Rafael presents data-driven recommendations to double revenue for the game "Catch The Pink Flamingo" in the next month. Key insights from data analysis include identifying high spending "High Rollers", focusing on premium platforms, and pursuing a 2.31x increase in user sessions through improved engagement. Recommendations center on leveraging player demographics for ads, developing social features like chat, and coordination across departments to keep users engaged.

1. How can we double the revenue of the game
Catch The Pink Flamingo
in the next month?
Rafael Aguiar
Data Science Engineer @ Eglence Inc.
We gather here today to answer an extremely important question, one that will shape the future of our company.
My name is Rafael, I’m one of the engineers at the data science team and I will guide us through the best insights we discovered from our data.
I hope this presentation will set the building blocks for every department at this company (specially sales and marketing) to bring our game to a whole new level.
So, how can we double revenue in the next month?

2. Problem Statement
What does our data tells us about ways of increasing revenue from game players?
Users
Team
Team Assignments
Level Events
User Session Chat Data
Buy Clicks Ad Clicks
Game Clicks
Let me start with a quick review of what kinds of data we collect in our game.
In the data sources represented by the blue boxes you can find all the data relative to a user and how he interacts with the game
(e.g., user Rafael, is from Brazil, 26 years old, member of the team EglenceAllStars, is currently at level 6, plays 5 days a week and almost every time catches the flamingo with a single click).
In the ChatData data source, represented by the purple box, you can find all the data relative to the communications between the users in our chat application
(e.g., Rafael communicates to other users in his team after every session he plays).
These blue and purple boxes have a direct impact on the green ones, where all the data relative to user’s purchase and advertising behavior are recorded.
Thus, throughout the rest of this presentation we will be investigating how changes in some variables in the blue and purple boxes (like daily active users) can help us achieve that 2x increase in revenue.

3. Data Exploration Overview
Revenue from item purchases 21407.0
Revenue from advertising* 8161.5
# items available to purchase 6
Price of the items 1,2,3,5,10 or 20
# users who made purchases 546
Total number of purchases 2947
Average # purchases per session 0.3051
Average sessions per day 420.4545
Average sessions per user per day 1.6631
Daily active users (DAU) 252.8182
Retention 1 day: 60%
3 days: 51%
7 days: 43%
18 days: 29%
* assuming U$ 0.5 per click
As one of the first steps of our analysis, we went through a thorough exploration of our data and collected some metrics that paint a clear picture of where we are today and where we need to go.
Starting from the table on the right side of the slides, you can notice the most important of them.
As of now:
- our revenue from Ads is about 38% of the revenue from in-app purchases;
- we currently have 6 items available for purchase, with prices ranging from 1 to 20 dollars;
- we have 546 paying users (representing a conversion rate close to 50%);
- almost 3k purchases and over 16k Ad clicks;
- an average of 0.3 purchases per sessions;
- an average of 1.66 sessions per user per day;
- about 253 daily active users;
- a retention rate of 60%, 51%, 43%, and 29% for 1 day, 3 days, 7 days and 18 days respectively;
- our top three selling items are Id2, Id5 and Id0 in this order;
- our top three items generating more revenue are Id5, Id4, Id2 in this order;
- finally, regarding our advertising revenue, the top 3 categories with most clicks (after normalizing for the amount of ads available for each category) are clothing, fashion and automotive in this particular order.

