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How Post Install Optimization Reduces UA Risks

How to start buying users that pay?Creatives vs Cost Per Install

How to scale you UA and reduce risks?

Moscow Nights Conference. October 2015. Moscow

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Buying Big Amounts Of Cheap Traffic Is Not As Efficient As It Used To Be

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- Increasing number of Apps in the stores

- AdNetworks compete for Publishers

- The Quality of Mobile Games is falling due to developing process speeding-up

What are the reasons?

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- Developers prefer buying less traffic with high quality, then big amounts of low quality traffic

- CPI rates are less important when choosing AdNetwork to work with

- Not CPI but CPA

What do we see?

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- Developers are looking for buying models with lower risk- Game Publishers increase in-house marketing team- Analytic, Automation and Mediation services are rapidly growing- ASO, reputation management, social integration is becoming more important

How does this effect on Developers?

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How does this effect on Developers?

Developing process is speeding-up and this brings to poor quality of the Game

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Post Install Optimization

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PIO – is optimization (improvement) of User Conversion Funnel after the install

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PIO includes:

- Registration, Login- Tutorial- Loyal users (3 opens)- Retention Rate metrics- Like&Share- Purchase

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ImpressionClickInstall

Login

Loyal User

Share

Buy

CTRCR

Event 1Event 2

Event 3Event 4

Marketing

Development

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Example:CTR 1,5%

CR 25%

Loyal Users 74%Purchase 4,5%

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Finding your ARPU and LTV(Our case)

Day X – 1000 installsDay (X+30) – $250ARPU(30) - $0,25

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We made a small research and found 3 user patterns

CTR 0.8%-1.2%, CR 25%-35%, Loyal above 50% = ARPU 0.25

CTR > 1.3%, CR 30%-45%, Loyal above 50% = ARPU 0.30

CTR 0.5%-1%, CR 15%- 25%, Loyal above 50% = ARPU 0.15

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Soft Launch Results

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Though we had very good viral effect (FB integration)

Our ARPU was still low

Decision:

1. Looking for new ways of increasing in-App monetization.

2. Starting a test UA campaign with CPI = 2*ARPU(30).

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The results of UA campaign were a surprise

1. We where able to test new creatives very fast. 2. We had very clear KPIs: if new banner performed good, we could increase bids, or decrease them if the CTR was low.

We were collecting campaign targets for each creative ID: Gender, Age, GEO, Device. We also divide Ad channels into: formats (video, interstitials, native) and pub_id (sources).

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We also were constantly updating our patterns and ARPU(30) values

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K-Margin:K-Margin = ARPU (30) – CPI

Example: K-Margin = -0.15 USDOur goal was to make K-Margin = 0.

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To make K-Margin lower:1. Decrease CPI (CPM) bid. This effected on the volumes2. Increase CTR3. Increase CR

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We also noticed:When CTR grew higher – CR grew higher as well.

While K-Margin was getting lower.

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But…Not always high CTR and CR brought us high ARPU.

… this is caused by mislead (wrong creative).

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To solve mislead problem, we analyzed:

1. What pattern brought us the most ARPPU and number of PU. 2. What banners involved more paying users.

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CTR 1.235% CR 28.4%CPI 0.48K-Margin -0.23

Campaign targeting:Man, 25-29, Russia, iPhone 7.1+

Campaign targeting:Man, 20-24, Russia, iPhone 7.1+

CTR 2.75% CR 17%CPI 0.76K-Margin -0.46

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Good Banners VS Misleads

CTR 1.504% CR 36.2%CPI 0.39K-Margin -0.09

CTR 1.035% CR 27.4%CPI 0.57K-Margin -0.32

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It’s a piece of cake1. Change the banner before increasing the bid.2. If you think you tried everything – just change the creatives one more time.3. Hire a banner making guy.4. When targeting a small group (look-a-like, app followers and etc.) banner CTR goes down in 1-2 weeks.5. CTR 2% (even if you pay for Install) will bring you twice more money, then CTR 1%

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It’s a piece of cake

If you meet User Expectations – you will be rewarded.

When a User see your banner it takes 3 seconds for him to decide whether to install the game or not.

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It’s a piece of cake

6. Find the right creative (by doing a small test) and target a small group. When your CTR will start falling down make the targeting wider and the bid lower. 7. Make your own patterns and calculate K-Margin.8. Pass & Collect as much data from the Ad Network as possible.

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THANK YOU!

Dmitry Semenovwww.unileadnetwork.com | info@unilead.net