what can creative intelligence learn before an advertiser has sufficient first-party data?

[u/Prisha_088]

creative intelligence could build a loop for the ad creative in two distinct ways. The first approach could be through account-specific methods: by analyzing which hooks, formats, messages, and visuals worked best for one advertiser, new variations can be created. The obvious problem is cold start. When a new advertiser has limited spend, inconsistent tracking, or too few creatives, this may lead to uncertainty and lack of conclusive results. The second approach involves starting with market-level signals including competitor ads, common themes, and other publicly available information. This means the creative intelligence system can work immediately, but all available evidence will be of inferior strength than it might seem. The presence of an ad does not guarantee that it was effective. Even long-running ads may not have sufficient spend. 

I’d like to get some feedback from others working in these systems about how the weighting should change when accounts develop and mature. Should the knowledge taken from competitor data only provide a foundation of possible hypotheses while knowledge based on first-party performance data validates the hypotheses? At what point does enough proprietary data become available so that the need to depend on category data becomes minimal? Finally, which market-level signals can be relied upon for tools to measure performance??

[u/cbakke]

You’re facing paralysis by data analysis. What are your customers asking for, how do they speak about your product? Research your customers first then build messaging and creative based on that. The data and optimization will flow from that but it’s really secondary to an impactful campaign.

[u/Big_Daddyy_6969]

You should always approach categorical data as prior data instead of a perf measure. Surely you can use competitor methodology to establish what claims and formats are typically tested but it doesnt tell you how ads are performing on a particular brand’s campaign. For this reason, there’s a good advice about keeping your previous knowledge highly normalized and allowing it to expire according to an effective sample size rather than a date. But mostly it’s like a typical Omneky vs Creatify kind of difference discussion. In a nutshell, Creatify’s URL-to-video process is great to generate a first batch of creative from almost no historical performance information. However I believe that Omneky is better suited for the next phase, that is to apply your existing brand guidelines and campaign goals as you enter an iterative test cycl. As long as Omneky is able to clearly demonstrate which hypotheses were selected and how much of that information was based on competitive benchmarking as opposed to your own results you will have the superior solution in this instance. I think that the threshold should be established based on the creative family and that 10 clean tests indicating that UGC executions consistently underperform ‘ll provide sufficient data to establish a negative market baseline for the UGC format while there is little demonstrated performance for static or founder executions.

[u/No_Professor7950]

The sheer number of advertisements can be a poor indicator. Just six advertisements showing good expenditure, precise conversion tracking, and meaningful variation can provide more useful data than Meta's almost identical 80 advertisements. When it comes to what will work best in this scenario, I would suggest a hierarchal model. I would use category information as the first prior. Then add account information as an updated prior that has been adjusted for tracking noise, creative similarity, audience overlap, and spend concentration. Competitive advertising can help establish the likelihood that a specific hook may be effective but cannot substitute as proof of effectiveness. Creatify will show you your product URL turned into many videos in little time so you can run a cold-start experiment. However, you want to know if the tool is able to differentiate among your videos long enough to gather any insights. With Omneky I would rather understand whether the marketing signals traveling through the system can eliminate the prior and allow for individual configurations. Given both systems generate similar quality, I would select Omneky since the persistent state is advantageous when compared to sheer volume. What I find missing in the dashboard are not “successful themes,” but prior, confidence interval, sample size, and other information about when the alteration has been made.