TikTok Advises Advertisers to Rely on Non-Traditional Ad Measurement Methods for More Valid Campaign Performance Insights - Visualistan -->

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"Discovery does not mean clicks," says TikTok as it shares latest insights based on how people are using its app. The social media company is letting advertisers know that current ad measurement solutions are failing to capture a significant percentage of purchases driven by its platform. 

 


After collaborating with external researchers, TikTok discovered that last-click attribution undervalues its conversions by 73%, and 79% of the purchases are not even reported through commonly prevalent attribution methods.

 


So, what does actually report accurate campaign outcomes? According to TikTok, view-through and click-through attribution can provide more accurate data and assist advertisers in moving their ads through the learning phase quicker. Moreover, advertisers may also need to try a variety of approaches before finding the one that is most suitable to their campaign. In other words, different levels of data insight could help inform their ad strategies.

 

The click-through attribution windows that TikTok offers are: 1-day, 7-day, 14-day and 28-day, and it is worth noting that longer attribution windows capture more events in campaign reporting. No view-through attribution options include 1-day and 7-day.

 

TikTok also has a Performance Comparison tool that shows and compares real world overviews of using different attribution measurements. The company also advises advertisers to refrain from making changes to their ads before sufficient measurement time has passed. This is because campaign performance improves with time as more attributed events are captured, therefore frequent changes before a certain period of time can lead to fluctuations in performance.

 

In a nutshell, if you are an advertiser on TikTok looking for accurate insights into how your ads are driving audience response, you may want to rely on attribution measurement methods other than traditional ones.

TikTok Advises Advertisers to Rely on Non-Traditional Ad Measurement Methods for More Valid Campaign Performance Insights



"Discovery does not mean clicks," says TikTok as it shares latest insights based on how people are using its app. The social media company is letting advertisers know that current ad measurement solutions are failing to capture a significant percentage of purchases driven by its platform. 

 


After collaborating with external researchers, TikTok discovered that last-click attribution undervalues its conversions by 73%, and 79% of the purchases are not even reported through commonly prevalent attribution methods.

 


So, what does actually report accurate campaign outcomes? According to TikTok, view-through and click-through attribution can provide more accurate data and assist advertisers in moving their ads through the learning phase quicker. Moreover, advertisers may also need to try a variety of approaches before finding the one that is most suitable to their campaign. In other words, different levels of data insight could help inform their ad strategies.

 

The click-through attribution windows that TikTok offers are: 1-day, 7-day, 14-day and 28-day, and it is worth noting that longer attribution windows capture more events in campaign reporting. No view-through attribution options include 1-day and 7-day.

 

TikTok also has a Performance Comparison tool that shows and compares real world overviews of using different attribution measurements. The company also advises advertisers to refrain from making changes to their ads before sufficient measurement time has passed. This is because campaign performance improves with time as more attributed events are captured, therefore frequent changes before a certain period of time can lead to fluctuations in performance.

 

In a nutshell, if you are an advertiser on TikTok looking for accurate insights into how your ads are driving audience response, you may want to rely on attribution measurement methods other than traditional ones.

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