Look-alike analytics

Look-alikes use predictive modelling to find prospects that 'look like' your existing best customers. Once created, you can further analyse these audiences using look-alike analytics. Further analysis is applied using model score variable dimension on a cube or model score dimension on a datagrid. These extra analytic dimensions provide deeper insights into your target audience, allowing data-driven decisions on audience size and composition.

To use look-alike analytics, you must first follow the same steps that you would to create a look-alikes audience:

  • Create an audience

  • Find a look-alikes solution

Then, you can Analyse your look-alikes.

Create an audience

To create an audience:

  1. Go to the Audience tab in Orbit.

  2. Click + New Audience.

  1. Add an Audience Name.

  2. Click Create.

  3. Click + Add Filter.

  4. Search for and select the relevant Variable (in this case Destinations).

  5. Select the relevant Values (In this case Sweden).

  6. Click Apply.

  7. Click Save.

Find a look-alikes solution

To create a look-alikes solution:

  1. Click + Add New to create a new audience tab.

  2. On the Find Look-alikes tile, click Add.

  3. Add a name to your look-alikes solution and click Next.

  4. Select the relevant variables and click Run to generate a profile.

    Note: At this point, it may take some time for analysing characteristics to run.

  5. Once the variables and insights have been generated, click Save.

You can now start analysing your look-alikes.

Analyse your look-alikes

Once you have gone through the steps above, you can apply complex analytics to your look-alikes audience.

You can analyse your look-alikes by using the model score variable. After using the model score variable, you can analyse further by using the model score in a cube or data-grid.

To analyse your look-alikes:

  1. Select the relevant variables and click Find Look-alikes.

  2. Within the Analyse Look-alikes tile, click Analyse.

  3. Add an Audience Name, confirm the Look-alike Score Name, and click Create.

    Note: At this point, creating a look-alikes audience may take some time. You’ll receive a notification when this is finished. Click on this notification prompt to view your analysis.

  4. Once complete, you’re presented with a cube showing the banded score as a dimension. Adding another dimension provides further insight.

    Note: You can’t make a favourite from this look-alike analysis, as the dimension applied is particular to this audience.

At this point, you can also view each records’s score on a datagrid.

To apply your score to a datagrid:

  1. Click + Add New.

  2. On the Browse Data tile, click Add.

  3. Name your datagrid and click Add.

  4. Click Edit Columns and add the relevant columns to your datagrid, as required.

    This provides you with a ‘More like this score’, which allows you to compare the relative similarity of your audience and look-alikes. The higher the score the more similar your look-alikes are to your initial target audience.