AUTOMOTIVE
Drive Full-Funnel Performance with Nativo’s SPARC Auto Package
SPARC
Discover new audiences to drive incremental reach and sales
Give your content the results it deserves with SPARC—our cookieless solution powered by Nativo Predictive Audiences.
SPARC
Discover new audiences to drive incremental reach and sales
Give your content the results it deserves with SPARC—our cookieless solution powered by Nativo Predictive Audiences.
SPARC
Discover new audiences to drive incremental reach and sales
Give your content the results it deserves with SPARC—our cookieless solution powered by Nativo Predictive Audiences.
SPARC
Discover new audiences to drive incremental reach and sales
Give your content the results it deserves with SPARC—our cookieless solution powered by Nativo Predictive Audiences.
3x
better performance than cookies
Analyzing over 5BN data points daily, SPARC builds an audience model that understands how users interact with content, what they’re most receptive to, and what topics, formats, & environments will convert.
How SPARC Works
Turn your engagements into outcomes.
1
Unique privacy-compliant engagement signals gathered
2
Data run through proprietary AI to find similar audiences
3
Expanded pool of new, incremental audiences delivered
Visit St. Pete/Clearwater was very impressed with the efficient and effective results from SPARC to reach our visitor profile within our targeted markets. Results like this deliver meaningful engagement and a positive ROI from bookings measured by our media tracking products.
Advertisers
Find fresh audiences and maximized performance
By expanding audience pools & identifying intenders earlier in their buying journey, SPARC drives authentic brand connections that convert.
Publishers + COMMERCE
Drive growth across key revenue streams
Leverage SPARC to boost your bottom line with better performing direct-sold campaigns and attract larger ad budgets to scale your audience extension strategy.
SPARC-powered curated deals by vertical
Frequently asked questions
How does SPARC work?
Our solution uses machine learning to find audiences similar to a seed audience known to be valuable to an advertiser. That audience can be clickers/converters from a given campaign, users visiting specific types of content, etc. The model looks at several attributes in the source audience such as demographics, browser and device type, and site context. We also tie in a wealth of vertical-specific data across our network.
The output of this model is a set of parameters our Ad Server can use to identify users similar to the seed audience. These parameters are packaged together into a “cookieless audience” targetable within the Nativo Ad Platform.
What is a “seed” audience?
A seed audience is simply a set of users that we know saw a brand’s ad and completed the brand’s KPI, e.g. a user who clicked on an ad from a campaign with a CTR KPI, a user that Engaged with a landing page for a Native Article campaign with an Engagement-based KPI, a user that filled out a quiz/lead form for a campaign optimizing towards leads/conversions.This audience acts as the source for which we build predictive audiences based on similar attributes.
What signals are collected to create SPARC audiences?
Context-based: Placement category (IAB), Placement Location (Main well, MOAP, etc.)
Device-based: Device Type, Browser Family, Browser Version, Browser Language
Location/Demographics based*: Zip (plus census data aggregated at the zip level), ISP
Time-based: Day-of-week, time-of-day
*Removed for financial lending campaigns
How is census data used?
We use all available signals in census data. Many variables are the amount of population in a zip code within a specific age range broken down by gender. Those values are aggregate/total values, and not averages. We use medians. Example: for gross housing rent within a zip code, that value from census data is the median housing rent in that zip code, not the average. To remain compliant, this can be removed for financial lending campaings.
Is SPARC fully privacy-compliant & free of personal identifiers if it uses census data?
The census data we use is not specific to an individual user — it is representative data at the zip-code level. We do have information on race, but we would never know if a specific user is actually a specific race. Instead we would know the percentage of people in a zip code of a given race.
