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Valuation Glossary
· Valuation
    · Audience Valuation
    · Efficiency Plot
    · Exposure Valuation
    · Audience Efficiency
· 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
Audience Valuation

Individual audience members can be thought of as having individualized value as potential customers to specific advertisers for specific products. Not all women 18-34 are the same, nor are their buying habits the same. With additional demographic information, one can improve on the age/gender valuation of potential customers considerably. If, for example, information concerning the presence of children in the home was available, the value of women 18-34 to toy manufacturers can be refined.

In fact, each individual person could conceivably have a different value to an advertiser for a particular product depending on the probability of each person becoming a customer for that product, and the degree that that person will remain loyal over time to the advertised product. No two people are the same, and no two audience members are the same. Each has a distinct value to each advertiser.

Advertisers often define their target audience using a customer profile, which includes broader terms and deals with more dimensions than just age and gender. They will speak in terms of primary and secondary customer groups, and these groups will be described using such demographic characteristics as

  • Age
  • Gender
  • Income
  • Education
  • Territory/Timezone
  • Race
  • Occupation
  • Cable connectivity
  • PC ownership
  • Internet connectivity
  • Car/Truck ownership
  • Pet ownership
  • Movie attendance

or a variety of psychographic factors. Using these descriptions, then, media planners and buyers match the media plan to the customer profile in an attempt achieve the media plan objectives.

Audience valuation is a systematic method of assigning value to each of the various demographic characteristics based on the target customer profile of an advertising campaign. Suppose, for example, we consider an advertising campaign for a product that appeals to upper income youthful drivers. The female age weights for this campaign might be selected according to the following.

Audience Valuation - Women
Audience Valuation - Women

According to these weights, the value women peaks between 21 and 34 years old, with a steep decline below 21 and a gradual decline above 34.

Audience Valuation - Income
Audience Valuation - Income

Weights can also be assigned to other demographic characteristics such as income. In this plot, we assume that the household income profile climbs gradually from a low of 0.2 to a high of 0.85 at $45-50K and then declines gradually.

Audience Valuation - Household Ages
Audience Valuation - Household Ages

In the figure above we assign value to audience members based on household composition. Those people who live in homes with children ages 6-14 are of greater value than those who do not. This type of valuation would be appropriate for those products that are purchased by adults for children rather than purchased by the children themselves, such as breakfast cereal.

Audience Valuation - Internet
Audience Valuation - Internet

Value assignments can also be made on yes-no variables, as in the figure above. We assume that for this product, audience members who do not have access to the Internet in their homes are 30% less valuable to the advertiser than are those who do.

Now, if we assume that these individual value assignments represent measures of probability, then, for each audience member, we can combine them to produce a composite value that estimates the overall probability of becoming a product customer as in the table below.

Individual Value Assignments
HH Income HH Ages Internet Age Gender Value
$45K 19,52,70 Y 70 Female 0.34
$13K 10,13,16,32 N 32 Female 0.29
$75+ 3,10,13,42,47 Y 42 Female 0.49
$75+ 10,19,47,60 Y 47 Female 0.44
$55K 13,37,42 N 42 Female 0.47
$68K 1,3,37 Y 37 Female 0.50
$75+ 3,10,13,42,47 Y 13 Female 0.14
$35K 13,16,37 Y 37 Female 0.88
$45K 16,42,52 Y 42 Female 0.61
$35K 52,70 Y 52 Female 0.39
$55K 10,32,37 Y 10 Female 0
$35K 22,42 N 42 Female 0.35
$55K 3,7,32,42 Y 32 Female 1.05
$35K 7,10,32,37 Y 32 Female 1.12
$55K 1,3,37 Y 37 Female 0.58

These audience valuation assignments can be used to compute the efficiency of ads in reaching audiences of value. They are also used, together with exposure valuation assignments, response index values, and cost data, by the optimization process to assign scores to the individual ads in an advertising campaign.

The absolute values selected are arbitrary. What is important for optimization is the relative values. The values in the above income plot, for example, could all be multiplied by 100 without changing the results of the optimization.

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