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In general I believe people overthink / overcomplicate organic attribution to paid marketing on mobile, usually because they overestimate how many organic installs they actually receive 1/X
Organic discovery is almost nonexistent for most apps — in most cases, the “organic baseline” (# of organic installs received independent of any other marketing) is negligible
Testing this is fairly straightforward — shut off marketing in a representative but not commercially critical geo (eg AU, CA) and watch what happens to organics
In some verticals / categories eg social casino / classic games / various utility apps, organic keyword search does provide somewhat meaningful inbound install volume, but my point is always: why not use that to augment paid marketing?
The reality for most apps is that if paid marketing went to $0, so would organics; Ive never seen a case where organics didnt increase / decrease as a measurable function of paid
Given that an advertiser has validated this notion via testing, they can attribute organic installs / revenue to paid with either of two strategies: dollar to dollar or install to install
Dollar to dollar: organic revenues are distributed to paid campaigns on the basis of how much revenue those campaigns deliver eg FB campaigns deliver 60% of revenue so 60% of organic revenue is attributed to them. Idea is that higher-value campaigns generate higher-value organics
Revenue to install: organic installs (and thus revenue) are distributed to paid campaigns on basis of how many installs those campaigns deliver eg FB generates 60% of paid installs so 60% of organic revenue attributed to it
(note that in both these cases, assumption is all channels are exploited to max possible extent against some ROAS goal. Obv this doesnt work if campaigns arent being run profitably. Also, each needs to be done at geo / platform level)
Attributing organic revenue simply means adding that revenue to paid channel revenue, which ultimately increases channel LTV and increases bid capacity and thus install volume
I like the simplicity of these types of “macro” models because: 1) they accommodate all traffic, 2) respond to changes in traffic composition, 3) are implementable with standard MMP data
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