facebook marketing science professional
experiments
Randomized control trials
A/B test
test vs control group
no control group
cannot monitor lift
test different versions of ads
Lift Tests
conversion lift
split testing
brand lift
uses a poll system
measure incremental conversions
which ad has lowest cost per result
multi-cell lift test
limitations
- not enough statistical power
Reasons
limited frequency/duration
other overlapping campaigns running
too small control group vs test
how to resolve
keep testing
use results
collect more data
- hard to isolate variables
person can be in both test & control group
ex. people located in same household
you can't track all data/unknowns
ex. cash transactions
ex. device switching
test/control groups may have outliers
windsorization - upper/lower bound setting for outliers
Observational method
Definition
done when intervention is not possible
does not interfere with data
limitations
isolating the variable
types
exact match
propensity score
regression adjustment
natural experiments
data availability
click to edit
how to resolve
ave. the diff. observation model results
conduct RTA's regularly and use DDA
collect many data points
how well ad resonated with people
3 ways to design lift test
nest test
single-cell test
multi-cell test
ex. fb ads vs no fb ads
ex. # of frequency exposure
ex. creatives
measurement tools
A/B test
brand lift
conversion lift
marketing mix model
facebook attribution
performing an analysis
Type of attribution model
single-touch attribution model
multi-touch attributionmodel
first click of visit
100% of credit of conversion
gives to the first click
last touch (including last click)
100% credit to the last click,
visit, impression or view
even credit
evenly split across touchpoints
positional
higher credit to first and last click
ex. positional 30. 30% first and last,
the rest is evenly split
time-decay
gives more credit closer to
conversion event
data-driven attribution model