What are the best attributions for app promotion at present?

The App has been developed, the advertising has been made, the channels have been selected, and the budget has been in place (although it always feels like it’s not enough), but the advertising is not effective enough. At this time, we will infer whether there is a problem with the product design? The copy isn’t seductive enough? Did you choose the wrong promotion channel ? ...Of course all of the above are true. But no one can answer: Where is my 50% advertising budget wasted?

Advertising is the last and most important link in directly facing users. If you cannot trace (attribute) the effect of the advertising (I am not talking about the gorgeous but fake data that the channels show you in the general sense), then it will be really difficult for you to do a good job in the next step of optimization. All the efforts made previously may be slowly wasted in the final promotion and delivery stage.

Anyone who is still doing extensive effect evaluation is too passive.

Compared with traditional advertising, the data of Internet online advertising is recorded and can be used for data analysis to optimize the delivery effect. Online media channels have taken advantage of these advantages and harvested advertisers' budgets under the concept of targeted delivery.

Data always makes people inexplicably obsessed. With our inherent cognition and limitations, we see that data is beautiful, but the results are far from satisfactory, especially now that channel conversion is getting worse and worse, and user growth is becoming more and more difficult. After the advertisements are put out, the operation staff also spends a lot of energy on effect evaluation, but it is useless. This extensive evaluation method cannot solve more substantial problems at all:

• We know that the user clicked on the ad from this channel, but did they install the app after clicking on it?
• Which channel brought about the download volume this time? Which one should I buy next time?
• In addition to user traffic , who should be credited for this later conversion? What is more scientific and reasonable?

The biggest difference between Internet marketing and traditional marketing is that it is directional and traceable

The characteristics of traditional advertising are wide coverage, rough distinction of target groups, and inability to track effects. Internet advertising can be targeted and its effects can be tracked, both of which are equally important. I believe that advertisers pay more attention to targeting research but rarely pay attention to attribution. The targeted delivery brought by big data technology is indeed very attractive. For example, when searching for "travel", the platform will show different products and advertisements to different people. Targeting seems to be very cost-effective for advertisers, but people always have limitations. Without attribution, how do you know whether extensive targeting or more refined targeting will be more effective?

Most advertisers will use a multi-channel combination promotion approach when launching apps. For example, when launching an overseas app, they will use a combination of Facebook, Google , Apple bidding ads or other media channels to purchase ads.

For advertisers, it is necessary to clarify which channel the users who receive the ads come from, the quality of the users, and the relationship between ad purchases and user acquisition and user behavior within the app. These require a set of methods or systems to verify, which is attribution.

In actual applications, attribution operations are more complicated. For example, if a user views an information flow ad , clicks on a social ad, and then completes a download in a search engine ad, how should this conversion be calculated? If a user downloads an app through a social client and does not have any other conversion behavior within the app, how can this channel be judged? For example, if an e-commerce user sees an e-commerce advertisement and generates internal conversion behavior, then to what extent will he repurchase and become active? These can be linked together through attribution to achieve more comprehensive and scientific judgment and tracing, thereby effectively optimizing all aspects of delivery and operation.

In other words, through a platform's attribution service, the advertising effectiveness data can be clearly identified. For example, you can tell advertisers what the internal payment rate and payment unit price are for App users brought by Google channels. You can make a judgment based on this data and know how to place your ads next time. If the volume on Google is good, you can increase the budget for advertising. In addition, if a certain channel brings a large amount of new traffic but has very poor internal conversion behavior, you can subsequently reduce this part of the promotion budget or even directly pass the channel. Traceability provides a strong basis for scientific optimization.

Attribution also requires methodology, and multi-touchpoint attribution is the scientific attribution analysis method

Strictly speaking, there are about 10 attribution models, and attribution classification is roughly divided into two categories: single-touch and multi-touch. To make it easier to understand, Sister Xi will introduce four common attribution models to you.

1. Last interaction model: 100% is allocated to the media that the user last touched before conversion. This is also easy to measure, but it is a single-touch model and is imperfect. It is suitable for conversion-oriented advertisers.
2. First interaction model: 100% is allocated to the channel of first contact, only considering initial brand awareness and not conversion, suitable for brand new brands.
3. Time decay interaction model: the ratio decreases over time, which is suitable for temporary promotional advertising.
4. Customized interaction model: Customize the ratio of each stage, suitable for advertisements that attach equal importance to sales and brand.

In the actual user conversion path, the user saw and clicked on an Apple mobile phone advertisement on a certain news website, and then clicked on a push advertisement in the circle of friends . After returning, the user searched for new Apple models on Baidu on the PC, and then clicked on a certain mall advertisement on Baidu and completed the purchase in a certain mall. This is a relatively common process from advertising display to user conversion. If the final interaction attribution model is used, only the mall advertisement that appears on Baidu will be evaluated for its effectiveness, while the advertising behaviors generated by those pre-advertising factors will not be calculated, which means that 100% is given to the final contact channel.

