7th edition of GameCamp: Monetisation and LTV measurement
9.30 - 10.00
Registration + coffee
10.10 - 10.40
Monetisation trends based on the data
What monetisation trends can we see? Based on the data and predictions Which gaming genres are getting better in monetisation, which ones are getting more challenging?
10.40 - 11.15
Monetisation in hyper casual: what is now and what is next?
Examples of good and bad approach to monetisation in hyper casual. Examples of tests and trials for different games. What are the newest trends in monetisation in hyper casual?
11.15 - 11.45
3 lessons from 9 years of locomotive offers Data based user segmentation and monetisation. TrainStation case study
How did TrainStation evolve from simple offers to automated personalized monetisation systems? How to combine using data, design insights and community outreach to create the most compelling offers for players? How do we integrate that information with contentcreation and automation? Presentation based on practical examples.
11.45 - 12.10
12.10 - 12.45
How user segmentation and personalizing offers improved monetization for Geewa
Good and bad practices in user segmentation based on Geewa case. Interconnecting monetization with player progression. How we maximized user experience, increasing monetisation based on that.
12.45 - 13.15
Most important aspects of ad monetisation. Learnings based on scale of 550 mln ad impressions daily.
200 MAU and 550 mln ad impressions daily give us enormous field for testing and trying new approaches. What learnings we saw from those learnings (both on high and operational level)?What you can learn from our successes and mistakes?
13.15 - 13.45
How to get out most of AdMob mediation
A hands on approach towards AdMob mediation. How to test different configuration, optimize watterfalls. Presentation based on real examples and case studies
13.45 - 14.45
14.45 - 15.20
What does it mean to have good Rewarded Video?
What are the best practices for Rewarded Video? Examples of successful integrations. What are good benchmarks for Rewarded Videos?
15.20 - 15.55
Personalisation as the key to optimising your game's revenue & LTV.
What are good and bad approach towards personalisation based on the data? Good examples of personalisation in mobile games.
15.55 - 16.20
16.20 - 16.50
Genre mashing as the way to create new value for gaming users and increase monetisation. Based on Traffic Puzzle example.
By combining the mechanics from different genres games can appeal to new audiences and provide a fresh experience to maintain or improve engagement. Genre mashing also opens up new possibilities for innovation and diversification in monetization. What was our approach in development of Traffic Puzzle? Where were the biggest challenges, where were the biggest gains?
16.50 - 17.20
The Retention, the Revenue and the LTV. A story of sacrifices and resurrections on the mature product.
It's a story about the quest to find balance that takes place in recent years of ZooCraft mobile game. We will show how various game design and product desicions both improved and ruined metrics and what we were doing about it to reach the win-win solution.
17.20 - 17.50
Transit King case study - data driven design with its benefits and challenges.
Working on a released free to play title creates a lot of design constraints. Sometimes constraints help to be more creative, but sometimes core elements need to be changed to move forward. In the case of Transit King, a big economy balance and design change had to be made to potentially increase KPIs and create more opportunities for its growth. In this talk we will speak about the process of identifying core problems based on data, evaluating risks and opportunities of potential design changes and see how big alteration in core elements of the game enhanced player retention by 30% and revenue by 40 %.
9.30 - 10.00
10.00 - 10.10
10.10 - 10.40
Growth markets in mobile gaming
Which markets you should focus on in mobile gaming? Where is the highest LTV versus CPI. Based on Google data
10.40 - 11.10
Ad LTV in mobile gaming: deep dive into industry
What is the situation in Ad LTV right now, what is a good benchmark for mobile game today? Data based on analyzing unique and granular data collected over a period of months and based on billions of in-app ad impressions. What are good and bad best practices looking at games from many genres? What is must have and what is nice to have? What should be most important for smaller company, what should be the focus for bigger organisation?
11.10 - 11.40
11.40 - 12.10
Time to kill: How to predict at an early stage whether an app is going to be successful or not?
We analyzed 1000 games of different genres and extracted common types of how the key metrics of apps develop after worldwide launch. We analyzed only notable games with the 1st year sum of revenue + downloads more than 2M and compared their performance during their first year. Learnings from the analysis.
Predicting user acquisition payback: methods and tools
It’s not a secret that most of the games pay money to attract new players. How can we be sure this money works as effective as possible? The earlier we predict how much money will be attracted by the campaign, the quicker we understand if it’s going to pay off (and when), the sooner we will be able to reallocate the money into more effective channels. In this session we will go deeper into different methods and approaches. What are advantages and disadvantages of different methods?
12.50 - 13.20
LTV/ROAS/revenue predictions for UA campaigns
How we are building models forLTV/ROAS/revenue predictions for UA campaigns. What challenges we faced in different gaming genres and in different approaches. What solutions we used? Presentation based on examples and real cases.
13.20 - 14.10
14.10 - 14.40
Big Problem, BigQuery: User Feature Engineering in Event-driven Analytics
How to find best predictor of good LTV in a game (feature/event in a game) ? What is needed for good feature selection? How to construct and aggregate data well? How it can be used in practical way? Strategic and operational approach.
14.40 - 15.05
UA focused on right LTV predictor
Examples of mobile gamin companies that used feature selection for scaling their UA activities. How they integrated their data with UA campaigns? Feature selection tests done by Cherry Pick Games.
15.05 - 15.35
LTV measurement and multi-touch attribution
Can multi touch attribution models help you increase the LTV of your campaigns? How should you split the credit for revenue generated by users who saw multiple ads? What is easy what is more difficult?
15.35 - 15.45
16.00 - 18.00
Mobile gaming residency ceremony
Join us for a Mobile Gaming Residency Graduation. We will kick off with a ‘Future of Gaming’ Panel followed by a short showcase of 11 gaming startups that took part in the program. Residency was a 6 months program for growth-stage gaming startups. Each startup received tailored mentorship and workspace at Campus in Warsaw.
18.00 - 23.00
Networking party at Campus
Join us for Google for Startups networking party!
Get info about our speakers.