Finding local events easily, clearly and all in one place.

Problem

Starting question

Do people struggle to get local, everyday information? That covered bin collection, parking and public toilets as well as events.

Focused problem

People who want to do something spontaneous nearby can only find local events with a lot of effort. Searching across different channels takes time and is frustrating.

Research & insights

Four steps from the broad starting question to the focused problem: desk research, interviews, a survey and clustering.

1Step 1: Desk research

Researching local everyday information showed that there are problems in several places. Where they were biggest was still open.

Result: problems confirmed, focus still unclear.

2Step 2: Interviews

In four interviews, three people brought up local events on their own. Two of them described specific, recurring problems when searching.

Result: a possible shift to events, to be checked with a survey.

3Step 3: Survey

To check the direction from the interviews, I ran an online survey with 27 participants. Five sections moved from general search behaviour through difficulties with local topics to events specifically. Anyone who had no difficulties or didn’t look for events was filtered out first, so only the 17 event seekers answered the event questions. Beforehand, I set hypotheses with target values to measure the results against.

Result: the shift is confirmed: focus on local events.

65 %

name events as the topic where information is hardest to find

64.7 %

often or always check several channels

90 %

find hard-to-find information somewhat or very frustrating

4Step 4: Where is the problem?Method applied – affinity mapping

To find out where the core of the problem lies with events, I clustered all the findings from desk research, interviews and the survey. Three areas emerged:

• Information quality
• Time-consuming search paths (prioritised)
• Orientation

“Every extra club website is another step and makes the search a chore.”

Who am I solving this for?

My persona grew out of all the findings and the focused problem area, “time-consuming search paths”.

“With events, there’s no single place where you get the full picture:when, where, what and who’s playing.”

Ideation & concept

From the HMW question I developed three directions that could take the search off Luka’s hands. Each follows its own core logic.

• Community feed · core logic: contribute

  A local feed where people post events they know about or run themselves, including the small ones.

  Catch: Collecting is up to the users. Without posts, the feed stays empty.

• Personal assistant · core logic: suggest

  An AI assistant that Luka tells in everyday language what he feels like, say “something spontaneous nearby tonight”. It puts together a matching selection.

  Catch: A filtered selection instead of an overview. If the suggestion doesn’t fit, the search starts over.

• Central platform · core logic: bundle

  Bundles all local events in an area in one place, categorised and filterable, from big concerts to a small club night.

  Catch: The idea stands or falls on completeness.

Risky assumption

The overview has to be complete enough for Luka to trust it and give up his usual channels. If it has gaps, he goes back to Instagram and club websites.

The route to completeness

So the focus is on completeness, and with it on how events get into the app in the first place. Lokal Los. combines four routes:

1. Existing sources · base

   pulled together through APIs, scripts and AI.

2. Organisers · addition

   add their own events.

3. Users · addition

   contribute events they know about.

4. A small team · review

   checks and fills the gaps.

How it differs from competitors

I analysed providers such as Eventim, Eventbrite, Rausgegangen, meinestadt and festle. Most of them rely on ticketing, organiser listings or an editorial selection. Small local events without ticket sales or marketing often slip through. Lokal Los. focuses on exactly these events, and on completeness.

The difference isn’t only in how events find their way into the app, though, but also in design decisions based on the research.

Further considerations

It was also important that the app works straight away without a login. Asking people to register the first time they open it would be an unnecessary hurdle.

Without login

• MVP flow
• Save events (stored on the device)

With location sharing

• Distance shown on the event card and the detail page

With login

• Choose interest filters for a personalised event list
• Submit events
• Sync saved events across devices
• Back up settings in the account, e.g. when switching phones

Testing & iteration

Does my solution work?

During the mid-fi phase, I ran a usability test based on the MVP flow.

MVP flow

Method: user story mapping

The main interaction happens entirely on “Entdecken” (discover).

1. 

2. 

3. 

Usability test

I tested with two participants.

1Problem 1: Event detail page: CTA button, price line & tab bar

• Label: “Zum Veranstalter” (to the organiser) didn’t make clear what people would find there. It matched neither the goal for paid events (buying tickets) nor for free events (more info).
• Price line: Both participants tried to tap the price instead, but it wasn’t clickable.
• Tab bar: The CTA button got lost among the navigation items, and neither participant noticed it straight away.

2Problem 2: Event data on the event card

• Price & date: The information was there but not visible enough. It got lost in the grey text.

How I would measure the success of the changes

As a next step, I would test the changes with a CRO hypothesis, using KPIs and UX metrics. Taking the CTA button as an example:

• CRO hypothesis: If the button says what happens next (“Tickets kaufen” or “Zur Veranstalterseite”) and the price line also leads to the ticket, more people will move on to the organiser’s page, because they know what to expect.
• KPI task success: Ticket route found on the first try; before 0 of 2, target 4 of 5.
• UX metrics:
  • Misclicks and questions before finding the ticket route
  • Confidence after the task on a scale of 1 to 5, target at least 4
• Successful if at least 4 of 5 participants find the ticket route on the first try without asking.

Solution & design

Idea and design decisions

For the design, I deliberately adopted familiar patterns from competitor apps and combined them where they made sense. People find their way around straight away, without learning new interaction patterns.

The goal is that people spontaneously get a clear list of all events around them, with a focus on small local ones. That helps on holiday or after a move, when you first have to work out where anything is happening at all. But even people who’ve lived somewhere for years miss a lot, because small events are scattered everywhere, online and offline. According to my research, this mostly affects people between 20 and 39.

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Structure: discover

Top: search + filter

1. 1City picker + radius
2. 2Quick filter (date) + category bar
3. 3Search
4. 4More filters

Bottom: orient + navigate

1. 5Interactive map
2. 6Tab bar

Images · AI-generated

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Structure: event details

Top: overview

1. 1Back, share and save stay visible while scrolling
2. 2Event image with the organiser’s logo
3. 3Like count as a popularity signal
4. 4Event details at a glance: date, venue, price and address

Bottom: action

1. 5A short note on what’s behind the button
2. 6CTA button “Tickets kaufen” (buy tickets) or “Zur Veranstalterseite” (to the organiser’s page)

Logo · AI-generated

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123

Structure: event details, further down

Left: information and location

1. 1Description with keywords as chips
2. 2“Mehr anzeigen” (show more) expands the full text
3. 3The map can be enlarged
4. 4Tapping the address opens the map app options

Right: organiser and more to discover

1. 1Organiser, with the source named
2. 2Report a problem, for example wrong details
3. 3Similar events as suggestions and ads

Images · AI-generated

Try the prototype in your browser

How do I measure whether my solution works?

After launch, I would measure success with the HEART framework and also map the metrics onto the AARRR funnel. Both lead to the same two goals:

Learnings & AI

AI as a tool

I ran this project with the help of AI. The tools were Claude (including via MCP in Figma), Claude Code, ChatGPT, Perplexity and Nano Banana.

My learnings

What worked, what would you do differently?

Next step

Next, I would check the changes in another usability test. I would also question the notification icon in the header again. Since it’s only a secondary action, it could move into the account area. That makes the header calmer and gives search and filters more room.