Cheating methods in APP promotion channels, how to prevent cheating in APP promotion?

Cheating often occurs in APP promotion channels. Today, let’s sort out the anti-cheating strategies for APP promotion.

1. What is channel cheating?

Channel sales use machines to simulate downloads and place manual orders to defraud advertising fees.

2. How do channels cheat?

1. Increase traffic.

2. Increase the order volume.

3. How to identify cheating?

Look at the user's contribution to the content (find the core indicators):

First of all, you need to look at the type of your application. Here are three examples:

1. For example, if you are an e-commerce application, the most direct way is to look at the order consumption of users in this channel - the conversion rate from activation to order creation. If this fraud can generate revenue contribution for you, that's not bad, haha, so this can't be false.

2. For example, if your application is a life-related application that can generate information, such as the Ask a Doctor series, then it depends on the number of questions asked by users of this channel. The cost of faking this is high, and it is not cost-effective for them to falsify this.

3. For example, if your product is a game , then it is similar to e-commerce. Look at the proportion of users who purchase value-added props and the proportion of users who complete a level. They will not hire people to cheat in playing games! !

You should have an idea after reading these three examples. It is better to look at your own product first, judge from the details of the product content, and find the core indicators to determine the real activity of users. This will be much easier.

4. Anti-cheating strategies for APP promotion?

1. Understand clearly

When promoting, you must ask clearly about the promotion method, you must also see the form of the advertisement, and judge the user quality based on your own ideas, so that you can control the user quality.

2. Raising the threshold for cheating

A. You can participate after registering and logging in.

B. When registering, you can limit the verification to email or SMS.

C. E-commerce sites can limit the participation of users who have purchased before

D. Community sites can set minimum participation levels, special tasks, etc.

3. Data Analysis

Some operators do channel delivery, and they deliver on every channel. The click-through rate is very high, but the activation rate is only in the single digits. It is also possible that the number of click activations is high, but the retention rate is low. All the money was spent, but there was no effect. I analyzed the background data myself, but couldn't draw any conclusions. What Top Promotion wants to say is that the prerequisite for analysis is to obtain reliable data. If the data is inaccurate, the conclusion can be imagined. Therefore, the statistical system of our company must be well done. In fact, experienced operators can still distinguish the difference between real and fake users through some data indicators.

A. Retention rate

Sometimes, channels will choose to import user data at important time points such as the next day, 7th day, and 30th day. We will find that the data of APP at key time points such as the next day, 7th day, and 30th day are significantly higher than those at other time points. In fact, the retention curve of real users is a smooth exponential decay curve. If you find that your retention curve has abnormal fluctuations with sharp rises and falls, it basically means that the channel has interfered with the data. It is conceivable that the quality of such users is very poor and has no commercial value.

B. User Terminal

Each channel has its own user base, and their user terminals will be different. For example, the top 10 models of Xiaomi App Store users may all be Xiaomi phones, while the majority of Mobile MM users may be users of mobile operators.

Excluding those app stores with special channels, the user terminals of most channels are similar to the distribution of the entire mobile Internet terminals. We can understand these data by looking at industry data and use them as benchmarks to compare and analyze App data.

C. User Behavior

Pull out the real user behavior trajectory, including the visit entrance, visit exit, which pages they generally visit, what content they read, and how long they stay. Then look at the discrepancies between the data from the channel and the real data, because the real data is known to you and the channel cannot know it, so it is impossible to match it very well.

D. Conversion rate analysis

The analysis of conversion rate data can not only help us deal with channel cheating, but also help us judge the user quality of different channels and improve delivery efficiency.

The road ahead is long and arduous, but I will continue to explore. Cheating and anti-cheating require careful research and planning before taking action.