What Ad Monetization Parameters a Game Dev should know

Ad monetization is of monumental and growing importance, and every game developer should know about the key parameters used for evaluating ad monetization and user value. But how acquainted with them are you, really? And do you really make the best use of them？Here’s an introduction to some key parameters of ad monetization, and explanation as to how best to use them for better game data analysis.

Impression: refers to the number of times an ad is effectively displayed. However, ‘effective display’ is defined differently by different advertising platforms. Taking rewarded video ads as an example, some insist that it can be considered as effectively displayed as long as a single frame of the video is viewed. However, others believe only when a video is fully watched can it be counted as an effective display.

Fill Rate: refers to the percentage of ads served based on the total number of impressions.

Viewing Frequency: refers to the average number of times an ad is displayed to a user.

Viewing Frequency is used to evaluate in-game ad frequency.

Penetration Rate: refers to the number of people who have viewed the ad, divided by the number of total active users.

It can be used to evaluate the reasonability of in-game advertising scenario and trigger mechanism, that is, to evaluate whether the ads can cover enough users.

eCPM(Effective Cost per Mille): It refers to the ad revenue on every 1000 times ad impressions.

Currently, it is acknowledged as the metric which helps to evaluate ad monetization capability. It’s no wonder that this parameter always comes to mind first when monetization evaluation is needed.

However, eCPM reflects profitability, rather than the revenue itself. In addition, ‘effective display’ is defined differently by different advertising platform, so it’s hard to say which eCPM is accurate.

ARPU: This refers to the ad revenue generated by a user per unit time.

In contrast with eCPM, ARPU can reflect the monetization capability more directly. When the number of active users is constant, higher ARPU represents higher ad revenue.

Here is the 1-day statistic of a casual game on App Store. 85% of this game’s total revenue comes from ad monetization. There are two rewarded video placements embedded in the game. On this day, 90% active users were from the United States:

Then let’s analyze each parameter:

· eCPM is $39.05; Fill rate is 99.18%: UPLTV’s（www.upltv.com）mediation SDK solution helps the fill rate of this game reach 100%, which means no opportunity is wasted. Besides, this game also has a better performance in eCPM compared with other casual games in this genre in the US.

· Ad ARPU is $0.04: it’s a relatively low number. Even when in-game revenue is counted, the total daily ARPU(in-game ARPU+ad ARPU) is no more than $0.05. It’s hard for Apps which keep only routine retained data to be profitable. Currently, for apps optimized by UPLTV, the daily ARPU can reach $0.15-$0.2 or even higher.

· Viewing frequency is 1.26; the penetration rate is 21.89%: For Apps embedded with two rewarded videos, these two parameters didn’t reach average levels. It may be caused by several factors: inappropriate trigger mechanism, non-conspicuous UI design, and undue advertisement restriction, etc.

In closing, I’d like to summarize the analysis method of monetization parameters:

· Total impressions and fill rate should be considered while analyzing eCPM;

· During the optimization of traffic distribution, monitor fill rate and ad ARPU timely;

· If ad ARPU is too low, remember to check viewing frequency and fill rate.

Bruce Du, Key Account Manager of UPLTV. Bruce has worked in game and mobile ads industry for years, and previously was in charge of user acquisition for a renowned game publisher. He holds a master’s degree from University of Lincoln.

UPLTV is a global internet company founded in 2017 with its headquarters located in Shanghai and additional offices in Singapore, Seoul, Hanoi and Dusseldorf. UPLTV’s SDK aggregates top advertising platforms globally. Using advanced algorithms to optimize advertising logic and utilizing smart and customized solutions, UPLTV is able to deliver at least 20% more in ad revenue compared to similar players globally. Leveraging Google’s TensorFlow framework, UPLTV uses LSTM to analyze in-game behavior, interactions, in-app purchases, and tolerance to advertisements. By using the data that tests players’ feedback and forecasting 7-day and 14-day retention rates UPLTV optimizes ad display duration and frequency maximizing player LTV. The intelligent ad mediation platform is enabled in order to achieve higher retention rates and achieve balance between revenue and user experience. Currently, UPLTV works with over 300 game developers and publishers to display over millions of ads daily helping partners earn higher revenues.

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