Problems in General Analytics

Google Analytics, Mixpanel, and Optimizely are highly flexible, all-round analytics solutions. However, most of us want to use analytics for conversion optimization. Here's why a general solution isn't optimal.

Information overload

General analytics is not built for any specific purpose. Mixpanel, for example, is a general-purpose event tracker suitable for many different use-cases.

The all too familiar information overload

Use Mixpanel for anything from measuring how your users fair slaying digital monsters, to QAing a back-end fix, or monitoring and tweaking performance metrics.

From Mixpanel best practices

This might be a good thing, but not necessarily what you are looking for. Most websites are only interested on doing more sales so the only thing that they want is conversion optimization.

The way general analytics helps you with this mission is to provide endless amounts of data, visualized in all the imaginable ways:

The result: too many metrics, graphs, and opinions to make confident decisions. No clear goal to strive for.

Missing insights

Conversion optimization is all about fixing your weak spots — over and over again. However, general data model is not designed for this task.

  1. No leading indicators — not measuring the important details what people do before they convert, essentially making the optimization impossible.
  2. No bottlenecks — not knowing the “low-hanging fruits” where you make the biggest wins with the lowest possible effort
Measurement is fabulous. Unless you're busy measuring what's easy to measure as opposed to what's important.

Seth Godin

A/B testing has serious issues

Traditional A/B testing is rather trivial: you make a small change to a web page and see how it impacts a single metric on some time range. Then you wait for months to get the results and you are expected to update your site during that time.

4,400+ visitors and 20 days was needed to show that the two variants were actually identical 🤷
Many businesses would be better off if they didn’t run any A/B tests at all.

David Kadavy

Author of “Design for Hackers”

Damaged visitor experience

Traditional A/B testing solutions are absurldy large pieces of software. For example, Optimizely is 200 times larger than Volument. That's right:

Optimizely is 200x larger than Volument

The more JavaScript you have, the slower your pages, and the less convincing is your first impression.

Moreover, these visual A/B testing tools tamper the first impression with a flaky FOUC effect: the original page is briefly displayed before the alternative appears.

These UX problems cause more people to leave your site even before they become aware of the product.

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