Banded Pareto

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The Banded Pareto allows you to focus on identifying your top customers based on a numeric variable. You can use this option of Pareto to understand, for example your top spenders based on a revenue variable, or your top complainers based on a number of complaints variable. Again, the rolling total percentage will allow you to select the percentage of people you wish to focus on by allowing you to add to a Dashboard Filter or even create an Audience.

Steps to use Banded Pareto

From the Solution Tile tab, select the Pareto -Banded icon to reveal the data input options for this tile.

In this example, we are going to identify our top spenders based on a system variable called Total Spend of Bookings.

  • To populate the Value and Number of Bands click on the associated menus:

Orbit will use the Value variable to calculate spend bands where there are equal numbers of people in each band. To have 10 bands means that it will allocate 10% of your individuals into a band according to how much they spent on bookings.

Use the Sort Order to set the initial view as Ascending or Descending, you can also change this on the dashboard Pareto Chart

In our case, we are looking at top spenders so we shall sort descending.

  • Select the Show in underlying data grid option If you wish the users of the dashboard to see the underlying data.

  • Once you have filled out the details in the Visualisation Editor click Apply.

The chart will show the 10 bands specified, and a cumulative percentage of people.

You can use the tooltips to see extra information regarding the spend on bookings, the count of people and the cumulative spend.

By hovering over a point on the line, details will be shown, for example the % of Bands chosen and the % of People selected. It also shows you the cumulative spend and the total percentage of spend that this equates too.

  • By hovering over a bar on the chart, details will be shown for example, the spend band of the bar, the number of people selected in that bar and the total spend of bookings in the bar.

  • If I wanted to select 50% of my Top Spending customers, I could use the tooltip to select the bars required to achieve this. Select the Line or the bar, to select all bars the left of it.

  • By selecting the bars, they will change colour and an Add to Filter button will appear in the right-hand side of the chart. Clicking this will allow the selection to be added as a User Filter to the dashboard.

The filter added will appear in the User Filter as a Top N. Rather than selecting static spend bands, a Pareto User Filter will always select the TopN (50% in this example) of Top Spenders to cater for the recalculation of the spend bands for the People selected. This will be beneficial when creating dynamic Audiences to use in analysis or campaigning where the requirement is to always select the TopN spenders.

Updating the Filter

The Pareto solution allows you to explore the Top Spenders selected. If further Individuals are required. select the additional spend bands and choose Update Filter to add them to the User Filter.

  • To clear a Pareto Filter, select the Clear Pareto Filter option on the chart or delete the filter from Dashboard Filters.

Sorting the Pareto Chart

When creating the Pareto Chart, a descending sort option was chosen to ensure we could see Top Spenders. You can change this in the dashboard to sort the chart ascending if required.

  • Click on the sort icon on the top right of the chart to change the way the data is ordered.

View Underlying Data Grid

If the option to Show Underlying Data Grid was selected when the tile was set up in the Visualisation Editor, then a grid icon will be displayed in the bottom left of the tile.


Using Pareto to Filter the Dashboard

As well as applying the selected bands as User Filters, any selected bands will also affect the display of data in the surrounding tiles. This will allow you to add other tiles to understand the Top Spenders such as what destinations they are frequenting, where do they live, their length of relationship with the brand or even their income to name but a few attributes which could be used to paint a picture of who they are.

The Pareto chart can then be updated to increase or decrease the spend bands or the cumulative percentage of individuals selected so that patterns in the data can be explored.

If User Filters are added from other tiles on the Dashboard, all tiles but the Pareto tile will be affected. The Pareto tile will remain in its original state, showing the Individuals and their Total Spend based on the Dashboard Filter only.

Creating an Audience from a Banded Pareto

Once a Pareto Filter has been applied as a User Filter, this filter can also be added to an Audience for practical application. Click on the +Audience icon in the dashboard.

The filter applied from Individual Pareto will be a TOPN function. This means that the Audience created will always reflect the Top or Bottom % of People selected rather than static spend bands.

Worked Example

Using Transactional Attributes on a Person Level Dashboard

Due to the hierarchical nature of the Apteco software, one person can have many bookings and those bookings can have many attributes etc. This provides for powerful data models, but means you need to be clear of the definition and dashboard results when working across tables.

This example will illustrate this using Booking level information on a Banded Pareto tile on a Person dashboard

  • On a Dashboard built at a Person level, a Banded Pareto has been set up with 10 bands of the Profit field from the Booking table (i.e. transactional level).

The Banded Pareto does not aggregate the Profit to a Person level. It looks at the bookings as separate records and creates 10 bands with equal numbers of Booking records (deciles) based on the Profit of each booking. Therefore, one person who has made a more profitable booking and has also made a less profitable booking will have bookings present in both Profit bands. Note that the Banded Pareto edit panel confirms that the tile will select (and therefore show) Bookings.

  • In this example, number tiles have been added to the Dashboard to explain the calculations. Initially, the Bookings tile shows the total number of bookings made by all the People included in the Dashboard filter. The People tile shows how many people the dashboard filter has selected and the Sum of Profit tile shows the total value of all bookings made by these individuals.

When we apply filters from Banded Pareto, the numbers will change but in a manner that is different from the totals on the tooltip.

  • Note from the tooltip that for the top 60% of the Bookings (the highlighted bars), there is a Cumulative Profit of £170,735,657. When applied as a Pareto Filter on the dashboard, the surrounding tiles will update to reflect the selection made by reference to the People who made the 60% most profitable bookings.

  • The 60% of the most profitable bookings have been selected in Banded Pareto. However, as the dashboard is built at a Person level, adding the filter, means the Dashboard is now filtering to the 767,733 people who have made the 60% most profitable bookings. These individuals have made other bookings which fall outside those most profitable ones. In fact, they have made 1,636,735 bookings in total and brought in a profit of £185,649,314. The difference between this figure and that shown on the tooltip is the £14,913,657 profit from additional (lower profit) bookings made outside the profitable ones selected in the Banded Pareto tile.

In many cases it is appropriate to use Dashboards resolved to People so you can get a rounded view of all activity. However, if you solely wish to analyse particular transactions, we recommend the dashboard is built at a transactional level to avoid additional transactions belonging to the Person being included. Alternatively, where, for example, the total profit for a person is required, the Banded Pareto tile can use Person level variables to ensure that the Person is selected because of the aggregated Profit that individual has yielded over time rather than looking at the profit of each booking.

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