Do you think you can do channel promotion well if you have a budget? You need these 4 points

Promoters should all have a feeling that the budget is never enough. If we want to spend money wisely, we must know ourselves and our enemies.

Today I will tell you how to use a limited budget to conduct quality assessment and channel allocation. I hope this helps you.

First, let’s talk about “ confidant ”.

Different industries, different brands , and different product stages will have different promotion goals. Different companies have different budgets and will have different plans, which requires us to make judgments based on the specific circumstances.

Knowing the enemy requires us to have sufficient understanding of the channels as the basis for specific budget allocation plans.

Generally speaking, we consider the following four indicators when analyzing a channel: cost, quality, scale, and stability.

Stability determines whether this channel should be deployed for a long time or only used at critical time points.

The scale represents the channel’s ability to acquire customers and is also related to business KPIs. The upper limit of the volume of a channel can be estimated by investigating the delivery of competing products, or by analyzing the situation of the entire advertising platform and then making an estimate.

For example, a mobile phone manufacturer’s store can estimate the overall sales volume of the mobile phone store based on the shipment volume of mobile phones, and then further estimate it based on the competitive landscape of the industry.

The first is a horizontal comparison of costs, which requires unified standards. There are some problems in the process of calculating costs with unified standards. Here I list what I think are the two most common problems and explain how to calculate them.

The first question is, how to eliminate the natural volume of the app store after paid promotion ? It is understood that many of my colleagues now do not exclude natural volume when calculating the cost of acquiring app store users. In fact, it is understandable that the data looks better when calculated and reported in this way, after all, everyone reports it this way.

But I personally think that there are some data that we should clarify, and everyone should be a sober promoter . This is what makes us different from others.

Since you need to control the budget allocation of channels as a whole, or make budget allocations for the channels you are responsible for, you need to know the actual cost of acquiring new users through each channel.

We need to know that information flow , DSP , advertising platform, alliance network , etc., do not have natural download users. Then it becomes necessary to eliminate natural quantities for comparison.

There are two methods I am using now, one is the year-on-year difference calculation method, and the other is the download activation conversion rate calculation method.

First, the year-on-year difference calculation method . We know that CPA = cost difference / activation difference. If we do not take the absolute value, taking the difference can eliminate the interference of natural quantity. For example, the average daily consumption of a certain channel in June was 1,000 yuan, and the average daily total activation was 10,000. In July, the average daily consumption increased to 7,000, and the average daily total activation became 12,000.

So if the average daily consumption of this channel is less than 10,000 yuan, its customer acquisition cost is about 3 yuan. But the disadvantage of this calculation method is that it is affected by natural quantity fluctuations. And if the difference in cost is not large enough, the error will still be relatively large.

Because with paid promotion in the app store, the natural volume of the app store will change. Because many factors such as the weight in the app store will affect the natural volume of the app store.

So here I recommend the second method, the download activation conversion rate calculation method . This method requires us to first calculate the conversion rate from natural downloads to activation.

Every product is different, and every product is different in different channels, but the natural download activation conversion of the same product in the same channel will not change much.

After calculating this value, we multiply it by the natural download volume we see in the platform background to get the estimated promotion activation volume . For example, if a certain channel had no paid promotion in June, the daily natural download volume was 10,000, and the average daily activation was 5,000, then the conversion rate was 50%.

Paid promotion started in July, and its natural downloads increased to 20,000 per day, and its total activations became 12,000. At this time, we can use the formula to calculate that the daily promotion activation may be 2000.

The only limitation of this method is that some app stores’ advertising backends do not provide natural download volumes and data, and can only provide promotional download data.

But frankly speaking, as long as the app is downloaded from the app store, the media can obtain the download data. If you can't see the backend, it depends on your business relationship with them. In fact, any data you want can be obtained.

In addition to the app stores that must be visited, are there many other channels that are mixed?

We need to conduct a horizontal comparison and take a holistic view before we start launching in order to reduce the time and financial costs of testing channels, still based on the principle of unified standards.

Two conversion formulas are proposed here. For CPM payment channels, you can multiply the CTR average exposure to click conversion rate by 1000, because CPM is one thousand exposures. Then calculate the possible clicks brought by one thousand exposures, and then divide it by the price of one thousand exposures to convert it into the CPC price.

