Build cost estimator - SelfBuild

Building your own House cost

Simple House Building / January 22, 2019

Amplitude 2.0 KPI DashboardAnyone starting a business has done their fair share of research and analysis. You researched your chosen market, ran financial projections, and figured out startup costs. This proves you’re an analytical guru, right?

That’s why it’s tempting to think you can continue to rely on spreadsheets to manage on-going analysis. Although this isn’t the “wrong” approach initially, as businesses grow, it gets harder and harder and harder to manage your analytics needs. Your time becomes more valuable and can’t be wasted trawling through ever more complex spreadsheets.

You can build a system in-house, but the time and money this takes aren’t worth the effort. You’ve got a SaaS billing system because you don’t want to—and can’t—process payments yourself, correct? The same thinking applies to analytics.

Here’s why you should buy rather than build when it comes to analytics.

Spreadsheets will only get you so far

Building your own analytics platform will quickly spiral out of control. The need to know more about who your customers are and what they’re doing will lead you down the proverbial rabbit hole. You’ll have wasted time and won’t be any further along in building your core business.

correlation-formulaLet’s see how this problem grows.

Customer tracking

It starts innocently. You launch your product and want to track new customers. Each new user gets their own row in your spreadsheet. You can track new customers month-over-month and come up with some easy to read graphs.

But what happens next? What do you do when you want to find out how these customers are really using your product, including:

  • The specific features they use
  • How often they use your product
  • What they do right before they churn

So you need to start tracking customers through every single page of your site. You’ll need to create, manage, and insert some kind of tracking code on every single page to get this information. Then you have to link it to your magical spreadsheet.

Take this snippet code from Amplitude as an example. To track what your customers are doing once they access your website, you’d install something like this for each action or event:


customer experience maturity modelIt’s not enough to know what actions are taking place, what makes your analytics more meaningful is know who is doing what. You can track this one of two ways:

  • If your customers have to log into your site, you can track them by installing this code:


If sign in isn’t required to use your site, you can track anonymous users by installing this code:

amplitude.getInstance.setUserId(null); // not string 'null' amplitude.getInstance.regenerateDeviceId;

With manual tracking, you have to figure out each individual customer yourself and track each and every one of their actions yourself. As you’ve probably guessed, there are endless actions you can track, which means things can get pretty complicated.

Graphing data

With all the information you collect, you decide you need a central location to track stats and run reports. You need a dashboard. Welcome to a new level of complexity. You have to correctly build in all the places it needs to pull in data from. A mistake could cost you thousands of dollars in missed opportunities.

With a custom dashboard you have to graph customer composition manually, so that you know who your audience is. Skipping this crucial step means you won’t have a clue who you’re targeting and what makes them stay. With a pre-built dashboard, such as the one above, this is taken care of for you. You can quickly see which customers make up the majority of your users.

While we’re on the topic of mistakes, let’s not forget that mistakes can go unnoticed in spreadsheets and result in you making decisions that could cost you and your business lots of money. Spending hours combing through data is a waste of your time and there’s still no guarantee that all the issues will be found.

Performing analysis

So far, so descriptive. But what about actual analysis like finding out why customers are retaining or churning from your app?

You want to know what features cause some customers to leave while others stay. You’ll also need to figure out the correlation between each of these features and how customers respond. Not so straightforward.