Data mining and AdTech. Two of this year’s biggest buzzwords are coming together, and it’s essential for publishers and advertisers to know the ramifications of combining the two. Let’s start by taking a look at AdTech. Then, we will explore data mining and how they work together.
What are AdTech practices?
There are three specific AdTech practices in which data mining is important. These are header bidding, retargeting and mobile programmatic. If you’re unsure what each of those are here’s a brief explanation:
What is header bidding?
According to Digiday, “Header bidding, also known as advance bidding or pre-bidding, is an advanced programmatic technique wherein publishers offer inventory to multiple ad exchanges simultaneously before making calls to their ad servers (mostly DoubleClick for Publishers).” If that definition sounds too complicated, it’s basically just a way for publishers to directly auction off ad space.
What is retargeting?
Most of us have experienced retargeting even if we aren’t familiar with it. Retargeting is the practice of tracking your website's visitors and then showing them advertisements at a later date. You may have experienced this when you visited a certain product page and saw ads for the same product the next day.
Retargeting ads are shown to have a great ROI, and are one of today’s leading trends in terms of advertising.
What is mobile programmatic?
Many consider mobile programmatic to be a game changer. The short answer is that mobile programmatic is targeted advertisements for mobile devices. But that’s somewhat oversimplified. The more in-depth answer follows:
It is an automated serving of highly targeted advertisements to mobile users. Unlike traditional blanket advertisements of the past, mobile programmatic can serve ads in real-time based on specific criteria such as location, user behavior, and more.
4 steps to data mining success?
Data mining is the process of gaining insights from patterns in large data sets. There are typically 4 steps involved in data mining.
● Collecting data from multiple sources including mobile devices, IoT sensor data, log files, web analytics, and more.
● Processing the data and formatting it so that computers can interact with it. This step is known as data acquisition and uses tools such as Avro, Spark, Kafka, S3, and more.
● Storing the data in a SQL analytics database such as SQream.
● Analyzing the data with BI/Reporting apps, machine learning, interactive visualizations, statistical analysis, and more.
How data mining impacts AdTech
Data mining impacts the major AdTech practices in a few different ways, so we’ll start by discussing its impact on retargeting.
Retargeting already has powerful potential for great ROI. But with data mining, you can drastically improve your ROI. By analyzing the abundant data surrounding your retargeting campaigns you can easily improve your retargeting criteria, hone in on the perfect ad, and optimize your delivery to achieve the highest number of conversions.
When it comes to mobile programmatic, your targeting is everything. Data mining can find patterns which lead to conversions that would not have been seen otherwise. This means that you can refine the list of behaviors that trigger your ads, or refine the types of ads you show. All of this hopefully leads to higher ROI and more highly targeted advertisements.
Then we have header bidding. Data mining is crucial to the successful implementation of header bidding as it enables you to check the bids from multiple exchanges in real time. Without data mining, there would be no way to implement the real-time check for current bids and you’d be back to the waterfall method.
If you’re an advertiser or publisher looking to remain competitive in 2017, you need to learn and employ data-mining. The top AdTech practices of today rely on data-mining to be effective, and AdTech is only going to become more reliant on data as time goes on.
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