UXUI

Agile

Camping Service Retention Improvement

/2 weeks

Member

1 Developer

1 Desigenr

1 Stakeholder

1 Developer

1 Desigenr

1 Stakeholder

Tools

Figma

In depth interview, UT

Responsibility

Product Designer

Research

UIUX Design

Prototyping

User Story Planning

Time frame

2 weeks

Summary

This is a project aimed at improving service activation and stickiness right after the launch of the car camping spot discovery service.

We explored ways to deliver the 'personalized recommendation' feature with limited resources.

I designed the flow and gathered user feedback through prototyping.

As a result, the 7-day retention rate for users of the feature increased by about 20% compared to the overall average retention.

Background & Mission

Camping discovery service: Improving retention through personalization

We launched GAMP, a car camping discovery service showing 20 sites by distance from user location.

As a new service with limited content compared to competitors, we adopted a quality-over-quantity personalization strategy—delivering customized recommendations based on user preferences.

To verify whether this strategy actually delivered value to users, we established retention metrics as our core performance indicator, and set a goal of 5% improvement.

See launching project

Market Position and Service Key Flow

Communication

Limited Resources:

Direct User Input Over Algorithm Development

Limited Resources:

Direct User Input Over Algorithm Development

While developing a recommendation algorithm would have naturally enabled us to suggest camping spots that match user needs, implementing such logic was realistically impossible with our team of just three members. Therefore, we decided to directly ask users about their preferences and recommend camping spots based on their responses.

OPTION 01

Logic based on

collected data

No additional user input needed beyond exploration

Building the data collection logic requires significant time and resources.

OPTION 02

Collect data
by directly asking

Quick development with a relatively simple logic.

User input is needed for survey responses, but participation rates may be low.

Challenge & Research

How can we reduce drop-off rates during responses?

I identified concerns about user drop-off due to lengthy Q&A flows and conducted case research to reduce this friction.

I observed that major brands like oHouse were running MBTI-style test events that went viral. Despite having over 10 questions, users completed the entire process and shared results with each other.

MBTI test style Q&A flow of o'House

Approach

Fun process and anticipated results, like an MBTI test

Approach 01

Binary choice questionnaire

We designed the questionnaire in a format similar to the MBTI-style marketing events that were popular in Korea at the time, featuring situational hypothetical questions with binary choice answers. We also added a humorous nuance to make it more engaging.

Approach 02

Recommend newly updated spots campers want most

According to previous interview, campers want to discover less-known camping spots. However, our existing data consisted only of popular spots since it was already exposed to users and crawled from third-party sources.

To address this, our team decided to personally scout and recommend newly added car camping spots. We implemented a weekly policy where users receive fresh recommendations every Friday at 6 PM as part of our new spot discovery routine to boost retention.

Approach 03

Turning information into rewards with a Goodie box metaphor

"To gamify the opportunity to view recommended car camping spots and make it feel more like a reward, we added a goodie box metaphor.

Design

Weekly Camping Spot Goody Box

Test

Coupon-like Entry Point

While there were no major issues once users entered the flow, all UT participants perceived the entry button on the home screen as a coupon pack. To address this, we added a speech bubble that includes the functional definition as a camping spot recommendation box.

As is

To be

Result

2x Retention Improvement for Recommendated Users

One month later, our 7-day retention analysis revealed that users who engaged with the feature demonstrated 30% higher retention rates than non-users.

Average

28%

58%

Used Cohort

Follow-up

Expanding reach to more users

We decided to launch a follow-up project to broaden our user adoption. To this end, I carried out phone interviews with the following user segments:

Users who used it once and then churned

Users who never used the feature

Users who continuously use the feature

The results showed that churned users lost interest because the recommendations didn't match their preferences, while non-users failed to engage with the feature due to lack of awareness.

While we couldn't improve recommendation quality due to our data constraints, we changed theentry point to enhance its visibility.