A/B Testing

A/B Testing is a very useful exercise by which we can distribution variants of a piece of content and assess the success, defined by an arbitrary metric, for those variants.

Defining a Test

A/B Tests can be proposed via the "A/B Testing" issue template on the FlowForge Website repository. These issues are used to track findings. Results will be shared here from PostHog, and will provide a searchable source in the future if we want to reference historical A/B tests when proposiing new ideas that we may have already tried.

What are your variants?

  • Control: You must always define a control (A). This is generally whatever the state of the content/UI looks like now.

  • Test: You must then define at least one variant (B) of the content/UI.

What does success look like?

You must have a trackable metric that the variants can compete over. Example success metrics could be:

  • Clicks of a certain button, e.g. a primary or secondary CTA
  • $pageview events of a certain URL
  • Number of Instances spun up on FlowForge

How we run A/B Tests

We run our A/B Testing via PostHog Experiments. These are easily configured and setup in PostHog, and require that you answer the above questions in Defining a Test

Types of PostHog Experiments


If you are purely interested in the raw number of a particular event taking place, then you'll want a Trend Experiment, e.g:

Screenshot from PostHog showing a "Trend" Experiment


If you are wanting to measure an improvement in conversion, then a Funnel Experiment is the way to go. For example, if we want to increase the number of visitors clicking our primary CTA, then we could do a Trend graph, but that may also just show improvements as a result of raw web traffic improving. Making it a "Funnel" Experiment, ensures we have a clean analysis of the conversion to CTA for a visitor.

Screenshot from PostHog showing a "Funnel" Experiment

A/B Testing on our Website

To conduct a new Experiment:

  1. Create an Experiment in PostHog
  2. Define the criteria for which users you'd like involved, and the split across the variants Screenshot from PostHog showing a "Trend" Experiment
  3. Make note of the relevant Feature Flag created, and variants (e.g. control, testA, testB)
  4. Wherever you want to implement the variants, includes the following (equivalanet) code:
{% edge "liquid" %}
<h1 class="text-gray-50 max-w-lg m-auto">
{% abtesting "<feature-flag>", "control" %}
DevOps for Node-RED
{% endabtesting \%}
{% abtesting "<feature-flag>", "test" %}
Run Node-RED in Production
{% endabtesting %}
{% endedge %}

As a quick explanation of what's happening here:

  • edge: This tells Eleventy that we want to render this code server-side as a Netlify Edge Function
  • abtesting: This is an Eleventy Shortcode that will automaticlaly check PostHog feature flags & experiments
  • <feature-flag>: The Feature Flag defined for the experiment in PostHog.