A common question in digital marketing measurement is "what was the
lift?" or "what is the incremental benefit?" from a particular
promotional campaign. Most of us would agree that the alternative of
relying on display ad clicks alone for display response measurement is
just wrong. When measuring display media, incrementality testing is
absolutely essential to properly gauge the baseline viewthrough effect.
In
order to answer this question, a test and control methodology (or
control and exposed) should be used, i.e. basic experimental design
where a control group is held out that otherwise are identical to the
group that is being tested with the marketing message. This is even more
important when marketing "up the funnel" where a last click or even
last touch measurement from a site analytics platfom will mask impact.
Email
marketers have been doing this with their heritage in direct marketing
technqiues. It is often pretty straight forward as the marketer knows
the entire audience or segment population and holds back a statistically
meaningful group; this will enable them to make a general assertion
about what the campaign's actual lift or incremental benefit is. Control
and exposed can also be done with display media if the campaign is
configured properly to split audiences, elimnate overlap and show the
control group a placebo ad. Often PSAs (public service ads) are used,
which can be found via the
AdCouncil.
This
technique is routinely used for qualitative research, i.e. brand lift
study services like Vizu, InsightExpress, Dynamic Logic and Dimestore.
It is the best way to isolate the impact of the advertising; read more
about the challenges of this kind of audience research in
Prof. Paul Lavrakas study for the IAB.
Calculating Lift and Incrementality
Dax
Hamman from Chango and Chris Brinkworth from TagMan were recently
kicking around some numbers to illustrate how viewthrough can be
measured; some of that TOTSB covered a while back in
Standardizing the Definition of Viewthrough.
For the purposes of this example, clickthrough-derived revenue will
be analyzed separately and fractional attribution will not be addressed.
In this example, both control and exposed groups are the same size
though this can be expensive and is usually unnecessary using
statistical sampling.
- Lift is the percentage improvement = (Exposed - Control)/Control
- Incrementality is the beneficial impact = (Exposed - Control)/Exposed
In addition to
understanding the lift in rates like viewthrough visit
rate, conversion rate and yield per impression by articulating
incrementality rate the baseline percentage is revealed - it is just the
reciprocal of incrementality (100% = incrementality % + baseline %).
Incrementality or incremental benefit, can be used to calibrate other
similar campaigns viewthrough response - "similar" being the operative
word.
Executing an Incrementality Test
PSA
studies are simple in concept but often hard to run. Some out there
advocate a virtual control, which is better than no control but not
recommended. This method does provide a homegenous group from an offline
standpoint
so if all things being equal TV and circular are running then it is safe
to assume both test and control should be exposed to the rest of the
media mix equally. ComScore even came up with a clever zero-control
predicted metholdology for their SmartControl service.
Most
digital media agencies have experience designing tests and setting up
ad servers with the exact same audience targeting criteria across test
and control. Better ad networks out there encourage incrementality
testing and will embrace the opportunity to understand their impact
beyond click tracking.
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