How data science can help you rule the ad-tech world

We know that the digital advertising market is growing fast. According to a report, digital ad spending will reach$427.26 billion by 2022. The success and continued growth of the digital ad market are slowly becoming bound to the massive volumes of data produced each day.

With an estimated 2.5 quintillion bytes being produced globally, there’s a lot that advertisers can do with this data: enter new markets, target specific audiences with relevant, personalized messaging, detect fraud, boost campaign performance, and more.

Through the use of innovative data science software and tools,ad agencies and brands can more efficiently target, deliver, and analyze their digital advertising efforts.

The role of data science in ad tech

If you’re wondering how you can drive better outcomes for your ads, you need to understand that widespread collection and a clever utilization of data will form the foundation of tomorrow’s advertising industry.

Successful audience targeting requires you to get down to the tiniest detail of what they need, how they want it to be delivered to them, when and using what medium.

Here’s how data science can help you rule the ad tech world:

  1. New market identification:

    One of the most difficult tasks for any advertiser is finding a new market. As the global marketplace gets increasingly cluttered, you will have no choice but to rely on advanced data science algorithms to detect new markets of potential customers. Only those who invest in data science will pull ahead. Data science tools can not only help you assess the pulse of new markets but it can also help in getting more returns from your advertising investment.
  2. Audience assessment and management:

    Data science can allow you to unearth deep insights from large data sets, and receive behavioral clues from cookies, web analytics, user-generated content, and other big data sources. Using such data, you can define segments based on real insight into the behaviors of your customers. You will also be able to verify quality audiences by testing your campaigns for factors like recency, frequency, and depth of data. For instance, The Times uses a data-crunching tool called Readerscope that tracks which topics resonate the most with which readers. Rather than relying on audience demographics, it assesses audiences on their behaviors, pain points, and interests, and how they intersect with content.
  3. Attribution modeling:

    Determining what message drove an ad campaign’s successor why a set of users in a specific geographic location preferred one campaign over another has always been difficult. But no longer. With data science tools, you can analyze millions of terabytes of data from converters and non-converters alike, and accurately identify and attribute the precise event that led to a conversion. Thanks to modern data science algorithms, you can now better understand the customer’s purchase journey, and look deep into marketing channels and devices to improve touch points and enhance ad messaging.
  4. Data Science Real-time bidding:

    Another great way to drive meaningful returns on your ad spend is by leveraging data science for real-time bidding. With modern users accessing a range of content online, from a range of devices, data science can allow you to segment your audience and get a deeper understanding of conversion events. You can scale up the buying process and enable direct targeting and re-targeting of individual users. For example, if your user looked at a product on a website, then transitioned to their social media account, you can showcase an ad for the very same product – thus driving higher value and revenue.
  5. Ad spend relocation:

    The clusters created through complex data science modeling can help you test the impact of your ads and make important decisions about ad spend relocation. You can develop algorithms that enable discrete testing of the results of different campaigns; when your ads perform or meet key performance indicators, you can relocate ad spend from under-performing ad sets to those that have a higher impact. You can then periodically feed campaign results back into the data science algorithms, and continuously refine your statistical models for improved performance.
  6. Fraud detection: 

    In the world of programmatic advertising, the likelihood of fraud is,unfortunately, unacceptably high. However, with data science tools, you can curb the fraudulent representation of online advertisement impressions, clicks, and conversions. Using data science algorithms, you can compare the statistical properties about the click behavior of regular users, v/s the click properties associated with that of malicious bots. You can then tweak your algorithms to stay ahead of the ever-evolving strategies of bots.

Reinvent the ad tech space

The fact is, in today’s data-obsessed world, it’s impossible to drive value in the advertising industry without using modern ad tech like data science software. Given that data in digital advertising comes from many different sources, and is accumulating at an exponential rate, data science can enable you to completely rediscover the ad tech space.

Using data science algorithms, you can extract relevant and timely insights into your audience, your campaigns, your market, and your competitors. By understanding the different behavioral patterns of your audiences, you can not only showcase the right ad at the right time through the right channel but you can also answer more strategic questions around what content resonates with them, and why they buy your products. And that’s what winning in ad tech should feel like!

We surveyed over 450 industry leaders about the challenges & opportunities they foresee in the CookieLess World.

The report has the insights & recommendations for you to be prepared for the changes to come.