What is FigPii Recommendations?
FigPii Recommendations gives you a prioritised set of specific, actionable improvements to make to your website each month, based on how your own visitors behave on it.
It's built for companies that want to improve conversion rate but don't have a dedicated CRO team to analyse the data and decide what to work on.
How it works
From the moment the FigPii tracking code is installed, we analyse what your visitors actually do what they click, which sections they engage with, and how they move through your site.
The engine identifies the behaviours that lead to conversions, then compares those sessions against ones that didn't convert. That contrast is what surfaces which elements are helping people convert and where visitors are dropping away.
Every recommendation is then reviewed by a person. Our conversion rate specialists go through the set the engine produces and prioritise the ones most worth acting on right now, for your site specifically. So you get the pattern-detection of automated analysis combined with the judgement of a CRO specialist, rather than a list of generic best practices.
What you receive
At entry level you'll receive 2 to 3 recommendations per month, with the number depending on your visitor volume.
Recommendations are delivered monthly, so your site improves incrementally rather than through one large redesign.
Requirements
- At least 1,000 visitors per month. Below that there isn't enough behavioural data to produce reliable recommendations.
- The FigPii tracking code installed on your site. See How do I install FigPii tracking code on my site?
- A plan that includes Recommendations. Check the packages page for availability on your plan.
Site type doesn't matter — ecommerce, lead generation and SaaS sites all work, since the analysis is based on your visitors' actual behaviour rather than assumptions about your industry.
Getting the most out of it
Run heatmaps and session recordings regularly. They feed the analysis and give you the context behind each recommendation.
A/B test the recommendations rather than simply implementing them. Testing tells you whether a change actually worked on your site, and the results feed back into the engine, so the recommendations you receive get better the more you validate them.