Dstillery helped leading footwear brand drive $2.44 in revenue for every $1 invested. Read this case study to find out how.
We've created a wide variety of audiences to help you reach resolution-makers in the new year.
A casual dining restaurant chain drove in-restaurant traffic from online orders.
Dstillery's guidelines for responsible targeted healthcare advertising.
Gear up this basketball season with Dstillery's Crafted Audiences for NBA teams. Here is an example of the types of insights you can find for Golden State Warriors Fans.
Longwood Gardens, a Pennsylvania public garden, tapped Dstillery to raise brand awareness and excitement for the fall season in key target markets.
Trade Desk power user drives up to 60% performance lift across four major brands.
The marketing landscape is saturated with data providers. How do know which partner is right for you?
Shopper insights from the National Retail Foundation.
Football season is here! Discover insights on audiences for each of the 32 teams.
Discover top content engagement across your audiences.
Discover your best audience activation strategies.
Create and manage all of your audiences in one place.
Dstillery’s Audience solutions help you discover and activate new potential customers that align with your Brand DNA, to drive awareness, engagement and ultimately revenue growth.
Discover multifaceted audiences based on your consumers' in-market behaviors
Demystifying Artificial Intelligence, Machine Learning & Data Science
Read the 2017 KDD Award winning paper on improving delivery by bidding for what you can win
A high-end fitness equipment brand has been working with Dstillery to drive new product sales.
This paper examines ways to estimate the causal effect of display advertising on browser post-view conversion.
Through an analysis of mobile device location data and online browsing behavior associated with devices from MLB stadiums during the 2015 season, we are able to shed light into an area of advertising that has previously been difficult to quantify.
In this paper, we will show examples of how non-intentional traffic adversely affects both general analytics and predictive models, and propose an approach using co-visitation networks to identify sites that have large amounts of non-intentional traffic.