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SaaS and Data Science, a Powerful Combination for Testing Side Projects and Understanding…

SaaS and Data Science, a Powerful Combination for Testing Side Projects and Understanding Monetization

Source: Lucas Amunategui
It sets up a monetization framework that is simple to follow and easy to replicate from project to project…

This is a match made in heaven for us weekend entrepreneurs and our burgeoning data science prototyping projects!

Risk, Cost, and Worry-Free

The idea of being a tech entrepreneur is great but taking that leap can be intimidating. What if there was a way of doing it without “leaping”? Here is a gentle way of launching side projects with minimal risk and at your pace. It can also be fun, especially if you don’t take it too seriously and start looking at failure as a positive nudge to move on to the next idea.

What is SaaS?

Software as a service (Saas) uses a subscription delivery model instead of selling a product outright. Most software companies are going there. It’s an attractive model for both sides of the fence. The cost of entry is cheaper than having to buy the software outright (think under $100 for Office 365 versus a couple $100s it used to cost for the outright purchase). It also ensures that you always have the latest version. For businesses, it creates revenue streams and allows the deployment of updates and new features with less formality.

For the ML entrepreneur, it’s a perfect model. It gives the weekend warrior the ability to showcase work to his or her network. But also formalizes the discovery and sales funnel in a way that empowers those with little experience or time to worry about the nuances of software production. Just slap a subscription or donation button to your page, and there it is, monetized! A service like Stripe will handle all the sensitive data, encryption, banking transactions, refunds, and even yield irrefutable financial analytics.

Start looking at failure as a positive nudge to move on to the next idea…

Why SaaS and Data Science?

  • It builds a monetization framework that is simple to follow and easy to replicate from project to project (at times as simple as moving a button from one page to the next).
  • It gives you an aiming mechanism to focus on and compare the impact of your work on different niches and groups.
  • It puts crystal-clear financial analytics within your reach to quickly gauge interest, success, long-term viability.

What to Build?

Here comes the fun part. We’re talking about data science here, so your product either includes a model or analytics or both and will produce an output as a web page or a rest-API source. Data science is your edge over the competition, that’s how you can differentiate yourself over others!

Either way, we need to build a web application (nothing scary here as its easy, cheap and, in some cases, free). And if you don’t have a business idea, don’t worry, think about what you can automate or simplify, think about the services you pay for, think about the pain points in your business or hobbies. We’re all very lazy, so any tool you can come up with that saves us time is something you can monetize.

A great place to start is to focus on a problem in your own backyard, in your niche, and automate it, simplify it or offer analytics around it.

If that doesn’t work, start roaming message boards and aggregation sites like Reddit, Hacker News, Facebook, and fish for business ideas…

My focus is on data science as it is still relatively a ‘low hanging fruit’, here are some ideas:

  • Information aggregation (stock market, news, social sites, sports, media, books, etc)
  • Custom data science feeds — sentiment index, the number of posts, rising or lowering traffic counts around particular topics, trends, etc (see this incredible WSJ article to get a taste of this type of data: Your Smartphone’s Location Data Is Worth Big Money to Wall Street).
  • Rest API funnels — data transformation services, risk management, compliance, audits, etc
  • NLP and crawlers (plenty of potentials there)
  • Sale funnels — automated email reminders, customer research, outreach, lead analysis, etc.
  • Wordpress rest API-based plugins, analytic tools
  • Custom trackers, a/b testing, traffic alerts
  • Online document processing, real-time site stats, and analytics (see a post I did a while back on mining Hacker News: Big Data Surveillance: Use EC2, PostgreSQL and Python to Download all Hacker News Data!
  • Lead generation

A Free Platform

Start with something simple. You’re better off breaking big ideas into multiple small projects and test them out individually.

This is the simplest part of this pipeline, I built an entirely free course on the topic of launching smart web applications using free tools: How to Create and Sell Your Machine Learning Product Online and For Free!

And For inspiration

For some inspiration, great post on Indie Hackers: “How to Come Up with Profitable Online Business Ideas”

Note: for those interested in learning applied data science, check out my new free eBook and course: