We implemented a radical redesign of Comodo’s landing page to address critical user friction points identified in our qualitative data analysis. As a result, we increased the visitor-to-lead conversion rate by 17.5% on a landing page that was already converting at a rate of 49.1% (!).
Our client is a leading cybersecurity service provider called Comodo, which has a 44% share of the SSL certificate market and operates on 13.9% of all web domains worldwide.
Our goal was to increase the visitor-to-lead conversion rate of Comodo’s most visited landing page, which offered a free forensic analysis to all users who signed up for the demo session. The challenging part of this CRO project was that the landing page was already converting at an amazing 49.1%.
The CRO of a landing page is primarily based on UX research and qualitative data from surveys and interviews. Landing pages are rarely able to analyze funnel or event maps that can provide CRO insights. So we rely on user feedback, user testing, video session recordings, and heat maps to provide data for landing page optimization.
We began gathering qualitative data by interviewing Comodo’s sales team.
Our goal was to get answers to the following questions:
What are the pain points and aspirations of Comodo’s prospects?
How do these pain points translate into Comodo’s value proposition?
What objections do users face in the buying process?
How is Comodo addressing these objections?
In the next phase of the research, we conducted an online survey of landing page visitors to determine the factors that drove the purchase decision.
The questions in the online survey were:
What were the top three factors that convinced you to sign up for the free demo?
What is the one thing that may have prevented you from signing up for the free trial?
What alternatives to Comodo are you considering and how do they compare?
| Research Findings | Conclusion | Hypothesis |
| Users don’t understand the value proposition of free forensic analysis | A short, one-screen interface is not enough to explain the value proposition of a technically complicated solution | Explaining the value proposition in a more user-friendly way will improve the perception of the solution |
| Complicated copy with technical language that is not clear to all user segments
|
Some potential buyers don’t have the technical knowledge to understand the technical aspects of the solution | By simplifying the copy and replacing technical terms with common terms, we will improve the understanding of the value proposition |
| Fear is a key purchase decision driver
|
Users are looking for cybersecurity solutions to alleviate the fear of being vulnerable to malware | Using a “Pain – More Pain – Hope – Solution – Call to Action” copywriting framework will alleviate fear and drive conversion |
| Common User Concerns and Objections to Purchase are not Copy or Design related
|
Many users are left with unanswered questions and therefore lack the confidence to convert | Identifying and addressing key user objections and concerns with copy and design will encourage conversion |
After conducting the user survey, we concluded that a significant portion of the user’s conversion friction was not being addressed by the current user interface. We identified the questions and objections that users had that were not being addressed.
We hypothesized that a radical redesign of the landing page that addressed key friction points and ambiguities would improve the UX and, as a result, increase the visitor-to-lead conversion rate.
We decided to simplify the communication of the value proposition. To do this, we used a ” Pain – More Pain – Hope – Solution – Call to Action” copywriting framework.
We created a new design and copy for the landing page that addressed all of the user’s key pain points and explained the solution’s value proposition in a more user-friendly way, without jargon.
To validate the hypothesis, we conducted an experiment where we compared the existing control variation of the landing page with the new alternative variation that we created based on our findings and hypothesis.
The experiment had the following characteristics:
Type of experiment: A/B test
Traffic split: 50/50
Device: Desktop
Key metric: visitor-to-lead conversion rate
Secondary metrics: user engagement (time on page, page scrolling depth, etc.)
Number of users who participated in the experiment 4,256
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