Facebook Ad User Value Division and Corresponding Optimization Methods
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What is user value:
Facebook has accumulated a large amount of user data, and by industry, different products give users a value estimate, and then based on the estimated value, and advertisers bid to determine the exposure of the people.
The goal is to buy a Maserati, so the spending power is high enough, and the value of the person who likes luxury cars is certainly high.But if the promotion target is hair planting, the user who buys Maserati above is not as valuable as the poor ghost like me.And if the goal of promotion is micro-lending, then perhaps we are not as good as the village of Xiao Wang, Xiao Li and so on.
User value is not the user's money is worth a lot, but according to different advertisers, conversion will to divide, is the corresponding value on Facebook.
The same user in different industries, the value of different advertisers is not the same, Facebook will be the data out, and then reasonable to the user to show the most valuable ads, in order to ensure that his revenue is maximized.
How to determine user value:
Facebook has different dimension data for users, including hobbies, gender, education, and even income levels, the most important is that there are a large number of advertisers back to the platform data, including user buying behavior, game payment behavior, etc. , through a large amount of data accumulation, Facebook estimates the value of each user in that industry by industry, and should be able to calculate the value of similar users in a clustered manner.
Most advertisers here actually send back all their data to Facebook to make it easier for them to learn more, and also allow Facebook to accumulate more data, usually the game will directly return their top-up orders to Facebook.
And Facebook starts automatic premium matches directly in subsequent SDK updates, collecting all the form data in the past to accumulate more data.
How Facebook uses these user value data:
Not only in different industries, but also in different user behavior habits, corresponding to different optimization goals to expose the user ads.
For example, in the e-commerce industry, if the target is a user to place an order, then Facebook will choose to prefer the user to place the order of shopping exposure ads (and based on the category /commodity may be the most converted users to expose), conversion of the highest willing users, his user value is the highest.
If the target is to follow the fan page, Facebook will pick out the exposure of users who particularly like to like to like it.
If the goal is app downloads, select users who like the download to expose it.
How to optimize your ads:
1, different material direction with interests, the system can learn from different directions to the system feel the most valuable users, get more exposure.
2, different bid methods, bid cap, cost cap, lowest cost respectively, with different budgets, in the controllable price range, reach different value steam, get more exposure. Regarding the different bidding methods there are also things that can be expanded to say, refer to the previous post:
Also note that your bid doesn't just represent your conversion goals, it also represents the value of the crowd you want to reach, and your actual price depends on your conversion rate and budget. Theoretically, if the conversion rate is high enough, the budget is more appropriate, even if your high final actual price will not really be the same as your bid, perhaps much lower, here the price and the overall target crowd of cpm related, not sure what Facebook price algorithm here is, hope to have a professional reminder.
3, different delivery objectives, AEO, APP INSTALL (CPI and OCPM), remarketing, Fanpage, POST and so on, to obtain greater exposure, but here is not necessarily every goal is the advertiser really needs, choose the right one.
4, the correct in-app dot, reference to Facebook's standard dot, such as add shopping cart, place orders, pay and so on, so that you can also choose these standard events when running AEO as an optimization goal, but also convenient system learning.
But it seems that this optimization method is not much related, after all, whether or not you understand how To distinguish user value, you have to be so optimized.
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