Overview
The Data Intelligence team can assist proving the value of a channel or tactic
Marketing Science methods can be deployed to achieve this:
Linear Regression looks at historical data and is fast and easy to do
Causal Impact is essentially a fancy pre/post test but is more accurate
The purpose of this is to prove the value of a tactic when performance is “poor”
If attribution says Facebook has a $0.50 ROAS and client wants $2.50
If in-platform says that Trade Desk has delivered 0 conversions
If the client “just doesn’t feel the impact on the business”
The Analysis Comes Back Good
The linear regression analysis (LRA) or causal impact analysis (CIA) come back
The results look good or are better than attribution (e.g. $2.50 instead of $0.50)
Work with the data intel team to explain and present these findings to client
If the analyses convinces the client that the channel is actually effective:
Apply coefficient to in-plat and last click numbers
The total volume of conversions or revenue must remain the same
The proportion "attributed" to each channel will change
At first, reduce revenue from organic and/or direct
Potentially scale up spend if the channel is significantly outperforming goal
Example: business goal CAC of $50. Last click says Meta CAC is $150, so we reduce budget. But an LRA shows that the “real” CAC is $25. We can 2-3x our budgets without Meta’s CAC of $50 (however total blended CAC of all marketing might increase with more spend, be careful here)
Scaling spend helps the client grow their business as the ROI is positive
If the analysis does not convince the client that it is effective:
Recommend that we validate the findings through experimentation
The easiest and most common experiment is through a Matched Market Test
Some channels can’t MMT (e.g. Influencers, Affiliate, PR, Email)
Significant budget changes or other holdout tests can work instead
Run a second measurement analysis (generally a CIA) to validate
If experiments are not allowed and the client wants to reduce or remove the channel altogether, then we can still treat its removal as an experiment:
Remove the channel per their request
Work with the data intel team to measure its absence
Report on latent impact of removing channel weeks or months later
The Analysis Comes Back Bad
Explain the rationale as to why it might not be effective, adhering to what we think is the true likely cause given our expertise. Examples:
Investment level is too low
Creative isn't aligned with message or audience
Account hasn’t had time to optimize
Recommend that we validate these findings through experimentation (see above)
Come prepared with pivot solutions and other services that might assist the client’s needs in the absence of losing this channel (swap CTV for YouTube, etc)