Conversion optimization

The problems in general analytics

Google Analytics, Mixpanel, and Optimizely are highly flexible, all-round solutions. However, they are too generic for conversion optimization. Here's the problem.

No goal

General analytics is not built for any specific purpose. There's just tons of data without priority. You have too many metrics, graphs, and opinions to make confident decisions, and there is no goal to work for.

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

Missing or broken metrics

The general data model is not designed to solve all the unique demands in conversion optimization.

  1. No bottlenecks — not knowing the “low-hanging fruits” where you make the biggest wins with the lowest possible effort
  2. No leading indicators — not measuring the details on how people gradually build interest and desire before taking action.
  3. No north star metric — not striving for maximum visitor lifetime value.

Worse yet, many of the vital metrics are broken.

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 trivial: you are limited to small changes on a single page and see how they impact a single, temporary metric. Moreover, you must wait for months to get the results and you must stop developing the site while waiting.

There is so much more you can do to optimize your site.

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”

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All problems in traditional A/B testing

Why traditional A/B testing is slow

Optimizely is 200x larger than Volument

Damaged visitor experience

The more JavaScript you have, the slower your pages, and the less convincing are your first impression. Moreover, all the visual A/B testing tools tamper the first impression with a flaky FOOC 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.

TIP — Use Volument to A/B test how much more traction you get after removing that dense tracking script.

Learn Volument