A) Index Numbers: Some times you have numbers of different ranges, however you may want to compare on a single scale. Indexing numbers will help you achieve that. For example average sales of apples may be 1/2 of average sales of oranges for a given month/geography. If you compare their trend of sales, you find that they are at same level from their repectives bases. How do you capture that information?
You fix a particular performance level as base, you can assign a metric as 0 or 100 or of your liking. All other performances are Indexed on base.
This will help you establish high/low movement of metric from base.
Therefore sales of apples and oranges, even though are different may be at same Index point. On the other hand grapes sales may be same as apples but its index may be 1.5 times that of apple.
Another use of index is offering points to sales guys for their performances. Every sales guy on field may have different targets to achieve. However they may get points depending on the ratio of their performance to their base level performance.
A particular state of performance is considered as BASE then the future performances are treated as ratio from base expressed in range of 0 – 100 or 1 – 5 or 1 – 10.
You can square the number before taking ratio. Alternatively take root, mod, absolute, log etc depending on how you want to represent numbers.
B) Conversion Rates / Probability: Whenever there are two levels of metrics one leading to another. Conversion rates are normally used. Most commonly used conversion rate is (Customers acquired / Prospects contacted).
This can be used as performance of channel used for identifying propects. In internet businesses Conversion rates are used for evaluating performance of landing pages, acquisition channels (SEO, SEM, Affiliate), product efficiency, managing advertising and many more.
Conversion Rates can also be used as probabilities of occurence of the event. For all practical scenarios you can use this as a metric to analyze effectiveness of campaigns.
Frequency distribution: In other words it is proportion of segment compared to whole. This can also be used as probaility.
In futre topics, lets talk about how we can use this to calculate lift, ROC etc.
For now it this will help point out Outliers in database, segments that take up large portion of the pie etc. This metric will also help you in segmenting database.
C) Ratios: When you divide one number with the corresponding number of a different metric its a ratio. Conversion rate is also a ratio.
Odds Ratio: This is defined by probability of occurence of an event over probability of non-occurence of that event. In some cases it is (Conversion rate / (1 – Conversion rate ) )
This is mostly used in logistic regression to estimate the probability of occurence from a set of predictor variables.
You could run a predictive model to predict the log of odds ratio as the outcome (dependent variable) and input independent variables to be customer predictors.
Predictive analytics is good at identifying, whether a event will occur or not, rather than when the event will take place.
Further in the series of understanding numbers, we will talk about concept of lift and other metrics.
Source: kpraveenkumars
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30th Sep 2011
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