{"id":1938,"date":"2016-05-25T06:00:04","date_gmt":"2016-05-25T05:00:04","guid":{"rendered":"https:\/\/uxstudioteam.com\/website\/?p=1938"},"modified":"2022-05-19T14:34:55","modified_gmt":"2022-05-19T13:34:55","slug":"save-thousands-of-euros-using-ux-research-by-getting-people-to-do-tests-in-exchange-for-chocolate","status":"publish","type":"post","link":"https:\/\/timemachine.uxstudioteam.com\/ux-blog\/save-thousands-of-euros-using-ux-research-by-getting-people-to-do-tests-in-exchange-for-chocolate\/","title":{"rendered":"Save thousands of Euros using UX research, by getting people to do tests in exchange for chocolate"},"content":{"rendered":"

\"\"What do you do when you need to decide between\u00a0two design solutions? A\/B testing can be one of the first research<\/a> methods that\u00a0comes to your mind. But in some cases, a poorly prepared A\/B test can lose you\u00a0thousands of crispy euros every\u00a0day. <\/span><\/p>\n

Liligo<\/a> <\/span>is a search engine<\/span>\u00a0that specialises in travel product comparisons, allowing millions of users to find the cheapest price for their journeys. We are currently working with Liligo to design an interface for their app to make it easier for users to choose from 100s of flight options, and motivate them to buy tickets on Liligo’s partners’ sites.<\/span><\/p>\n

<\/p>\n

We all know that choice can be an attractive thing, but as\u00a0Sheena Iyengar<\/a>\u00a0has discussed, when users are faced with too many options they don’t know what to do, and\u00a0are burdened by the responsibility of distinguishing good decisions from bad.<\/span><\/p>\n

\"jams\"<\/p>\n

Extensive choices do not necessarily lead to enhanced motivation when there are too many\u00a0options, or substantial time and effort is\u00a0required for users to make truly informed comparisons between\u00a0alternatives. <\/span><\/p>\n

So, our UX company<\/a> decided to\u00a0improve Liligo’s business performance using\u00a0a transparent option list which has enough information to help users\u00a0decide and raise the conversion rate. In this case study I am concentrating on only\u00a0one phase of the user journey, the moment when users have already chosen their trip and need a clear summary of their\u00a0selection.<\/span><\/p>\n

That is what the original version looked like:\"Screenshot_2016-04-04-15-38-00\"<\/p>\n

We always create\u00a02 or 3 different solutions for a problem, and test it on real\u00a0people.<\/p>\n

Version A: Tabs:\u00a0We separated the outbound and return travel information \u00a0into two tabs.<\/span><\/p>\n

\"Screenshot_2016-03-24-16-08-17\"Screenshot_2016-03-24-16-08-24<\/p>\n

Version B: Scrolling:\u00a0We put the outbound and return travel information on the same\u00a0page and let users scroll up and down.<\/span><\/p>\n

\"Screenshot_2016-03-24-16-16-25 \"Screenshot_2016-03-24-16-16-30<\/p>\n

Both versions looked good to us but we wanted to know which worked\u00a0better for users. As I am sure you will be aware, a\u00a0two-week A\/B testing period with millions of users can cause thousands of euros to be lost if one version does not work well and users cannot find what they are looking for. We had to find a quick and cheap solution to prepare appropriate A\/B test versions. <\/span><\/p>\n

Guerilla\u00a0tests proved to be the best solution in\u00a0this case because:<\/span><\/p>\n