Should brand marketers mourn the loss of audience segments built using third-party cookies?
On the one hand, these third-party segments were plentiful, and allowed brand marketers to reach massive scale.
But on the other hand, segments based on third-party cookie tracking just weren’t very good. Visit a car site and, suddenly, you’re an auto-intender. You were just visiting to check out the latest luxury cars you will never be able to afford but now you are part of an “intender” segment.
Brand marketers also have very little insight into how audience segments are built, how many data points are used, the recency of those data points or the quality (see auto-intenders above).
Audience segments built on third-party tracking cookies do not work across fragmented platforms and channels. Marketers often need to purchase multiple lists from multiple providers in order to launch omnichannel brand campaigns. This fragmentation leads to wasted ad spend, over targeting some consumers while under targeting others.
Segmentation based on third-party cookies does not distinguish between intent and need. Cookies can not differentiate between internet traffic coming from an office building, a cell tower or a private residence. And yet, this is critical information for any marketer keen to drive efficiency in their ad spend.
For instance, by segmenting traffic marketers can tailor their ads to the specific demographics that are more likely to convert. Ads for household products can be directed specifically at home traffic, while B2B services can be targeted towards business traffic.
For this reason, many believe privacy regulators did the advertising industry a service with GDPR, CCPA and other regulations. It forced ad-tech out of its complacency around this largely ineffective targeting strategy.
So what options do brand advertisers have?
Post-Cookie Strategies Available Now
Marketers are experimenting with multiple strategies, including contextual, Privacy Sandbox, and targeting by household and neighborhood characteristics.
Let’s look at some of them:
Contextual
| Pros | Cons |
|---|---|
| Inherently tracking free | Targets content, not people, which may not be ideal for all brand marketers |
| Inexpensive in certain scenarios | Achieving scale requires loosening of targeting criteria, leading to risk of unsuitable brand placements |
| Market tested (TV and radio have used contextual targeting since their inception) |
Privacy Sandbox
| Pros | Cons |
|---|---|
| Reduces cross-site targeting | It may not work: The IAB says that in reality, Privacy Sandbox doesn’t support basic programmatic use cases |
| Improves user privacy by building cohorts from private signals. | It may not be legal: The UK regulator, CMA, is investigating Privacy Sandbox, which means its future is uncertain. |
| Provides a framework and APIs for targeting |
Deterministic signals through clean rooms
| Pros | Cons |
|---|---|
| One-to-one data effective | Adds cost to advertising value chain. |
| All users are anonymized so no PII data is shared. | Match rates and reach low. |
| Deterministic signals still present. | Deterministic signals “washed out/diluted for the benefit of consumer privacy. |
Household characteristics
| Pros | Cons |
|---|---|
| Segment people not content, Full-reach | Less precise than deterministic data |
| Truly omnichannel | Less effective lower funnel |
| No PII. | Not useful on cellular connections e.g. 3G, 4G, 5G. |
The Solution: Targeting by Household & Neighborhood Characteristics
Digiseg’s data is built on the principles of privacy-by-design. Rather than placing cookies and tracking people without their knowledge or permission, Digiseg built 200 audience segments based on household and neighborhood characteristics. These include the presence of children, number of cars, household income, interests and many other attributes.
The benefits of Digiseg’s Audience data:
- Consumer needs are validated, making intent and behavior easier to predict. Why purchase inventory to show ads for solar panels to consumers who rent apartments in high rise buildings?
- Omnichannel support. Targeting by household and neighborhood characters means the same data set can be used across all channels for both targeting and analysis.
- Reaches 100% of a market.
- Exceeds industry average CTR. Digiseg data consistently outperforms industry campaign metrics, thanks in large part to its needs validation.
- Build your upper funnel. Digiseg offers the best datasets for building your upper funnel as it targets consumers who have a need for your products, but may not be ready to purchase at the moment.
How it Works
As a marketer, you want your ad to be seen by people who can make it through your sales funnel, and not waste your budget on users who will not convert.
Digiseg is able to segregate traffic by home, business and in-transit usage, which makes it easy for us to suppress business and in-transit traffic:
| Private (at home) | Business | In-Transit | |
| User Type | People at home | People at work | People on the go |
| Traffic Types | Typically Wifi | Mobile of desktop accessed via campus-based network | Typically cellular-based. |
Next you can mix and match any of our segments. The Digiseg Audience Builder lets you select the attributes that are important to your campaign, and see instantly how many people you can reach across all channels quickly and easily.
It also gives you insights into the precision of the data you are working with through the audience selection index.campaign, and see instantly how many people you can reach across all channels quickly and easily.
Digiseg Audience Builder
Ready to get started?
Go build your ideal audience and with the press of a button, it will be accessible in your preferred DSP. You can use the audience to target users across display, CTV, video, in-app and audio.
