This document describes the operations used to build calculated metrics in Beckon. The underlying logic is based on the premise of Reverse Polish Notation
Operation |
Description |
Use Case |
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Metric Sum |
Beckon adds the data of two or more metric values. |
ABC Company wants to know the total spend for Social and Online Display for week 1 and 2. From Table 1, spend for Social and Online Display is $29.9 for week 1 ($27.6 + $2.36) and $282 for week 2 ($28.5 + $253). Total Spend for Social and Online Display were added together by using the METRIC SUM operation. Notice in this case, the sum values of the same metric but different dimensions were added together. You may also use this to sum two or more unique metrics. |
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Metric Multiply |
Beckon multiplies the value of two or more metrics |
ABC Company wants to know the overall spend for their impressions for their recent Online Display campaign. Total impressions for the recent campaign are 1,360 for week 1 and 1,940 for week 2. Cost per impression is $$0.039 for week 1 and $0.077 for week 2. Total overall spend is $53.04 for week 1 and $150 for week 2. Total Online Display impressions is MULTIPLIED by spend per impressions to determine overall spend for Online Display impressions for their recent campaign. |
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Metric Max |
Beckon compares the value of two or more metric values and selects the highest value |
ABC Company wants to know which channel had the highest spend. Based on Table 1, Social had the highest maximum value of $27.6 for week 1 and for week 2 Online Display had the highest maximum value of $253. The highest spend per week was determined applying the METRIC MAX operation to the Online Display Spend, the Email Spend and the Social Spend. If data is missing for a metric, it is not considered when determining the maximum. Notice in this case, the values of the same metric but different dimensions were considered. You may also use this to find the maximum value between two or more unique metrics. |
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Metric Min |
Beckon compares the value of two or more metric values and selects the lowest value |
ABC Company wants to know which channel has the lowest spend. Based on Table 1, Online Display had the lowest minimum value of $2.36 for week 1 and for week 2 Email had the highest maximum value of $6.09. The lowest spend per week was determined applying the METRIC MIN operation to the Online Display Spend, the Email Spend and the Social Spend. If data is missing for a metric, it is not considered when determining the minimum. Notice in this case, the values of the same metric but different dimensions were considered. You may also use this to find the minimum value between two or more unique metrics. |
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Metric Average |
Beckon calculates the average for two or more metric values |
ABC Company wants to know the average spend by week. Based on Table 1, the average number of clicks for week 1 is $12.3 (66.85+253+28.5)/3) and for week 2 it is $95.9 ((86.09+253+38.5)/3). The average spend was determined by applying the METRIC AVERAGE operation to the Email Spend, Online Display Spend, and Social Spend. |
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Division |
Beckon divides one metric value by another metric value |
ABC Company wants to know how much they are spending per impression on a weekly basis. Week 1: Spend = $254 Impressions = 6,600 Week 2: Spend = $500 Impressions = 6,470 The cost per impression for week 1 is $0.039 ($254/6,600) and for week 2 it is $0.077 ($500/6,470). The spend per impression is calculated by applying the operation, DIVISION: taking the total spend by week as the numerator and dividing it by weekly impressions as the denominator. |
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Negation |
Beckon multiplies any metric by -1 |
ABC Company wants to know for week 2, how many more Online Display impressions they had compared to Social impressions. Based on Table 1, the difference in week 2 of Online Display impressions compared to Social impressions is 470 (1,940-1,470). The difference was calculated by applying the NEGATION operation to Social impressions, which multiplies the impressions value by -1 making it a negative number. Online Display and Social impressions are then summed for week 2, which equates to a difference between the two impressions. Essentially, by applying the NEGATION operation it enables you to subtract one metric value from another unique metric value. |
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Inversion |
Beckon divides 1 by any single metric |
ABC Company wants to know what is the spend per impression. Based on Table 1, the inverse of total impressions for social is 1/2,890 or .035% (1/(1,420 + 1,470)). The inverse rate was calculated by applying the INVERSION operation to social impressions. Based on Table 1, total spend for social is $56.1 ($27.6 + $28.5). Multiply $56.1 with .035%, which is $.019. Spend per Impressions was calculated by applying the METRIC MULTIPLY, to multiply $56.1 to the formula metric of .035%. (Note: A simpler way to calculate spend per impression is by using the DIVISION operation dividing $56.1/2,890.) |
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Scalar Addition |
Beckon adds a constant number to any single metric |
ABC Company wants to know the total weekly spend with the additional fixed cost of $10 Based on Table 1, total weekly spend with the fixed cost of $100 for each channel for week 1 is: Email = $6.95 Online Display = $2.46 Social = $27.7 The weekly total spend for each channel was increased by $100 fixed cost by applying the SCALAR ADDITION operation to Total Spend. The constant can also be a negative number where it is subtracted from a metric value. |
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Scalar Max |
Beckon compares the value of a single metric to a selected constant number and determines the higher value |
ABC Company wants to know if there are any engagement rates that are greater than 50%. Based on Table 1, for week 1 the Email engagement rate of 128% and for week 2, the Email engagement rate of 125% are greater than the scalar max of 50%. The engagement rates that are greater than 50% were determined based on applying the SCALAR MAX operation and defining the constant value as 50%. |
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Scalar Min |
Beckon compares the value of a single metric to a selected constant number and determines the lower value. |
ABC Company wants to know if there are any engagement rates that are less than 20%. Based on Table 1, for week 1 the Online Display engagement rate of.27% and for week 2, the Social engagement rate of 19% are less than the scalar min of 20%. The engagement rates that are less than 20% were determined based on applying the SCALAR MIN operation and defining the constant value as 20%. |
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Scalar Multiply |
Beckon multiplies the value of a single metric by a selected constant number |
ABC Company wants to know the CPM (cost per thousand impressions) for Email. Based on Table 1, the CPM for Email is 0.0136 (($6.85+$6.09) / (926+24.5)) The CPM for Email is calculated by applying the SCALAR MULTIPLY operation with a constant number of .001 to a cost-per-impression metric (formula). |
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Filter |
The filter sifts a single metric by a dimension(s). |
ABC Company wants to know what percentage of total spend was attributed to the Social for week 1. Based on Table 1, the percentage of social spend to total spend for week 1 is 10% ($27.6/$254). Percentage of social spend was calculated by selecting Percentage as the Format Type, and applying the FILTER operation. Caveat in chart builder is if you selected the formula above (Percentage of Social Spend) as the metric and selected a dimension of Online Display, chart builder will display Social Spend + Online Display Spend/ Online Display Spend. |
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Dimension Product |
Beckon calculates all the vectors for any user-selected dimension(s) and disregards any dimension that is not a number, then multiplies the values for all the valid vectors. |
ABC Company wants to know what the percentage penetration growth of each country to global growth of 1,000 HH.