The same applies to CPT channels. The disadvantage of this method is that we need to find a way to find the channel to be evaluated and the average exposure click rate of the advertising platform.

And we know that different materials and different products may have differences, so this is a big part of the reason for the error, but this is a method that can be used when making early predictions.

The above lists a total of several formulas, I hope they can help you with cost calculation.

Next, I would like to briefly discuss with you the topic of channel quality assessment. The quality evaluation indicators of sharing are all integrations of content shared by industry predecessors, and are not evaluation indicators in the process of Baidu video promotion .

I have divided the channel quality evaluation indicators commonly used in the industry into five categories.

The first is retention , including next-day retention rate , 3-day, 7-day, and some companies also divide it into 14-day, 30-day retention rate, etc.

Then activity includes daily active numbers, weekly active numbers, and monthly active numbers.

Some companies that are strict with channel assessments will also consider user composition , return of active users, loyalty, and continuous active users and other indicators.

In addition, participation is also a dimension that must be assessed. For example, for a certain channel, the number of times its users launch it, the duration of use of the channel, the pages visited and the time interval between uses, etc.

For revenue-related products, we will also focus on indicators such as user lifetime value, average revenue per person, ROI and payback period.

In addition to the indicators listed in the above figure, there are also data indicators in many other dimensions such as functional attributes and user portraits , but they may not be the most core indicators that channel promotion focuses on.

Other indicators may be closely related to content operations and product design, and require joint attention. The above are all relatively basic analysis indicators of APP.

Another method you can try when doing channel quality assessment is anti-cheating strategy .

Because the development cost of technology is relatively high and the threshold is relatively high. Assuming that from a business rather than a technical perspective, we only need to grasp one core idea, which is to determine whether the user's usage behavior is consistent with the real user behavior . After grasping this core idea, we can actually come up with some relatively low-cost methods.

1. The proportion of key operation activity to total activity

For example, on Bilibili , the proportion of its users sending barrages may be very high, but if the proportion of users sending barrages on a certain channel is very low, then we can think that it is suspected of cheating.

2. Usage rate of new features

For example, a certain product did not have a community at the beginning, and users could not leave comments. Later, a comment function was added. This function itself was successful at the product level and the overall performance was good, but the comment rate of users on a certain channel was very low, so we can consider this to be cheating.

Because the machine cannot know when new functions are added, it is a good idea to approach it from the perspective of new functions. Moreover, the amount of data will not be large and the running cost will be relatively low.

3. The time when users activated the app

For example, if users download and activate at two or three in the morning, or if a certain channel has a lot of activations every day but they are all concentrated in the middle of the night, then we will think that this channel is highly suspected of cheating .

4. Look at the concentration of equipment

For example, machine models, operating systems, etc. This is also a method widely used in the industry.

The following two methods are to think from the perspective of the cheating traffic party, where they might find loopholes, and what they might be afraid of when cheating, so these two points are put forward.

The first one is click validity judgment. For example, when we promote on iOS, we will match it with IDFA. That is to say, the advertising traffic party will send data on which users clicked on the advertisement to jump to the App Store today, and he will give the IDFA.

Then we also have our own IDFA data on how many users have downloaded and activated the product. Then we match the traffic party's IDFA with our IDFA data. The overlapping activated device numbers are considered to be activations brought about by advertising from the channel.

But if I am the traffic provider and want to cheat, we can provide a large number of IDFAs for matching. As long as the number is large enough, there will always be some overlaps and matches. Of course, there will definitely be some strategies when he provides the data.

So if you want to counter this type of cheating, you have to look at the approximate match rate from click to activation . Imagine a user clicks on an ad, and the conversion rate from download to activation will not be less than one percent. If the click-to-activation conversion rate is too low, we can consider it cheating.

The last one is that the business should agree from a contractual perspective that once cheating is discovered, the amount of compensation will be increased and the punishment will be severe. I think this may to a certain extent block some channels for cheating, because at least it will not let you go through the trouble of testing, and it will also save your time costs.

I wonder if the several methods of channel evaluation that I shared today are helpful to you? If you have any, please leave a comment below to discuss.

The author of this article @51coo compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting!

Product promotion services: APP promotion services, advertising platform, Longyou Games