Using phone number for identifying the users is not exactly a widely used practice yet and comes with the need to pay for a text message for activation. However, nowadays people aren’t that much into email anymore and do everything on the phone - so possibly phone number-based signups have the potential for bringing in more users.

VS