The New Media Planning is About Picking Combinations of Media.
By Erwin Ephron
The brain takes comfort in simple sorting Ė- I like apples better than oranges. It is less relaxed with conditional complexity Ė- But, if Iíve already eaten an apple, then Iíd like an orange.
Media planning today is awash in conditional complexity, not because of produce, but because of media-mix. This is a migraine-inducing change in the way we need to think about what we do.
The opening words of the plannerís Bible, Toward better Media Comparisons, (ARF 1961), reminds us that media planning is allocation. "This decision is unavoidable, since . . . the use of any medium to any degree implies the avoidance of some other medium . . ." Today we would have to add, " . . . implies the inclusion of some other medium."
The old media planning was about picking individual media. The new media planning is about picking combinations of media (and permutations of media, where sequence of exposure is important). This increases relevant media choice from a manageable few hundred to an unruly few hundred thousand. It also means comparing apples with oranges. Both tasks are well beyond the abilities of a planner with a notepad.
That is the reason for the great interest in media-mix optimizers .
This is an encore. Weíve been there before with television. TV optimizers were a response to three powerful forces -- recency planning, fragmentation and sharp increases in prime time pricing. Recency established reach as the planning goal. Prime time had become too costly for most brands to use for reach. Fragmentation offered the alternative of buying reach through dispersion, but the many possible combinations were more than planners could handle, so optimizers, like SuperMidas and X*pert, were brought in to help do the job.
These same forces are active in pushing media-mix, but the obvious decline of television is the whip. In the face of strong demand and shrinking inventories, the networks have raised prices and added commercials. Both make television less effective. Advertisers know this and are seeking options. The move from TV is not rampant, but it is inevitable, and that is spurring agency interest in better media-mix planning data and optimization.
Just as a second
tastes as good...
Just as a second beer never tastes as good, a second dollar in the same medium never buys as much response. Thatís why the idea of mixing media has always been attractive. Itís in the physics of a flattening sales response curve.
Marketing-mix models will often show a high return-on-investment for a low-budget medium, simply because spending fewer dollars puts it at a better point on the response curve. The general rule is spend more money and the response-per-dollar goes down. Spend less money and it goes up. But, while sales-per-dollar go up, total sales do not, so a brand canít scrooge its way to growth.
Media-mix gives advertisers a way of beating the curve. Where market-driven CPMís reflect relatively comparable media value, spending fewer dollars in more media will produce a greater response.
There are also the under-explored benefits of focus and synergies. Some media may just communicate better to some consumers (reading people versus viewing people) and mixed exposures may have a greater summed effect (sell the car on the TV and the deal in the paper).
These are the arts of media-mix. The science is knowing which medium to add and when to add it. CPM alone offers no guidance, CPM and reach-build together do. This is the routine stuff of optimization.
Because a media-mix optimizer deals with several media, the problems in building one are far more complex than those encountered with TV alone. The big three are database, comparability and frequency value.
The first big
hurdle is which
data to use?
The first big hurdle is reaching consensus on what data to use. This is different from TV optimization, where the only choice is Nielsen. The media-mix database has to contain two things. The "currency" measurement for each medium (e.g., NTI for TV, MRI for print), so that the optimized schedules will price-out. And a means of estimating duplication between media, so that it can calculate reach.
A database with these qualities does not exist and creating one is a bear.
The simplest approach is to use the currency for ratings and within-medium duplication, and calculate the across-media duplication rates from a single-source study like MRI or Simmons. The weakness of this approach is the MRI and Simmons recall measurements of television do not track with NTI meter measurements of television. As a result, the duplication with television data will be poor.
This is a fatal flaw. Because TV is so important in media-mix, duplication with television data controls the optimization.
The alternatives are to use random duplication -- even though we know cross-media duplication is not random -- or to use data fusion.
Fusion is not just theory. It is currently used in the UK, Japan, Latin America and Europe. For years, US media researchers have been told that fusing together surveys designed to measure specific media may be preferable to using a single survey that measures them all. Reigning media statisticians like Gerry Glasser of Statistical Research, Inc. and Marty Frankel of MRI, have long said these statistical techniques could be appropriate in the US. Never-the-less introducing fusion, or any kind of data modeling here, has proven difficult.
As of this writing, you pays your money and takes your choice. There will probably be both an MRI-based single source optimizer (offered by IMS and Telmar), and a Nielsen/MRI fused database (offered by Kantar, the SuperMidas people). 
Data access is another media-mix issue. In TV optimization, all of the players -- buyers and sellers -- had access to the Nielsen database. Turner Cable was especially aggressive in using the new information to switch-pitch the dominant broadcast networks. In media-mix optimization, only agencies and TV sellers are likely to have access to Nielsen. Competing media which can best use the data -- magazines, newspapers, radio, outdoor and internet Ė may be shutout by its high cost.
But there is a crack of opportunity. If competing media can interest Nielsen Media Research in selling limited, affordable NTI data access through the media-mix database, it could open the way to more intelligent competitive selling. Today when other media attempt to sell against television, this lack of access (and lack of familiarity with what the Nielsen data show) is a high brick wall.
the "Time Planning"
of all media.
Because TV is the primary (dollar) medium for most advertisers, TV will set the data standard for all media. To be a candidate, a medium will need a planning period and a TRP definition that conforms reasonably to those used for television. The critical TV planning period is the week, so a full-function media mix optimizer will have to do weekly-reach planning for all media. This requires data reporting magazine issue readers by week and the ability to calculate the week-by-week reader duplication with other magazines and other media. Weekly planning is simple for time specific media, like TV, internet and radio, but it needs to be developed for out-of-home.
Media-mix raises the quiet issue of CPM comparability to a shout. Agencies are far more willing to accept it within a medium (that a cable :30 is equal to a broadcast :30) than across media (that a cable :30 is equal to an internet banner). A wealth of experience with media-mix optimizations done in the 1970's showed that without comparability weights, or pre-defined dollar allocations by medium, the plans produced were so counter-intuitive, they were usually deep-sixed. This flags the importance of adjusting CPMís for media value before attempting to optimize a mix of media.
Several kinds of CPM adjustments need to be considered before an optimization program can produce an intelligent schedule.
Media synergies are
an important reason
to mix media.
Synergy values add a complication to frequency values, but they are essential, because synergies appear to be an important reason to mix media.
In practice, the five weightings can be compacted into two groups: Exposure, commercial and communication weights, which equalize the different measures of audience, and frequency and synergy weights, which establish the value of reach and repetition.
The greatest benefit of TV reach optimizers has been analytical. They opened-up the Nielsen database for the first time and allowed everyone to learn how TVís various pieces work together to build reach. This led agencies to abandon traditional day parts in optimizing schedules and as a result, TV buying changed. Cable and prime time benefited, daytime lost.
Media-mix optimizers will do the same thing on a larger canvas. They will lead us to combine media in complex ways to reach consumers more cost-effectively. They will help us to think about and use media synergies. They will improve media ROI. All of this will play-out in the marketplace. Media-mix optimizers will show advertisers how and why to move dollars from TV to other media.
Advertisers canít wait.
 Actually the first US example was the MARS Pharmaceutical Readership study fused with the Nielsen TV Index database (2002).
- February 28, 2000 -