by Reach
In the application of statistics to advertising and media analysis, reach refers to the total number of different people or households exposed, at least once, to a medium during a given period. Reach should not be confused with the number of people who will actually be exposed to and consume the advertising, though. It is just the number of people who are exposed to the medium and therefore have an opportunity to see or hear the ad or commercial. Reach may be stated either as an absolute number, or as a fraction of a given population (for instance ‘TV households’, ‘men’ or ‘those aged 25–35’).
For any given viewer, they have been “reached” by the work if they have viewed it at all (or a specified amount) during the specified period. Multiple viewings by a single member of the audience in the cited period do not increase reach; however, media people use the term effective reach to describe the quality of exposure. Effective reach and reach are two different measurements for a target audience who receive a given message or ad.
Since reach is a time-dependent summary of aggregate audience behavior, reach figures are meaningless without a period associated with them: an example of a valid reach figure would be to state that “[example website] had a one-day reach of 1565 per million on 21 March 2004” (though unique users, an equivalent measure, would be a more typical metric for a website).
Reach of television channels is often expressed in the form of “x minute weekly reach” that is, the number (or percentage) of viewers who watched the channel for at least x minutes in a given week.
Reach is the number of people in the Media Market that will likely be exposed to one Spot. Estimating reach is tricky because when you run an ad multiple times, the same person may see the ad more than once but you only want to count them once in Reach. There are many different methods to estimate reach. Most rely on software.
Cumulative/Frequency Reach
Cumulative frequency analysis is the analysis of the frequency of occurrence of values of a phenomenon less than a reference value. The phenomenon may be time- or space-dependent. Cumulative frequency is also called frequency of non-exceedance.
Cumulative frequency analysis is performed to obtain insight into how often a certain phenomenon (feature) is below a certain value. This may help in describing or explaining a situation in which the phenomenon is involved, or in planning interventions, for example in flood protection.
This statistical technique can be used to see how likely an event like a flood is going to happen again in the future, based on how often it happened in the past. It can be adapted to bring in things like climate change causing wetter winters and drier summers.
For impressions, use this formula:
Impressions = Cost / (Clicks Per Impression/1000)
And for frequency, use this formula:
Frequency = Impressions / Unique Users
Discrete & Cumulative distribution
For the different distributions which I will cover in these articles, they are classified as either a Discrete Probability Distribution or Continuous Probability Distribution. A simple way to understand if your data is discrete or continuous is to answer the following question: “Are the number of your outcomes finite?”
If the answer to the above is yes, then you have a discrete dataset. Otherwise, you likely have a continuous dataset.
To put things in a marketing perspective, imagine that you are looking at the number of likes that your Facebook campaign generated, you know that likes are finite because you count the number of likes as 1,2,3, …100, there is no chance of getting a 1.2 or 2.6 likes or other smaller value that isn’t exactly 1 or 2. These outcomes are a set of values rather than a continuous length of values.
Example of a continuous distribution would be the profit margin from your online store. On any given day, the store may report that it’s profit margin is $320. The actual profit margin may not be exactly $320. It could be $320.60, $320.06, or even $320.31415. All these values are different from the other and as such as referred to as a continuous range of values.
Average Opportunity to See (AOTS)
Average OTS is the number of times on average that a member of your target audience will see your ad. Opportunity to See or OTS is a measure in advertising media which denotes number of times the viewer is most likely to see the advertisement. It is basically frequency of media exposure. It is used in media planning or advertising media selection to answer the question- how many times.
Calculation:
OTS is calculated by dividing your TVRs by your reach: At 300 TVRs we have about 70% reach. That means the average OTS is 300 / 70 = 4.2.
Reach = TVRs / OTS. So if you are planning 50 TVRs at 1.5 OTS it would seem that your reach would be 30%. However, reach is not easily predictable. This is for two reasons:
First with you will see that whilst the growth in TVRs is linear, the growth in reach is non-linear i.e. it decreases as you add on every 100 TVRs. Between 0 and 100 TVRs we generate 50% reach. But when we add on the next 100 TVRs we only generate 65% reach at 200 TVRs.
Different types of campaign on different stations, phased in different ways, with different use of daily schedules (dayparting) will increase reach in different ways. For example, a campaign that runs in weekday daytime between 9am and 5pm may struggle to get over 50% reach even at more than 300 TVRs. This is because you will not be reaching the audience that is working during the day.
Effective frequency/Reach
Frequency is the average number of times the advertisement will be presented to the Reached Population. One way to calculate frequency is to divide the number of Impressions by the Reach. Another way is to divide GRPs by Reach Percentage.
Reach can also be expressed as a percentage, which indicates the percentage of the Population that is exposed to at least one Spot.
Gross Rating Points (GRPs)
Gross Rating Point (GRP) is a measure of the size of an advertising campaign by a specific medium or schedule. GRP is calculated by multiplying the number of Spots by Rating.
In advertising, a gross rating point (GRP) measures impact. GRPs help answer how often “must someone see it before they can readily recall it” and “how many times” does it take before the desired outcome occurs.
Construction
“One GRP is one percent of all potential adult television viewers (or in radio, listeners) in a market.” If they are exposed to the ad three times, then that is 3 GRPs.
GRPs are simply total impressions related to the size of the target population: They are most directly calculated by summing the ratings of individual ads in a campaign.
Mathematically:
GRPs (%) = 100 * Impressions (#) ÷ Defined population (#)
GRPs (%) = 100 * Reach (%) × Average frequency (#)
