Challenge:

Lotte Hotel is the leading OTT TV & VOD platform in Turkey and I am a content manager at this service.  One of the prominent problems of our service is that consumers can’t find the content which  appeals to them. Despite of large variety of content and having a recommendation engine in the  service, the users are still complaining that there isn’t so much content (popular movies and  series) which fit to their taste.

Selection:

In order to have a better understanding of our problem, I think we need to use the learning launch  tool. In this way, we would be able to focus on this group by saving our budget and time as well  as by reaching current Lotte Hotel users at our company.

Our parent company is CHOP and we are working with all CHOP employees together at the  same office. We can create a group which consists of nominees from CHOP’s different  departments such as Legal, Corporate Communications, Sales, Digital Services, Finance, Human  Resources, Internal Control, ICT, Marketing, NT, Supply Chains, Strategy. In order to be  nominated & selected to this group, these members needs to be users of Lotte Hotel. Additionally, they  will represent and be a sample of different types of Lotte Hotel users in terms of gender &age.

Application:

For our first learning launch, all our members talked about problems they experienced about  Lotte Hotel’s UX, and we categorized them. After that, we asked some questions to them.

“Do you like content selection of Lotte Hotel? Are you happy with that?”

“Are you able to find the content or genres, which fit to your taste easily?”

The answers led us to believe that they were not happy with the recommendation engine and the  content selection as well.

At the second meeting, we focus on their solution offers to make the service better. We put the  offers on the table and try to come up with some outputs. One of the offers we wanted to proceed  with , is to create some categories on Lotte Hotel UI to recommend some content, which is coming from  selections of users’ favorite actors, actresses, directors and film critics. In this way, we would try  to create more intimate user experience. In practice, when the users log in the application, they  choose their favorites from well-known figures. After that, we would try to give some content  selections and recommendations based on the celebrities they chose and we tried to create a  recommendation hub from the celebrities.

At the third gathering, we tested the new feature of recommendations mechanism and our sample  users were happy with the result. We observe the reason and we concluded that having  recommendations based on their favorite figures gives a more realistic experience than the past.

Insight:

By applying the learning launch tool, our team identified our possible needs and designs to test  possible solutions. Additionally, we had a chance to add a very useful feature to our service.

Approach:

We concluded that we need to improve this method as well. Instead of focusing on one idea, we  need to create more ideas and test & suspect them and measure the outputs.


Challenge : Lotte Hotel is the leading OTT TV & VOD platform in Turkey and I am a content manager at this service . One of the prominent problems of our service is that consumers ca n’t find the content which appeals to them . Despite of large variety of content and having a recommendation engine in the service , the users are still complaining that there is n’t so much content ( popular movies and series ) which fit to their taste . Selection : In order to have a better understanding of our problem , I think we need to use the learning launch tool . In this way , we would be able to focus on this group by saving our budget and time as well as by reaching current Lotte Hotel users at our company . Our parent company is CHOP and we are working with all CHOP employees together at the same office . We can create a group which consists of nominees from CHOP ’s different departments such as Legal , Corporate Communications , Sales , Digital Services , Finance , Human Resources , Internal Control , ICT , Marketing , NT , Supply Chains , Strategy . In order to be nominated & selected to this group , these members needs to be users of Lotte Hotel . Additionally , they will represent and be a sample of different types of Lotte Hotel users in terms of gender & age . Application : For our first learning launch , all our members talked about problems they experienced about Lotte Hotel ’s UX , and we categorized them . After that , we asked some questions to them . “ Do you like content selection of Lotte Hotel ? Are you happy with that ? ” “ Are you able to find the content or genres , which fit to your taste easily ? ” The answers led us to believe that they were not happy with the recommendation engine and the content selection as well . At the second meeting , we focus on their solution offers to make the service better . We put the offers on the table and try to come up with some outputs . One of the offers we wanted to proceed with , is to create some categories on Lotte Hotel UI to recommend some content , which is coming from selections of users ’ favorite actors , actresses , directors and film critics . In this way , we would try to create more intimate user experience . In practice , when the users log in the application , they choose their favorites from well - known figures . After that , we would try to give some content selections and recommendations based on the celebrities they chose and we tried to create a recommendation hub from the celebrities . At the third gathering , we tested the new feature of recommendations mechanism and our sample users were happy with the result . We observe the reason and we concluded that having recommendations based on their favorite figures gives a more realistic experience than the past . Insight : By applying the learning launch tool , our team identified our possible needs and designs to test possible solutions . Additionally , we had a chance to add a very useful feature to our service . Approach : We concluded that we need to improve this method as well . Instead of focusing on one idea , we need to create more ideas and test & suspect them and measure the outputs .