|
|
|
|
|||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
HOW PPM COULD RESCUE RADIO
ROI Modeling Requires the Better
Audience Measurement
By Erwin Ephron
|
|
I’m
not one to worry about the far off future. Remembering to pick up my laundry
is challenge enough. Then Steven Spielberg casually mentioned civilization’s Man Vs. Machine There are many things machines do far better than people. In media the robotic Radio PPM compared to the all too human diary is a good example. The vagaries of the Diary measurement may be costing Radio millions each year in a simple, but hidden way. Diary reporting is no longer adequate for how audience estimates are used to select media. Marketing Mix Modeling
For media the deciding measure is its contribution to total brand sales, minus the cost of goods, divided by the cost of the medium. It is the equivalent of advertising-delivered profit before taxes or “Payback”. You can’t argue with the goal or the model. Both seem to work. It’s the marketing input data that need attention, especially the Radio data. Why Radio Should Win
Both findings suggest brands should shift marginal TV dollars to other media -- 20% to Radio for example -- to improve total media payback. The dollar shift works in three ways:
There is supporting data for this theory. MMA Study Shows Radio Works An MMA study of multiple brands analyzed by John Phillip Jones in “The Ultimate Secrets of Advertising” showed a medium’s rank in payback was the reverse of its rank in spending. In John’s examples, Radio, with the lowest share of dollars, produced the highest payback. Some marketing mix studies also show this higher Radio payback pattern, but most others don’t. There could be several reasons for this, ranging from the Radio creative to the inadequacy of the data used to represent Radio. I think our friend data may be the problem. Modeling Tracks Change
Diary recall tends to exaggerate listening to leading stations and the data is reported as audience averages for the 12-week survey. The use of Diary data, or the alternative Radio dollars spent, tend to flatten the audience delivery highs and lows marketing-mix models need to work effectively. In contrast the PPM, now in most major markets, can provide measured week-by-week ratings. We’ll know it’s happened when RAB throws a Welcome party for Robots.
- March 1, 2009 -
|
|
|||||||||||||||
|
|||||||||||||||
|
|||||||||||||||
|
|||||||||||||||
inevitable move from a carbon to silicon base, his matter-of-fact way of saying
Robots will take over the earth. At that point Data, now the new creative,
will also be the creative director.
Today many advertisers leap over conventional measurements like audience,
demos and CPMs and go directly to consumer response to make their media decisions.
The tool of choice is Marketing-Mix Modeling. Advertisers take the pieces of
last year’s brand marketing-spend and match them to brand sales and see
how they track. 
Years
of marketing mix modeling have uncovered two planning truths. All marketing
expenditures show diminishing marginal response. Each additional dollar spent
in a medium usually pays back less than the one before. This argues
against media concentration and supports adding other media.
The
second truth is each week added to a schedule usually pays back more than
the week before. This recommends continuous advertising.
Reducing
TV dollars should increase TV-generated payback per-dollar. (Remember
each added dollar pays back less.)
Marketing Mix Modeling works by linking changes in advertising weight to changes
in brand sales. In the case of media, if the audience data fed into the system
are overstated or averaged rather than time specific, the causal link between
changes in media exposure and changes in sales can be lost. 
It will be interesting to see whether Radio’s Marketing-Mix payback
numbers improve as more PPM markets are installed.
