Index Profile

Overview

Index profiles visualise, for each unique value of a given dimension (e.g., values MALE/FEMALE for dimension GENDER), the relative counts of a TARGET dataset compared to a BASE dataset.

At the most simple level, when working with a table of customers, this might be comparison of a base dataset (all customers) compared to a target dataset (campaign responders).

If MALE/FEMALE is distributed 51%/49% in the BASE dataset, and 40%/60% in the TARGET dataset, then it can be said that MALES are UNDER REPRESENTED in the target (40% compared to 51%) , and FEMALES are OVER REPRESENTED (60% compared to 49%).

Another way of expressing this would be to say that customers in the TARGET dataset (e.g., campaign responders) are more likely to be FEMALE than MALE in the target group compared to the base group.

Calculation

This under or over representation is expressed as an index score, where

• An Index of 1 means the value has the same penetration in both BASE and TARGET datasets
• An Index of less than 1 means the value is UNDER REPRESENTED in the TARGET dataset
• An Index of greater than 1 means the value is OVER REPRESENTED in the TARGET dataset

When looking to see which values are over-represented in a target dataset, all values with an index greater than 1 are over-represented to some degree.

The further the index score is away from 1, the greater the under or over representation - i.e., the more pronounced is the relative difference in penetration between the target and base datasets for that value.

The Index profile calculation uses a ratio of ratios so that target and base datasets of differing size can be compared to each other.

Report Layout

Input : Dimension only

The only required input for an index profile is a dimension.

To add a dimension to the report, drag a discrete field from the Database Explorer and drop it onto the Dimensions box

• Dimension - [e.g., customer.occupation]

Providing just a dimension, and no target filter, will create an index profile where all values will have an index of 1.00 and base and target counts will be equal.

Input : Dimension  and Base Filter

• Dimension - [e.g., customer.occupation]  
• BASE Filter/Dataset - [e.g., customer.frequency > 6]  

To add a base filter to the report, drag a dataset or field value from the Database Explorer and drop it onto the Base DataSet box

Note: If just a dimension and base filter are applied, the index will remain at 1.00 for all values, and target and base counts will be the same:

Input : Dimension and Target Filter 

• Dimension - [e.g., customer.occupation]  
• Target Filter/Dataset - [e.g., recency-decile = 10]  

To add a target filter to the report, drag a dataset or field value from the Database Explorer and drop it onto the Result Grid and choose "And"

By providing a target filter, but no base filter, all records in the table will be used as the base.  

Here we can see the difference in count between base and target filter for occupation = freelance  (1,261,484 vs 195,765, with a index of 1.73): 

From the index profile shown, we can see that freelance, student, junior tester and junior programmer are over-represented in the target group (as they are above the index=1 line).

Retired, product manager, lead tester, lead programmer... etc are markedly under-represented and are some way below the index line.

By looking at the BASE line, we can see that junior testers make up the largest group in the base group, and architects are the smallest.

Junior testers are also the largest group in the target group, as well as being over-represented in the target compared to the base.

Input: Dimension, Base and Target

• Dimension - [e.g., customer.occupation]  
• Base Filter/Dataset - [e.g., customer.gender = female]  
• Target Filter/Dataset - [e.g., customer.age band > 3]

Target and Base Filters

The TARGET filter is combined with the BASE filter - this means that the Target column contains counts of records that are in the base filter AND the target filter.    Adding a target filter that has no overlap with records in the base filter will therefore create an empty result set.
 

When a target and base filter have both been supplied, the base counts will reflect the distribution of dimension values in the base dataset, and the target counts will represent the distribution of dimension values in the target. The Target line show the distribution of counts in the target dataset (using right-hand axis) and the Base line shows the distribution of counts in the base dataset.  The distance between the target and base line represents the difference in counts for a particular value.

Interacting with results

Sort the result grid by Index, base or target to more easily see values of interest:

Select segments from the grid or chart and drag into the HotBar to work with them further:

Drop extracted segments back onto an index profiling dashboard for further insight:

Multiple Dimensions

More than one dimension can be added to create a multi-dimensional index:

By default, the index in a multi-dimensional profile will be that of the combined dimension.  In the example above, the combined dimension is 14 | 2014 | Corporate | Second Class | Australia

Select composite in the options menu to perform a full index across all combinations of the different dimensions:

In a composite index, the calculation is performed for each dimension, and every combination of 2 or more dimensions at the same time.

Where a dimension has been ignore in a calculation a * will appear in the value cell. 

This indicates that the dimension has made no contribution to the total index for that particular value.

Measures

The default mode of index profile is to count records; by dropping numeric field into the measure box the index will be calculated from the relative sum of that measure for each value in the dimension.

To calculate a measure index, drag a numeric field from the Database Explorer and drop it into the Measure Box:

Display Options

Index profile has similar display options and micro-toolbar features as a standard profile