About the strategy for choosing the type of advertisement
When advertising on Facebook, it is crucial to choose the right delivery strategy. This is not only related to how your advertising budget is allocated, but also affects the effectiveness of the advertising. Here are some suggestions based on different budget levels:
Friends with a low budget
If you are just starting to run advertising and have a limited budget, I personally recommend starting with A/B testing (ABO). ABO allows you to manually set the budget and bid for each ad group, which helps to test different audiences and creatives with a limited budget. The advantage is that the consumption will not be particularly high, and the disadvantage is that you need to observe more.
Friends with a moderate budget
If you have a certain budget, you can consider using CBO. CBO is like an autopilot system that automatically allocates the budget to the best performing ad group to maximize the advertising effect. I personally also tend to use it to test materials, or to run ads that already have models, which will at least make your advertising data for a period of time relatively good, that is, ROAS.
Friends with sufficient budget
If you have sufficient budget, it is recommended to run a CBO and an ABO campaign at the same time. It's like having both an autopilot mode and the fun of manual driving. With CBO, you can use the automation of the system to allocate budget to the best data, while ABO allows you to conduct more refined tests. For example, you choose a combination of CBO and ABO activities, and find that the ABO activity performs poorly during the testing phase, while CBO steadily brings you a higher conversion rate. So the final choice depends on everyone. Everyone's delivery strategy is more or less different, but one thing I believe everyone must be targeting ROAS, so please pay more attention to your CPC, CPM, and CTR during the testing phase to avoid unnecessary consumption.
Audience selection strategy
Facebook provides a variety of audience targeting methods, including interest audiences and automatic audience selection (ASC):
Interest audience
Applicable situation: Applicable to those interests that users may maintain for a long time, such as fitness, yoga, running, etc. For example, fitness enthusiast 萂, her interests may have remained unchanged for the past five years.
Advantages: These interests are relatively stable and the audience may be more accurate. For example, 萂 has been buying yoga-related products in the past year, indicating that her interest in yoga is continuous.
Disadvantages: The audience may not necessarily be the current interest, and the data may be lagging. In addition, the size of the interest audience may be limited, limiting the coverage of the advertisement. For example, I found that his advertisement performed well in the interest audience, but soon reached the audience limit and could not be expanded further.
Broad Targeting
My suggestion: Start testing with Broad Targeting.
Advantages:
The coverage is wide, which is conducive to machine learning. Just like when you run an advertisement for yoga clothing, you choose Broad Targeting. Although your target is yoga enthusiasts, through Broad Targeting, you will find that you have found more potential yoga novices.
Avoid poor results due to improper audience selection. I once had poor results when selecting a specific interest audience, but after switching to Broad Targeting, the advertising effect was greatly improved.
It is easier to judge the performance of the material, and there is no need to worry about whether the audience selection is correct. You may find some materials that perform well in a specific group of people through Broad Targeting, but you are not sure whether these audiences are all your target market.
Providing a larger audience range will slow down the decay of the material, leaving room for the preparation of the next material. At this time, you have tested multiple materials through airdrops and found that some materials perform well among a wide audience, extending the life cycle of the materials.
Here, it is important to emphasize why it is better to test with airdrops. First of all, it must be the materials first and then the audience. If you empower the audience you think is good without knowing the materials, then the final result will be unsatisfactory and your analysis points will be more confusing. Another thing is that if you are a hot product, such as toys, pets, clothing, etc., it may be better if you test the audience and materials together to bring you what you think is a good conversion, but believe me, this is definitely not the best data. On the contrary, if you are a cold product, such as nunchakus and plastic sticks, then your audience must be very single and the limitations are relatively large. It is better to airdrop directly. Sometimes everyone still has to believe in Meta's advertising algorithm.
Testing strategy
ABO test
Create a campaign with 3-5 ad groups, each group with a budget of $30, and 3-5 different ad materials in each group. Run for at least 3 days to leave enough time for machine learning. If the effect is not good, you can end it early and start again the next day. So sometimes don't deny the material you spent a lot of time making, maybe it will bring you unexpected results the next day.
