lecture 2

Question 1

How to evaluate the performance of marketing

Answer 1

each campaign / channel evaluated on incremental profit that it produces relative to its cost

ROI = incremental profit due to advertising - cost of advertising / cost of advertising

Question 2

Incremental sales

Answer 2

additional saels made due to advertising over and above what would have been sold without advertising

Question 3

Incremental profit

Answer 3

additional profit made due to advertising over and above what would have been sold without advertising

Typically a function of incremental sales

Question 4

Conversion journey (couple example)

Answer 4

you swipe online
get a coffee date
Get a movie date
Proposal
Marriage
yay

Question 5

Marketing attribution

Answer 5

is the process for determining which marketing touchpoints led to a conversion

Question 6

Attribution models

Answer 6

are the rule or set of rules that determines how conversion credit is assigned to different marketing touchpoints

Question 7

Seeking answers to the following strategic question: How can an analyst attribute credit to multiple campaigns that (may have) contributed to generating a conversion?

Answer 7

approaches to the solution

Rule based attribution
Data-driven models

Question 8

last touch attribution

Answer 8

attribution looks backward from each conversion to find the last ad that the user saw (or clicked on) prior to the conversion

Question 9

Limitations of last touch attribution

Answer 9

1) other ads may have influenced customer and contributed to the sale
2) all sales are treated as incremental… would not have bought if one did not see ads
-less well recognized
3) unfairly favours channels that tend to show ads towards the end of path to purchase
such as seach, or ads served due to targeting

Question 10

When does last touch “work”

Answer 10

accurate measure of ad response when:
all sales are incremental; that means no sales owuld happen without the advertising

Effect of ads on behaviour is short-lived and ad exposures are spaced out over time
- no “assists” from other advertising channels

Question 11

First touch attribution

Answer 11

first touch (first click( attribution looks backward from each conversion to find the first ad that the user saw (or clicked on) prior to the conversion

Question 12

Linear attribution

Answer 12

attribution looks backward from each conversion to find each ad that the user saw (or clicked on) prior to the conversion, assigning them equal weight

Question 13

Position based attribution

Answer 13

attribution looks backward from each conversion to find each ad that the user saw (or clicked on) prior to the conversion, assigning higher weight to the first adn last

Need to decide on what the higher weight is

Commonly seen: 30% or 40% for both first and last

Aka U-shaped attribution

Question 14

Time decay attribution

Answer 14

time decay: attribution looks backward from each conversion to find each ad that the user saw or clicked on prior to the conversion, assigning higher weight to more recent ads

Question 15

limitations of rule based attribution

Answer 15

rule based solutions are inflexible and unable to distinguish between the true low and high impacct touch-points

Leads to an inaccurate division of credit
Ignores all customers that dont convert

Analyst/manager decides the attribution
Can pick something to show the results one needs

Question 16

Data driven models (key difference)

Answer 16

dealing with mid-journey funnel in a non ad-hoc way
and mid funnel parts of customer journeys matter

Example models:
1) markov attribution models
2) shapely value attribution

Question 17

Markov attribution model

Answer 17

a probabilistic model that represents buyer journeys as a graph

Graphs nodes are touchpoints
Graphs edges are observed transition between those states

first state webinar -> second state ad click

the the big example with websize campaign y and z and the webinar in the middle

Question 18

Markov attribution models measuring the removal effecet

Answer 18

Simulate removing an advertising campaign from a graph

Measure change in success rate (i.e. change in KPI)
(the how to is beyond the scope of the class)

attribution value of a given campaign is then

A = total value of KPI * Campaign removal effects/Sum of all removal effects

Question 19

shapely value attribution:

Answer 19

finds each ad campaigns marginal contribution, averaged over every sequence where the campaign could have been shown

Different campaigns work together in cooperation to generate value (i.e. some KPI)

Based on co-operative game theory (beyond the scope of this course)

Key idea is : measuring each campaigns marginal contribution

Determined by what is gained or lost by removing them from a coalition of campaigns

Order of ads doesnt matter (in the standard setup)

Question 20

Two channel example (conversions shapely value)

Answer 20

facebook ads attracts 5 ppl
Google ads attracts 10 ppl

Conversion of facebook and google ads gets 45 ppl

Marginal Contributions
The Shapley value considers the marginal contribution of each player (ad channel) based on different possible orderings.

Facebook’s Marginal Contribution:

If Facebook joins first: Its contribution is 5 (because without Google, Facebook attracts 5 people).
If Google is already included, and Facebook joins second: The total combined contribution is 45, but Google already brought in 10. So, Facebook’s marginal contribution is 45−10=35

Google’s Marginal Contribution:
If Google joins first: Its contribution is 10 (because without Facebook, Google attracts 10 people).
If Facebook is already included, and Google joins second: The total combined contribution is 45, but Facebook already brought in 5. So, Google’s marginal contribution is
45−5=40
45−5=40.
Shapley Value Calculation:
For each channel (player), we calculate the average of its marginal contributions in different orders:

Facebook’s Shapley value:

ShapleyvalueforFacebook=0.5×(5+35)=20

Google’s Shapley value:

ShapleyvalueforGoogle
=0.5×(10+40)=25

Marginal Contributions:
Facebook’s marginal contribution: 20
Google’s marginal contribution: 25