Programmatic digital advertising enables you to reach nearly 3.5 billion people with highly customized and personalized messages based on real-time data. Programmatic digital advertising combats the rampant fraud in other types of online ads by utilizing advanced technology to help keep your campaigns on track in delivering results.


  • Transparency – Through programmatic advertising, advertisers can see exactly what sites their advertisements are reaching, the type of customer looking at their ad, and any costs associated with the advertisement. Advertisers can view all of this information in real time. ThereŠ—’s no waiting until the campaign is over, lack of information, or unknowns. With one click of the mouse marketers and advertisers are able to view anything related to their live campaign.

  • Real-time Measurement – Advertisers can measure exactly how their creative, campaign, and overall targeting is running as soon as it is launched.

  • Efficiency – Targeting allows marketers and advertisers to directly reach those customers most likely to complete the goal conversion, Š—– aka the ideal customer. Through programmatic advertising, marketers and advertisers can target these ideal customers through IP targeting (zoning in on a specific IP address – usually good for targeting a business), geolocation targeting (east/west coast, state, city, zip code), or category and site targeting. Additionally, programmatic advertising allows marketers and advertisers to retarget customers after they have visited their site. Initially, only 2% of consumers convert on the first visit to a website. With retargeting, marketers and advertisers are now able to continue to reach out to those ideal customers and get the other 98% to participate in a conversion.

  • Reach – At any given time, there is an average of 3.5 billion people on the internet. ThatŠ—’s a potential reach (depending on where the advertiser is targeting) of 3.5 billion people.

  • Optimized and Personalized – Advertisers can deliver more customized and personalized messages to audiences based on real-time data.

  • Recency Retargeting – This allows brands to bid and reach users more or less aggressively based on when the user last completed an action. Brands should be targeting users based on their actions; if a user hasn’t been to the brand’s site in 30 days, that person should be bid on differently than a user who has visited a client’s site within the past day.

  • Look-a-Like Audience – The way a look-a-like is created varies, but DSPs and data partners look at converters’ attributes and use an algorithm or create audience segments to target users who have a high propensity to convert based off historical data.

  • Behavioral – The amount of data available to target and plan against in a DSP is much more exhaustive than that in search engines with Display capabilities because DSPs tap into dozens of 3rd party data aggregators. Types of data available through 3rd parties include:

    • TV commercial exposure involving the types of TV shows and channels users are consuming

    • Offline purchase behaviors via credit card data

    • Online activity such as content being read, links shared, purchases made and searches conducted

    • Social Media activity and shares

    • Consumer personas based on the types of locations users are visiting

  • Search Retargeting – Provides brands with thousands of publisher search data points and large engine data.

  • Location Data – Allows brands to leverage more valuable information by:

    • Targeting users based on their IP address, which is great for B2B targeting by honing in on IPs of relevant businesses

    • Geo-fencing users and targeting based on vicinity to specific locations

    • Reaching users who have been to specific locations and messaging them after they leave

  • Minimizes Incidences of Ad Fraud – Ad fraud is a serious issue – one that is costing publishers in lost revenue. Programmatic fights and solves the issue of fraud by creating machines which help in keeping campaigns on track. If in any case there was a loss of money initially, they can be put back to help in driving leads and conversions. This in future will assist advertisers in achieving their key performance indicator targets. This is done by analyzing traffic patterns and determining where clicks are originating from and if they really convert or not. It is possible to use machine learning technologies to filter and separate low quality leads and concentrate more on channels which deliver better results.