CBO test
Set up a CBO campaign with a budget of $50. Place 3-5 ad groups in the campaign, each containing 3-5 different materials. The testing method is similar to ABO, but the budget allocation is handled automatically by the system. This is what I said about my personal CBO. Generally, when testing materials, the system will select the best one to run. Of course, you can't just leave it alone. You must observe more in the early stage.
Why test ABO and CBO at the same time
Different advertising accounts may have different preferences for ABO or CBO. Some accounts perform better under ABO, while others are more suitable for CBO. It is very clear to look at CPC and CPM.
Expansion strategy
Once the testing phase is completed, you can start to expand the amount of advertising:
Horizontal expansion
You can add new campaigns to test different audiences and creatives. Keep the series with good performance and gradually eliminate the ones with poor performance. Generally, after a campaign has good results, I will gradually add 3 new campaigns to test different audiences and materials.
Vertical expansion
Increase the budget in the campaigns with good performance and gradually increase the delivery volume. If I find that a campaign has good data and ROAS in the airdrop, I will generally adjust the budget. I personally adjust the budget by percentage and double it directly. Of course, it can be adjusted flexibly according to performance. If it is a large amount, I may set rules to run.
Interest audience
When the system accumulates a certain amount of data, you can test some interest groups to speed up the learning speed of the account. For example, if you are in the clothing business, you can use clothing multi-word groups to test, or you can use similar related words to test (it is recommended that this type of people can judge the detailed demand and economic ability).
ASC
Once 2-3 ad groups have accumulated 50 conversions, you can turn on ASC, copy the order materials into it, and start running with a higher budget (such as $500). There is almost no need for further optimization. There is another point here. I guess everyone has tested it. At least my personal experience is still good. When you put several materials for testing, the best material will have better results if you run it with ASC alone. Remember to delete the original material. At the same time, other materials that do not run well will also get better. Of course, you can also change it directly.
When advertising on Facebook, it is crucial to choose the right delivery strategy. This is not only related to how your advertising budget is allocated, but also affects the effectiveness of the advertising. Here are some suggestions based on different budget levels:
Friends with a low budget
If you are just starting to run advertising and have a limited budget, I personally recommend starting with A/B testing (ABO). ABO allows you to manually set the budget and bid for each ad group, which helps to test different audiences and creatives with a limited budget. The advantage is that the consumption will not be particularly high, and the disadvantage is that you need to observe more.
Friends with a moderate budget
If you have a certain budget, you can consider using CBO. CBO is like an autopilot system that automatically allocates the budget to the best performing ad group to maximize the advertising effect. I personally also tend to use it to test materials, or to run ads that already have models, which will at least make your advertising data for a period of time relatively good, that is, ROAS.
Friends with sufficient budget
If you have sufficient budget, it is recommended to run a CBO and an ABO campaign at the same time. It's like having both an autopilot mode and the fun of manual driving. With CBO, you can use the automation of the system to allocate budget to the best data, while ABO allows you to conduct more refined tests. For example, you choose a combination of CBO and ABO activities, and find that the ABO activity performs poorly during the testing phase, while CBO steadily brings you a higher conversion rate. So the final choice depends on everyone. Everyone's delivery strategy is more or less different, but one thing I believe everyone must be targeting ROAS, so please pay more attention to your CPC, CPM, and CTR during the testing phase to avoid unnecessary consumption.
Audience selection strategy
Facebook provides a variety of audience targeting methods, including interest audiences and automatic audience selection (ASC):
Interest audience
Applicable situation: Applicable to those interests that users may maintain for a long time, such as fitness, yoga, running, etc. For example, fitness enthusiast 萂, her interests may have remained unchanged for the past five years.
Advantages: These interests are relatively stable and the audience may be more accurate. For example, 萂 has been buying yoga-related products in the past year, indicating that her interest in yoga is continuous.
