Retargeter Blog

What’s Your Return on Audience Data (ROAD)?

Your audience data is as good as gold (probably better) when it comes to the returns you can earn from it. We call it Return On Audience Data (ROAD). You’re probably familiar with Return On Ad Spend (ROAS), a key performance metric in advertising. ROAS and ROAD are related, so let’s start by clarifying the relationship between Ad Spend and Audience Data.

ROAS = Revenue ÷ Ad Spend (there are variations, but we’ll use this one)

How do you calculate Ad Spend?

A partially true statement:  Ad Spend = Cost of Media

A truer statement:  Ad Spend = Cost of Media + Audience Data

In fact, 2nd and 3rd party audience data is often more expensive than the cost of media for digital display advertising, sometimes much more expensive.

1st party audience data is the data you already own. It wasn’t necessarily free to acquire it, but now you own it. Your audience data includes the visitors to your website that you cookie, your email lists and CRM data, your app users, and more. When you use your own audience data for digital display advertising, we call it retargeting or remarketing. With retargeting you eliminate a major expense in your advertising, data, and you amplify your revenue. Retargeting is one example of how we build Return On Audience Data (ROAD).

Measuring ROAS and ROAD

To better understand the importance of ROAD and how it can drive ROAS, let’s look at examples of Audience Targeting and Retargeting campaigns and compare results.

Audience Targeting. Let’s say we run an ad campaign to reach a new audience using 3rd party audience data. We are buying data from other (3rd party) sources to reach this audience. It’s an audience we want to reach, but it’s not yet our audience data. We call this Audience Targeting:

Audience Targeting campaign assumptions:

Media cpm = $3.00 (inventory cost per 1,000 impressions served)

Data cpm = $3.00 (data cost per 1,000 impressions served; when you buy audience data to target a specific audience, you pay the data provider each time you serve an ad to this audience)

Impressions served = 1,000,000

Click-thru rate = .1%

Conversion rate = 1.5%

Average order value = $200

Using the assumptions above, we calculate ROAS:

Ad spend = ($3 + $3) * 1,000,000 ÷ 1,000 = $6,000

Revenue = .1% * 1,000,000 * 1.5% * $200 = $3,000

Audience Targeting ROAS = $3,000 ÷ $6000 = $.50

The upshot? For every dollar invested, we generated $.50 in revenue on this campaign. We actually have negative ROAS on this Audience Targeting campaign, but that’s okay with us because we acquired 15 new customers and each customer has a lifetime value (LTV) of $1,200, so we’ll generate a return on this campaign over the customer lifetime, especially when we combine this campaign with our Retargeting campaign.

Retargeting. Now let’s make some assumptions about a campaign we run using our 1st party audience data. This is our retargeting campaign. There are different types of retargeting including Site Retargeting, Facebook Retargeting (FBX), CRM Retargeting, and more. We’ve adjusted some of the assumptions, because this is an audience we’ve already engaged and they behave differently than an audience we’ve not engaged. Note that this audience includes users we acquired through our Audience Targeting campaign:

Retargeting campaign assumptions:

Media cpm = $3.00

Data cpm = $0

Impressions served = 1,000,000

Click-thru rate = .13%

Conversion rate = 3%

Average order value = $300

Using the assumptions above, we calculate ROAS:

Ad spend = ($3 + $0) * 1,000,000 ÷ 1,000 = $3,000

Revenue = .13% * 1,000,000 * 3% * $300 = $11,700

Retargeting ROAS = $11,700 ÷ $3000 = $3.90

The upshot? For every dollar invested, we generated nearly $4.00 in revenue on this campaign! By using our 1st party data, we didn’t incur a data cost and our ad spend was cut in half, which by itself effectively doubles our ROAS! Furthermore, the audience we reach using our 1st party data converts at a higher rate and spends more than the audience we reach using 3rd party data. Using our 1st party audience data v. 3rd party audience data, we generated an incremental return of nearly $3.50 for each dollar spent! That’s an example of how we build Return On Audience Data (ROAD).

A Case Study

Now we’ll illustrate how we can combine Audience Targeting and Retargeting to maximize your ROAD. The graph below shows actual results for one of our customers in 2014, a mid-market specialty retailer that sells online and offline through retail stores.

The blue bars represent impressions served for Site Retargeting campaigns.

The red bars represent impressions served for Audience Targeting campaigns.

The green line represents customer purchases by month.

Pretty nice looking graph, right? What does it tell us?

We started working with the customer to retarget their audience, particularly using Site Retargeting and also some CRM Retargeting. We learned a lot about their audience and customer/user behavior, experimenting and continuously optimizing the campaign to deliver steady purchase volume and high ROAS/ROAD. For every dollar of ad spend, the Retargeting campaign generated greater than $10 in revenue.

Heading into Q4, an important season for the customer, our objective was to scale purchases. Using the information we had learned about their audience, we developed a plan for an Audience Targeting campaign using 2nd and 3rd party data. This included Search Retargeting and Contextual Targeting along with an exclusive Audience Extension partnership that we negotiated with a top publisher that reaches the advertiser’s target audience. The use of high quality 2nd and 3rd party data enabled us to scale our campaigns and sharply increase the size of the audience we want to reach along with impressions served to this audience.

The graph above clearly shows a sharp increase in purchases in Q4. Perhaps most importantly, along with increasing purchase volume our ROAS actually increased even as we increased spending in Q4! It seems that the combination of Retargeting and Audience Targeting was very effective.

An interesting point is that a large percentage of the incremental purchases generated in Q4 were attributed to the Site Retargeting campaign. We increased Site Retargeting impressions approximately 50% and purchases doubled. Awesome! Why? Once users clicked through from the Audience Targeting campaign they were added to the Retargeting campaign audience segment and removed (burned) from the Audience Targeting campaign segment. This meant that we could reach a large target audience and concentrate spending on a relatively small percentage of this audience, those whose behavior suggested that they were the most valuable.

If we measured results for either campaign on its own in isolation, we would potentially misinterpret results and overlook the value that was created by running these campaigns simultaneously and synchronizing them. This is a terrific example of how to build Return On Audience Data (ROAD)!

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  • Such a great article! Really like the implementation of running retargeting strategies first for a certain period and then add up 2 and 3rd party data (audience) based on the learning, rather than trying all strategies, contextual or data targeting from assumptions.