7 things I did to increase my top line with video ads
Posted: Wed Dec 11, 2024 5:34 am
Have you ever had an experience like this when running video ads?
When evaluated based on conversions, it pales in comparison to direct response measures.
Attempting to target attitude change indicators but unable to determine target values
Even if we try to aim for reach efficiency, we can't decide on a reasonable target value.
We conducted a brand lift survey for media, but were unable to effectively use it as an evaluation indicator.
If you evaluate it based on its direct contribution to sales, it will be underestimated, and if you separate it from sales, the results will be unclear and will not last... In this way, video advertising is always plagued by the problem of "how to evaluate effects that are difficult to visualize . "
In this article, we will introduce kuwait phone number resource based on case studies, the " 7 things we did to increase our top line through video advertising" while addressing these concerns .
The case studies introduced in this article are based on the following assumptions.
- A store-based business (store reservations) that can measure conversions online
- Video advertising budget is several million yen (per month). In some months, it can reach four digits of yen. Almost all are YouTube ads
- There are no absolute value targets for attitude change indicators (awareness, favorability, etc.) (there is no track record within the company)
- Video ads are focused on reaching new customers. Retargeting is not implemented
In this case, although we understood the importance of verifying the effectiveness through statistical causal inference, we were unable to do so due to human and financial constraints. For measures such as video ads, where advertising effects are expected that cannot be fully captured by advertising effect measurement tools alone, it is ideal to verify the effects after making correct comparisons using statistical methods to verify causal relationships.
However, in this case, the marketing measures for each area, the influence of competitors, and the brand power of the company were very different, so it was difficult to create an environment for correct comparisons. In addition, even if we had tried to approach the issue using other methods, we could not prepare human and financial resources. Due to this background, the result was a story of ``growing video ads while making decisions mainly based on data related to programmatic advertising, and increasing the overall number of inquiries . ''
When evaluated based on conversions, it pales in comparison to direct response measures.
Attempting to target attitude change indicators but unable to determine target values
Even if we try to aim for reach efficiency, we can't decide on a reasonable target value.
We conducted a brand lift survey for media, but were unable to effectively use it as an evaluation indicator.
If you evaluate it based on its direct contribution to sales, it will be underestimated, and if you separate it from sales, the results will be unclear and will not last... In this way, video advertising is always plagued by the problem of "how to evaluate effects that are difficult to visualize . "
In this article, we will introduce kuwait phone number resource based on case studies, the " 7 things we did to increase our top line through video advertising" while addressing these concerns .
The case studies introduced in this article are based on the following assumptions.
- A store-based business (store reservations) that can measure conversions online
- Video advertising budget is several million yen (per month). In some months, it can reach four digits of yen. Almost all are YouTube ads
- There are no absolute value targets for attitude change indicators (awareness, favorability, etc.) (there is no track record within the company)
- Video ads are focused on reaching new customers. Retargeting is not implemented
In this case, although we understood the importance of verifying the effectiveness through statistical causal inference, we were unable to do so due to human and financial constraints. For measures such as video ads, where advertising effects are expected that cannot be fully captured by advertising effect measurement tools alone, it is ideal to verify the effects after making correct comparisons using statistical methods to verify causal relationships.
However, in this case, the marketing measures for each area, the influence of competitors, and the brand power of the company were very different, so it was difficult to create an environment for correct comparisons. In addition, even if we had tried to approach the issue using other methods, we could not prepare human and financial resources. Due to this background, the result was a story of ``growing video ads while making decisions mainly based on data related to programmatic advertising, and increasing the overall number of inquiries . ''