Disadvantages: The audience may not necessarily be the current interest, and the data may be lagging. In addition, the size of the interest audience may be limited, limiting the coverage of the advertisement. For example, I found that his advertisement performed well in the interest audience, but soon reached the audience limit and could not be expanded further.
Broad Targeting
My suggestion: Start testing with Broad Targeting.
Advantages:
The coverage is wide, which is conducive to machine learning. Just like when you run an advertisement for yoga clothing, you choose Broad Targeting. Although your target is yoga enthusiasts, through Broad Targeting, you will find that you have found more potential yoga novices.
Avoid poor results due to improper audience selection. I once had poor results when selecting a specific interest audience, but after switching to Broad Targeting, the advertising effect was greatly improved.
It is easier to judge the performance of the material, and there is no need to worry about whether the audience selection is correct. You may find some materials that perform well in a specific group of people through Broad Targeting, but you are not sure whether these audiences are all your target market.
Providing a larger audience range will slow down the decay of the material, leaving room for the preparation of the next material. At this time, you have tested multiple materials through airdrops and found that some materials perform well among a wide audience, extending the life cycle of the materials.
Here, it is important to emphasize why it is better to test with airdrops. First of all, it must be the materials first and then the audience. If you empower the audience you think is good without knowing the materials, then the final result will be unsatisfactory and your analysis points will be more confusing. Another thing is that if you are a hot product, such as toys, pets, clothing, etc., it may be better if you test the audience and materials together to bring you what you think is a good conversion, but believe me, this is definitely not the best data. On the contrary, if you are a cold product, such as nunchakus and plastic sticks, then your audience must be very single and the limitations are relatively large. It is better to airdrop directly. Sometimes everyone still has to believe in Meta's advertising algorithm.
Testing strategy
ABO test
Create a campaign with 3-5 ad groups, each group with a budget of $30, and 3-5 different ad materials in each group. Run for at least 3 days to leave enough time for machine learning. If the effect is not good, you can end it early and start again the next day. So sometimes don't deny the material you spent a lot of time making, maybe it will bring you unexpected results the next day.
CBO test
Set up a CBO campaign with a budget of $50. Place 3-5 ad groups in the campaign, each containing 3-5 different materials. The testing method is similar to ABO, but the budget allocation is handled automatically by the system. This is what I said about my personal CBO. Generally, when testing materials, the system will select the best one to run. Of course, you can't just leave it alone. You must observe more in the early stage.
Why test ABO and CBO at the same time
Different advertising accounts may have different preferences for ABO or CBO. Some accounts perform better under ABO, while others are more suitable for CBO. It is very clear to look at CPC and CPM.
Expansion strategy
Once the testing phase is completed, you can start to expand the amount of advertising:
Horizontal expansion
You can add new campaigns to test different audiences and creatives. Keep the series with good performance and gradually eliminate the ones with poor performance. Generally, after a campaign has good results, I will gradually add 3 new campaigns to test different audiences and materials.
Vertical expansion
Increase the budget in the campaigns with good performance and gradually increase the delivery volume. If I find that a campaign has good data and ROAS in the airdrop, I will generally adjust the budget. I personally adjust the budget by percentage and double it directly. Of course, it can be adjusted flexibly according to performance. If it is a large amount, I may set rules to run.
Interest audience
When the system accumulates a certain amount of data, you can test some interest groups to speed up the learning speed of the account. For example, if you are in the clothing business, you can use clothing multi-word groups to test, or you can use similar related words to test (it is recommended that this type of people can judge the detailed demand and economic ability).
ASC
Once 2-3 ad groups have accumulated 50 conversions, you can turn on ASC, copy the order materials into it, and start running with a higher budget (such as $500). There is almost no need for further optimization. There is another point here. I guess everyone has tested it. At least my personal experience is still good. When you put several materials for testing, the best material will have better results if you run it with ASC alone. Remember to delete the original material. At the same time, other materials that do not run well will also get better. Of course, you can also change it directly.