In the example above, DIMENSON PRODUCT by “Country” is applied to multiply growth by 1/1000 (another formula metric). |
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Dimension Sum |
Beckon calculates all the vectors for a single metric by a selected dimension(s). All dimension+metric combinations that do not produce a number are not considered and only sums the available values. |
ABC Company wants to know what the total impressions are per DMA.
Using the DIMENSION SUM operation, total impressions by DMA are displayed in the Total by DMA column. |
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Dimension Count |
Beckon counts the number of vectors for a single metric for a selected dimension(s). All dimension+metric combinations that do not produce a number are not considered and only counts available values. |
ABC Company wants to know how many countries have activity for social clicks for week 1 and 2.
By applying the DIMENSION COUNT, week 1 and 2 both have 2 countries that had click activity. Total dimension count by country for social clicks is 2 for both week 1 and 2. Since Canada and England had 0 clicks, they are not considered in the dimension count formula. |
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Dimension Average |
Beckon takes the Dimension Sum divided by Dimension Count. Beckon averages all the vectors for a single metric for a selected dimension(s). All dimension+metric combinations that do not produce a number are not considered and only takes the average of available values. |
ABC Company wants to know what the average is for the number of engagements for week 1 and 2.
By applying the DIMENSION AVERAGE, week 1 average is 961.75 and week 2 is 3,282.75. |
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Dimension Median |
Beckon locates the midpoint of a vector for a single metric for a selected dimension(s). All dimension+metric combinations that do not produce a number are not considered. |
ABC Company wants to know what the median value is of engagements by channel.
By applying the DIMENSION MEDIAN by channel (dimension) for engagements (metric), the median value is $240. |
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Dimension Max |
Beckon compares the value of two or more metric values for a selected dimension(s) and selects the highest value. All dimension+metric combinations that do not produce a number are not considered. |
ABC Company wants to know which channel had the highest dollar value of engagements. Based on Table A above, Online Video with an engagement value of $577 is determined as the dimension max. By applying the DIMENSION MAX, the highest value based on channel is Online Video with a January 2015 engagement of $577. |
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Dimension Min |
Beckon compares the value of two or more metric values for a selected dimension(s) and selects the lowest value. All dimension+metric combinations that do not produce a number are not considered. |
ABC Company wants to know which channel had the lowest dollar value of engagements. Based on Table A above, Email with an engagement value of $27.9 is determined as the dimension min. By applying the DIMENSION MIN, the lowest value based on channel is Email with a January 2015 engagement of $27.9. |
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Moving Average |
Beckon calculates the moving average for a metric based on the time frequency and an integer value indicating the number of data points to be used to calculate the average (denominator). |
ABC Company wants to know what the 2 month moving average is for the total monthly sales data below;
The two month moving average is as follows:
The moving average was calculated by applying the MOVING AVERAGE operation to the sales metric and selecting a frequency of monthly and an integer of 2 indicating two months. |
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Dimension Coefficient |
Beckon calculates the coefficient for each dimension(s) where the coefficient represents the contribution of the selected dimension towards the overall metric value. |
ABC Company has several banner ads displayed on various search sites. They want to know how much (or if) the ad-size and ad-content impacts the engagement rate. They are assuming that a bigger ad size and animated content will result in more clicks, thus increase in engagements. To calculate the impact of ad-size and/or ad-content, multiple ads have to be run with varying ad-sizes and content and its results compared to the base or “control” ad.
The DIMENSION COEFFICIENT was applied to the engagement rate and impressions to calculate the impact of the impact or contribution of the selected dimension(s) to the engagement. The larger the resulting number, the greater the contribution to the overall engagement metric. Ad #2 resulted in a negative number which means that there is a 0 impact or it did not contribute any increases to engagement. This formula can be used to uncover how effective a specific change is dimension values are to the engagement level. (Note: To see actual use of this operation, go to Yahoo and view the formula, “Media Incremental Conversions w/ Coefficient (primary 1 & 2)”. Yahoo uses displays ads about ‘Fantasy Football’ to engage its users with football. The two dimensions associated with these ads are Ad-Size and Ad-Content, its expected that changing these dimensions will cause change is corresponding ad metrics: a bigger ad-size might cause more users to click on the ad and get engaged. ) |
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