4. What have we learned from clustering?
Cluster # Cluster Center
Cluster 1 adClickCount age mornings afternoon evenings night
43.643443 39.639344 3.885246 4.254098 2.901639 1.844262
Cluster 2 adClickCount age mornings afternoon evenings night
17.182482 54.437956 1.525547 1.109489 1.554745 1.109489
Cluster 3 adClickCount age mornings afternoon evenings night
17.364130 27.250000 1.614130 0.934783 1.456522 1.255435
Cluster 1 has users with average age around 39, the highest Ad click rate and players who play multiple times during the whole day (specially at afternoon).
Cluster 2 has users with average age around 54, the lowest Ad click rate and players who play a few times during the day (specially on mornings and evenings).
Cluster 3 has users with average age around 28, a low Ad click rate and players who play a few times during the day (specially on mornings, but less at afternoon).
For both young and senior users, It’s likely that the Ads shown in the game are not very appealing. Eglence should leverage the player's demographic info to deliver a better advertising experience.
Eglence could also run user acquisition campaigns to acquire more users like the ones in the Cluster 1, for example, since they are more likely to click on ads than other users.

5. From our chat graph analysis, what further exploration should we undertake?
If we look into our chat application data there are other patterns worth mentioning:
- the chart on the left represents the relationship between the age_group (eighteen to forty four years and forty five to seventy years) and the clustering score of a user;
the clustering score measures how tight is the relationship between the cluster of users formed by an arbitrary user and his peers;
it goes from 0 (when no one talks anybody in the cluster except to the arbitrary user) to 1 (when everybody talks with everyone in the
cluster, including the arbitrary user);
by this measure we can see that younger users tend to form tighter clusters than older users in our chat application (though clearly there’re some outliers);
- while the chart on the right represents the relationship between the continent each user comes from (inferred from their birth country) and
how talkative they’re (measured by the number of chat messages they sent);
by this measure we can notice that Antartica, South America, Europe and Oceania are the continents with users there are chatting like crazy;
We can leverage this information to build new features into our chatting application to better serve these demographics and foster an ever increasing number of engaged players.
For example, a special flamingo could appear on the chat screen randomly and all users on the chat would have a chance to click the flamingo to win an item for free.
And although we can't back this with our data (because there’s no such pattern yet), we strongly believe that a vibrant chatting community is one of the key
aspects involved in keeping players coming back to play the game. We have seen this play a role in the success pretty much every game involving any team dynamics.

6. What have we learned from classification/regression?
1. High Rollers vs Penny Pinchers
2. The formula for doubling revenue
The prediction workflow we recently developed is nice, but Eglence can go further. By training a model that has a higher success in predicting true positives for High Rollers,
Eglence could identify more users that are willing to spend large amounts of money in the game. The current model is more successful at predicting true positives for PennyPinchers,
which good, but has less impact in revenue.
Eglence could focus item's revenue streams on premium platforms (Apple) and improving advertising strategies in other platforms (Android, Windows). This could be done
by promoting flash sale events on the former and improving Ad placement in the latter, for example.
Regarding, the formula for the success, the doubling revenue trick: if we were to suggest a single metric to keep in mind that would be the number of user sessions.
We have trained a regression model using the number of user sessions and the revenue (both from Ads and in-app purchases) as some of the features and we discovered that a 2.31x
increase in the number of user sessions will lead us to that 2x increase in revenue. I didn’t say it would come for free! If we want to achieve that 2.31x increase in user sessions we will
have to think of better ways of keeping the users coming back to the game. And this is good because it’s something that every department in this company can contribute to.
Improving the chat application, improving the UX, the push notification system, the rewards system, all this will play an important role in keeping the users engaged.

7. Recommendations
• Leverage the player's demographic info to deliver a better advertising
experience
• Foster the development of a vibrant chatting community around the game
• Build models capable of identifying HighRollers with high recall
• Focusing item's revenue streams on premium platforms (Apple) and improving
advertising strategies in other platforms (Android, Windows)
• Pursue an 2.31x increase in user sessions
I made several recommendations for you here today, but here are the most important ones (in no particular order) that I think everyone should keep in mind.
Hope you all now have a better understanding of what needs to be done to take this company to new heights.

8. Rafael Aguiar
@rafadaguiar
https://ca.linkedin.com/in/rafadaguiar
Thank You!
Thank you all for your time today!
Here is how you can get in touch if you have any questions.
[END OF PRESENTATION]