Audience Watch For Television Programming and Advertising Professionals
Home Glossary About Audience Watch

· Audience Data
· Audience Distribution
· Audience Flow
· Audience Sample
    · Sample Analysis
    · Sample Details
    · Drill down
    · Unified Sample
· Audience Tracking
· Co-viewing
· Demographic Group
· Exposure Group
· Impressions
· Influence Index
· Optimization
    · Integrated
    · Transparent
    · Spot Optimization
    · Steps
· Program Environment
· Program Trends
    · Defection
    · Experience
    · Program Loyalty
    · Retention
· Quad Analysis
· Reach
    · Effective Reach
    · Incremental Reach
    · Network Reach
    · Incremental Net Reach
· Recall
· Recency Theory
· Response Index
· Roadblock
· Valuation
    · Audience Valuation
    · Efficiency Plot
    · Exposure Valuation
    · Audience Efficiency
· Viewing Quintile
Optimization Focus

Optimizers can operate at either of two levels of detail: 1) Regional (daypart optimization), or 2) Detailed (spot optimization). The Audience Watch System includes a detailed advertising plan optimizer.

1. Regional (daypart) Optimizers

These are frequently referred to as daypart optimizers, meaning that the optimizer allocates advertising to dayparts over a some period of time, such as a week or a month. When the optimization process is complete, a media planner still needs to allocate the time in each daypart to specific programs.

These regional optimizers often make one or more of the following assumptions, which typically are not accurate:

  • Uniform CPM levels within a daypart.
  • Limited switching between programs within a daypart.
  • Reliable forecasting of individual viewing habits by daypart.

2. Detailed (spot optimization)

This class of optimizer is capable of producing a detailed advertising plan, complete with the specific ad timing to achieve an optimal plan.

This type of optimizer can be somewhat more powerful in defining optimal advertising plans because it does not make the assumptions outlined above. Detailed CPM values, switching habits, and detailed viewing data can be considered in the optimization process.

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