The DMC Explore [Audience Lens] composite index logic has been recently updated by the Product Team. The way that users read and interpret the charts will now evolve. We wanted to share a quick rundown of what these changes are and how users can keep benefiting from the charts by reading them correctly.

Why this update?

1. To make the logic behind the calculation of the composite index more comprehensive.

2. To make activations from DMC Explore [Audience lens] more efficient and even closer to the brief than before.

What’s Different?

Composite index is now based on the AES score, earlier it was on the basis of the size and affinity scores of any given audience cohort.

Let’s deep dive into this:

The Old Logic

Audience Size gave you a view of the size of any cohort basis your input brief.

Affinity gaves you a measure of the likelihood of your audience to convert for your brief.

The position of the bubble was on the basis of the Composite Index, which was previously a metric to be understood as a fusion of the size and affinity scores of any given audience cohort; it gave you a comprehensive view of the relevance of any given audience cohort to your brief. It struck a balance between size and affinity. 

What’s New?

The Audience Size still gives you a view of the size of any cohort basis your input brief. But the logic behind the charts have now evolved:

Affinity gives you a view of the interest any cohort shows to your brief vs. the general population.

Composite index is now based on the AES score, which indicates how efficient any given cohort would be in media activation (likelihood of any audience cohort activating for your Facebook Campaign). 

The AES Score is calculated on the basis of the location, demographics and core interest that a user provides, on the basis of these every audience cohort is analysed to churn out their composite index. 

For example, you put in the following brief:

Age: 18-34
Gender: Male
Core Interest: Xbox

Location: Mumbai, Delhi, Bangalore

Now every audience cohort will be analysed against this brief. Let’s take an audience cohort like “Technophile” as an example here.

First, the entire universe of the audience cohort will be defined, i.e people who are categorized as Technophiles in your given location. We will then zoom in on your audience (through an intersection of the demographic (M 18 to 34) and core interest (here Xbox) you inputted) within that universe.

On the basis of these various conditions, the composite index of the audience cohort “Technophile” will be calculated.

With this new logic, just because the size and affinity of a cohort are high it won’t mean it is automatically more likely to be more effective when activated. Though size and affinity still have an impact on composite index, there are now other factors that go into determining how effective the audience cohort  would be once activated.

This means you may come across scenarios where the composite index for a cohort lying more on the left is lower than that of a cohort that is more on the right of a chart:

One of our aims during the evolution of DMC Explore has been to ensure that our users are able to move through the interface efficiently and quickly. Even with this updated logic; users can rest assured that the position of the bubbles are still indicative of the size and affinity of any audience cohort.

To make activations quicker, we have already introduced “Get Recommendations” across DMC Explore [Audience Lens]:

Our “Get Recommendations” feature is built on our proprietary AES (Activation Efficiency score) and helps you expedite the process of identifying the most efficient cohorts in the planning stage. For Example: A cosmetics brand looking at reaching urban women would be looking at a vast set of audience cohorts with overlapping interests. Through our AES algorithm, we can pinpoint to the audience cohorts that would be the most conducive to your brief.

For any further queries, please reach out to us at dmcsupport@dentsu.com.