# Multi Channel Attribution Modelling: Using A Simple Probabilistic Model

Here, a method is described by which the influence of online-marketing-channels/campaigns (for example: "Display" as a channel for Display-Ads, "SEA" as a channel for Search-Engine-Ads etc.) on a binary criterion (for example: 1 = positiv User = User buys a product in online-store vs. 0 = negative User = User does does not buy a product in online-store) can be calculated. The described method is described as more easy and simple than conducting a logistic regression.

With the first equation, you can calculate for every channel the share of positive users (=users who had contact to this channel AND bought the product) on all users which had contact to this channel: Example: 1000 Users had contact to the channel "Display" and 400 of them bought the product. Hence, the probability for this channel is 0.4 (=400/1000 = 400/(400 + 600))

For considering overlapping between channels, you use equation two, which includes the second-order interaction term: What i am not understanding and at which point i need your help is the following: For calculation the contribute of each channel to the buy-probability (= C(xi); and this is what is important, because every marketer wants to know, which online marketing channel/campaign "works" and converts a internet-user to a buyer of the product) the following equation is used: What does mean "The contribution of channel i is then computed at each positive user level" and what does mean "for a particular user"? With equation 1 and 2 we have calculated terms on a aggregated level and suddenly, in equation 3, they talk about calulation on "User level". What does this mean? And what is "N"? When i have 3 channels in total, is N = 2 (3 channels - 1 = 2 channels)? This "user level"-thing is irritating. I thougt i have to calculate for a specific channel - for example: channel "Display" - the terms according to equation 1 and 2 and just enter this terms in equation 3, and then i get C(display) = [myresult] But is it really so easy?

• dont know, if i understood the part "rom each user across all your channels you sum that up to get the total value of each channel", therefore: let us assume we have (to make it easy to describe just) 5 Users and 2 Channels and all 5 Users have contact to channel 1, but only 3 Users have contact to Channel 2. Hence, we will get 5 times a C for Channel 1 (hence: C(Channel_1)) and 3 times a C for Channel 2 (hence: C(Channel_2)). Then, i have to sum up the 5 Cs for Channel 1 to get the Contribution of Channel 1, and sum up the 3 Cs for Channel 2 to get the Contribution of Channel 2. Correct? – flobrr Feb 3 '17 at 12:24
• Yes you're right, you don't have to use revenue. I guess that's just what I have hence I'm used to that. In the scenario you have above, yes, you would sum them up. If instead of conversion you had a \$amount to it, you would probably want to multiply the probabilities by the \$ and then sum up the \\$s. You'll probably want to re-normalize to 1 after summing up your C's (when comparing C1 and C2). – Sharon Feb 6 '17 at 14:32