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Worst case is easy. Find the largest single population % * effectiveness and assume that dominates all the others
best case is similarly easy. Assume no overlap and just keep summing the population*effectiveness until you reach the total population.
For the tough bit I made a guess, and just messing about in Excel (attached) quickly...
I've assumed that the users are randomly spread (which is unlikely).
Starting out
universe population: 30,000,000
effectiveness of no advertising: 0%so you have 100% of the population untouched
next step
facebook: population 12m (40%)
effectiveness: 50%
so you'd expect someone at random to have a 20% chance of being hit by facebook
so given you have the entire population left, your chance of them being hit by nothing or facebook is 0% + 20%next, Linkedin
pop: 4m (13.33%)
effectiveness: 75%
random hit by linkedin: 10%
pop hit by linkedin after being missed by the prev lot. (100-20%) * 10% = 8%
total hit population 28%Twitter
pop: 6m (20%)
effectiveness: 40%
random hit by twitter: 8%
pop hit by linkedin after being missed by the prev lot. (100-28%) * 8% = 5.76%
total hit population 33.76%
etc.
1 Attachment
duncs-
That's super useful - thanks.
Question then, if I know UK addressable audience for FB (ie total number of adults with accounts) and I know the number of adults in the UK, I can use that as the effectiveness?
I'll still be using the audience population and platform population for the specific audience (ie 18-50 yr old men who like football - not my actual audience but you get the point).
For example (made up data):
- I know there are 25m adult men in the UK
- I know that there are 20m adult men with FB accounts (80% effectiveness of FB)
- I know there are 15m people in the UK who match m+football criteria (my universe in your sheet)
- I know I can find 13m people on FB who match m+football criteria (my platform population)
- I know there are 25m adult men in the UK
Soul
Wondering if anyone here might have some thoughts on how to look at a problem I have...
I'm trying to understand how I can deduplicate reach across channels. For example, I'm targeting an audience in the UK.
On FB, there are 12m of them and I've reached 50% (or 6m people)
On LinkedIn, there are 4m of them and I've reach 75% (or 3m people)
On Twitter, there are 6m of them and I've reached 40% (or 2.4m people)
Is there a way of calculating the probability of the maximum reach I can achieve when deduplicating the data sets, ie, not knowing the crossover in data between them, how many total people I may have reached? It won't be cumulative (6+3+2.4) and it won't be 100% duplication (6m people) but somewhere in the middle.
I think, for two channels, I can calculate as follows:
P(FB)=6/12
P(LinkedIn)=3/12 - Using largest audience size as a base...
P(FB)xP(LinkedIn) = 6/12x3/12 = 0.125
Total possible reach is therefore 6+3=9
With de-duped reach being 9/12-0.125 = 62.5% or 12x62.5% or 7.5m people
Problem is, I have no idea how to scale this to multiple channels.
@Sam_w - I guess you probably deal with this a lot...