If we follow the first interaction model, the effect will be attributed to a news advertisement. Regardless of whether it is the first interaction or the final interaction, both belong to the single-touch attribution model. This attribution method is rather one-sided in multi-channel combination delivery. The various channels that users come into contact with reflect the exposure rate and may have generated conversions for users. Using single-touch attribution is very unfair to other forms of advertising because it also plays a role in the entire purchasing process of users.

A more scientific attribution needs to consider the user's comprehensive behavior, which is what I call multi-touch attribution. It uses a set of algorithms or models to make allocations. For example, if a user sees an ad on Baidu but does not complete the purchase, and then completes the purchase through a news channel the next day, I will give 70% of the effect to the news channel and 30% to Baidu. Multi-touch attribution will conduct a comprehensive evaluation across devices, screens, and channels to give a more reasonable effect distribution ratio. The time decay interaction and custom interaction models mentioned in the above figure belong to multi-touch attribution.

What are the best attributions for app promotion at present?

Due to the difficulty of data statistics in app promotion, attribution technology cannot be implemented in many places for the time being. For example, the interconnection of online promotion data, or the collection of offline promotion data. Let me talk about two places where attribution has been implemented:

1. Attribution of Apple’s official bidding advertising at the iOS promotion level

Apple's official website paid ranking advertising, as an emerging promotion channel, is favored by CPs because it can provide attribution APIs to tell CPs which downloads come from which keywords , thereby helping CPs improve their keyword delivery plans. Currently, Apple's bidding has been launched in the United States, New Zealand, Australia and the United Kingdom since early October 2016. Domestic CPs are also looking forward to its launch because it will facilitate settlement, effect assessment, and program improvement.

The downside is that Apple's attribution API is still simple and crude. It can trace which keyword each download came from, but it can't provide any further information. What does it mean? For example, advertisers may not only want to know which keyword each download comes from, but also want to see the number of registrations, retention , purchase conversion and other data. In this case, they need to use a more professional and systematic third-party attribution tool .

2. Anti-cheating tools for Android app promotion

Used to identify the source and quality of channel traffic . Identify fake traffic, don’t pay for it, and save promotion costs; operate real users to effectively improve user conversion rate . (Sister Xi will talk about anti-cheating separately later)

How can third-party tools use ROI calculations to improve the conversion rate of Apple bidding ?

Third-party attribution tools mainly aggregate external data by monitoring click data and conversion data after ASM delivery . In addition, by collecting internal data of the App through the SDK, it is ultimately attributed to which keyword brought this user, whether he has registered, paid, or even has other in-depth behaviors. Attribution plays a connecting role, connecting all the data to obtain more accurate analytical data.

In addition to embedding the SDK, it is more important to make a customized tracking solution. It is important to decide where to place the embedding points. For example, by embedding a point on the App registration page, you can obtain the user registration information. By embedding a point at the membership service, you can know which users have purchased related services, such as the type of service, the amount of the service, and the user's account. Embedding points at various key locations can help to fully record every step of the user's behavior and obtain various types of data.

Finally, we analyze the collected data to identify which word the download came from. For example, we know that a certain word brings in 1,000 users, of which 600 are registered users and 200 complete payment. This can then guide the next launch. From the perspective of channel management, it can be used to judge the quality of the channel, reasonably optimize the channel delivery strategy, save promotion costs, and improve channel conversion efficiency; from the perspective of user growth, it can be used to judge the volume effect and conversion efficiency of a certain word, thereby optimizing keywords and improving user growth and revenue growth.

postscript

The purpose of attribution is ultimately to improve operational conversion and revenue growth. Without attribution analysis and tracing, there is no way to know where 50% of advertising spending is wasted. In the era of traditional advertising, data monitoring and evaluation were simply impossible. Today, as big data technology is gradually improving, there is a foundation for attribution tracing, which can bring its value into play in refined operations.

It can be said that attribution is an indispensable tool for refined operations. Today, when the traffic dividend of mobile Internet disappears, low-cost and explosive traffic acquisition is no longer possible. What we should think about next is how to identify and utilize high-quality traffic and achieve steady growth in revenue. Then, how to improve the return on investment (ROI) becomes crucial. Only through attribution analysis of traffic, words, and users can ROI be evaluated.

Just as we did in an attribution analysis of a bidding campaign, by calculating the ROI of keywords, we found the high-precision words that brought the most purchase conversions and the words with the worst performance. We added more good words to our campaigns and reduced or even stopped the campaigns of bad words. After improving the strategy, the revenue growth of the App product doubled. This is just like what the Matthew effect says, anything that is valuable (users or keywords) should be given more attention and resources so that it can generate greater value, while anything that is worthless and ineffective should have its investment cut quickly. The more waste there is, the less the overall benefit.

This article was compiled and published by @溪姐 (Qinggua Media). Please indicate the author information and source when reprinting!

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