How Data Predicts Game Success: From User Acquisition to Budget Decisions

The purpose of the "Exploring Game Methodology" open class is to invite developers, operators, and analysts to share universal product concepts and methodologies in the gaming field.

The speakers, senior data marketing managers from Tencent Interactive Entertainment, focus on the predictive role of data in product decisions.

Data Sources and Value

Data is obtained from three main sources:

Game data, including market and operational data.

Platform data, such as cross‑game data and platform behavior data.

External data, both directly accessible and obtained through partnerships.

After identifying sources, data is collected through "point placement" across key paths and time dimensions. Collected data is then filtered to remove noise.

Data is gathered throughout the entire user lifecycle, from brand awareness to interest, trial, and eventual recommendation.

Pre‑Decision

Data supports three stages: pre‑decision, in‑process optimization, and post‑assessment.

How to Predict Product and Market Trends?

Estimate new user volume after product launch.

Forecast daily active users (DAU) and peak concurrent users (PCU), then assess how many of those users stay and are willing to pay.

What Influences New User Acquisition?

Not all factors are quantifiable; team quality is hard to score. When using data to predict trends, we prioritize quantifiable factors with major impact.

Two key indicators strongly affect a product's launch success:

Baidu Index, reflecting market heat and user attention.

Net‑bar click‑through rate, representing early interest from target users.

Analysis of many games shows a clear linear relationship among Baidu Index, net‑bar click‑through rate, and resource conversion rate.

Combining these metrics allows prediction of overall success around 90%. Word‑of‑mouth impact is harder to quantify but still relevant.

What Happens After Users Arrive?

We estimate monthly active users and track daily active users (DAU) to understand retention. Retention curves often follow exponential decay, and fitting models to daily data yields product‑specific retention coefficients.

Using the retention curve, we calculate daily active users and find that simulated DAU closely matches actual DAU, with minimal error.

How to Support Budget Decisions?

After launch, we assess whether users stay, pay, and for how long. By estimating user scale from resources and retention, we can forecast output and compare investment versus return.

This data‑driven approach prevents following a wrong path and gives decision‑makers confidence in outcomes.

Typical Data Tests in Daily Work

Market response testing: compare core‑user retention from niche tests with broader audience performance.

Creative material testing: evaluate how well ad creatives match user psychology and drive conversion.

Landing‑page testing: assess static vs. dynamic ads, banner clarity, prominent branding, and style impact on conversion rates for both PC and mobile games.

In‑Process Optimization

Precise marketing starts with user segmentation. By building tag libraries from user behavior, we can deliver tailored content, reducing CPA by over 40% and boosting acquisition conversion.

Post‑Assessment

If pre‑decision forecasts match actual results, the KPI is met; otherwise, data review identifies shortcomings.

Interesting Data

Conversion efficiency for PC and web games has declined about 30% over the past year.

Net‑bar gaming usage dropped roughly 15% in the same period.

Users aged 21‑24 dominate the market; those over 40 account for about 10%.

Mobile gamers are younger, with only ~15% over 30.

Guangdong has the highest concentration of gamers, followed by Jiangsu and Shandong.

In mobile gaming, Guangdong remains first, with Zhejiang overtaking Shandong into the top three.

Beijing, Chengdu, and Guangzhou rank top three in user distribution.

Gender ratio is roughly 6:4 (male:female).

About half of gamers engage daily.

About Tencent Interactive Entertainment Data Products and Technology Team

The team originated in 2010 as a data analysis group, shifted toward data‑driven marketing in 2011, became an independent marketing data group in 2013, and launched dedicated data products in 2014.