Using Data to Understand the User

When we started working with the Zoological Society of London (ZSL), their Head of Digital Services and the team were ready to look at innovative solutions to improve the user experience and increase conversion across the their website.

As a data-driven agency, we started this campaign initiative with their Google Analytics data. The brief from the client was vague, however we were determined to uncover insights which would enable us to create an impactful Conversion Rate Optimisation (CRO) campaign.   

The Experiment

Based on their website data, we uncovered that 73% of users landing on the homepage, navigated away from the ticketing journey. Notably, 18% of these users went on to try to find information about ticket pricing or opening times.  The interesting point here was that the eCommerce conversion for these users was 21% higher. 

What does this tell us? 

Visitors who see relevant ticketing information are much more likely to convert.  The problem on the homepage however was that the opening times and ticket pricing call-to-actions (CTA) were located below the fold, and there were no clear next-steps for users who were ready to buy tickets. 

It became obvious that the reason for the high drop-off rate from the ticketing journey was because there was no clear path for the user to follow.  This is where our campaign hypothesis was born; IF we add a module including a “Buy Tickets” CTA, as well as links to the most converted secondary pages, THEN it should increase the number of transactions by 10%, BECAUSE users who are ready to buy will have a clear action, and those that are still in the exploration process will have an easy way to find the information they are looking for.

The Results

We used their A/B testing platform, Google Optimize, and Google Analytics to analyse the results and saw:

From this experiment, we understood that the price information was crucial in the users' decision-making process and it increased their likelihood to convert.

It also seems that the users included in the variation, and those who visited the price page, were more aware of the Zoo pricing strategy and decided to buy the “cheapest option”, explaining why the Average Order Value (AOV) decreased by 9%.

The Iterations

You may be wondering why we’re telling out about how we dropped the AOV by 9%? Why are we telling you about our “failure”? But should we class a disproven hypothesis or a decrease in revenue as a failure?  We would argue the exact opposite, and say that in Conversion Rate Optimisation, there is no such thing as failure.  We did not fail, but in fact, we learnt something very important about our users.  Learning this vital piece of insight, about what users need before converting, fuelled further iterations on our experimentation efforts.  

As part of our next steps, we focused on increasing the average order value and streamlining the user journey.  One example of the iterations was on the Ticket Information page. Here, as you can see below, the original version does not include any information about the cost of the tickets. In the variation, we removed the masses of CTAs, highlighted the main Buy Tickets CTA, and added pricing information. These changes would aid in streamlining the journey, as users will not have to navigate away from the funnel to check the specific pricing page.  We also highlighted the Zoo’s value proposition in order to overcome the price pain point.

Sneak preview, the results were amazing! We saw an increase in both eCommerce conversion rates, as well as the precious AOV.