September 01, 2006
Splintered Audiences and Optimizers
Media industry is besieged by fragmentation with multiple suitors for consumers' time (prime or otherwise). Look at the larger, picture television fragmentation is but  a pimple on the face of larger fragmentation that we face in everyday life. Burgeoning real estate market, multitude of shopping malls replete with varied product choices amongst almost all categories be it perfumes, automobiles or even holiday destinations; even for spending money we have to swim through a deluge of choices.
Fragmentation is ubiquitous and we are living in a splintered society (Fletcher 2006). A television media manager is no different when faced with the realities of a splintered society; aiming to reach the optimum number of prospective consumers in the most cost efficient manner. There is an ocean of content choices for the consumers, to intercept at their discretion - every minute of the day and night, and there are more reception platforms than ever before, within the ever-expanding TV spectrum. This level of diverse TV consumption results in a sea of data that has to be analyzed for buying and planning on a day-to-day basis. There are numerous forces at play dictating media management decisions:
- Fragmentation increases the demand of high rated programs
- Prices are dictated by demand and supply
- It also presents buying opportunity in terms of multiple media sellers
- Multiple media sellers translate into lower prices
Television Reach Optimizers
 Any discussion about optimizers has to start from a basic definition of what an optimizer does. They enable TV planners and buyers to manipulate respondent-level audience data to maximize the purchase and scheduling of TV spots. They do this by identifying the most cost-effective combination of dayparts/programs/genres, sources and vendors available to achieve pre-specified reach and frequency performance goals (Lynch and Woodcock 1997).
As per Ephron (1998) optimization is the child of fragmentation and recency planning. Optimizers like most of the innovations in media research and planning/buying were first developed in Europe inspite of a government controlled media (Oxygen May 2006). These were exported to USA in 1997, which was a watershed year for two basic reason:
- Procter and Gamble placed its US$ 1.2 billion television agency of record assignment up for review which included not only the traditional buying of airtime but also tactical planning. Till that time the same was being done by media buyer in UK and media planner in USA.
The rationale “ We need an optimizer to help us buy TV, because we have so many options that sorting through inventory is beyond our current systems”
- Recency, the idea that advertising works with people who are ready to buy the product, was establishing target reach as the priority planning goal for advertisers
Optimization and Robobuyer
Optimization as a process is defines as the action of finding the best solution within the given constraints and flexibilities. A television optimizers essentially executes three basic functions:
- It is a real time reach and frequency calculator
- It can generate reach curves for set inputs at the daypart, vendor, or program type levels for available demographics
- It calculates the best or optimal daypart or program combinations to achieve set budget or reach goals
 Optimizers are essentially tools to help translate planning strategies optimally into an executable plan, it cannot create strategies.
Optimizers can be touted as the highest expression of technology in media management. Inspite of all the hype surrounding the introduction of Optimizers in U.S.A (1997-99) the concept of Robobuyer did not catch on. The idea of optimizers for buying was to buy reach more efficiently by negotiating around high-cost dayparts and its sellers, but this meant changing a lot of things and so they were happily shipped to planning (Ephron 1999). After their initial avatar the optimizers have undergone many a changes and some agencies also include qualitative factors into optimizing programs as, If every agency used the same [quantitative] optimizer program, then everyone would come up with the same set of solutions. Enactment
- The tightest fit with a particular daypart or programs has its own premium and as optimizers work with incremental reach point planning, any large scale substitution or less precise timing of delivery are totally unacceptable for optimized schedules and defeat the very purpose of the exercise. In effect the enactment of the schedule is hypercritical.
- Buying in bulk is a function of a corporation and optimization happens at the brand level, this requires careful planning and execution.
- Optimization happens on reach and spot selection happens on cost per incremental reach point, the planning, buying and of course selling has to be on Target Reach Point and not Target Rating Point.
Real Time Accurate Data
- Accurate commercial level data is critical for any optimizer to function optimally and a lack of the same can place question marks about the sophistication and quality of its output.
- Dynamic posting: A daily comparison of weights (GRP's) as runs against weights as ordered is essential for reach optimization as, for example a 80 GRP a week plan is of no use if delivered as 40 GRP in week one, 50 GRP in week two and 150 GRP in week three as taking timely action as against the practice of fortnightly or monthly post evaluation and quarterly reconciliation.
All this can be summer up as Poor Data Poor Results!
Sales
Optimizers!!
They are an “upside down” tool which can directly produce sales presentation; in effect they exhibit to the advertiser how a particular vehicle provides more value than any other buy which excludes the same. It has to potential of bringing the next paradigm into media sales providing them with enough sophistication and technology to equal that of the agency on the other side of the table. Robobuyers beware Robosellers are round the corner!
After the break
October issue of Oxygen will discuss global trends in Television Industry, including the impact of technology on content distribution.
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