Big Data Ads: How to Smoothly Turn Ad Systems Into Big Data

Big Data Ads – Big data is seen as the next big challenge in marketing innovation. The digitization of the marketing and advertising industry leads to the accumulation of huge amounts of data that must be processed and analyzed.

Companies can capitalize on big data by making end-to-end, real-time data-driven decisions that enable them to streamline processes and enhance the ability to tailor and personalize services. 

Custom advertising software development provides effective solutions that are more cost-effective and better perceived by the target audience.

Big Data = Big Opportunities

Big Data Ads How to Smoothly Turn Ad Systems Into Big Data

The ability to collect and analyze data from both internal and external sources is critical to successful digital advertising.

The problem arises because 80% of the data is unstructured. Photos, videos, and social media posts are pieces of information that say a lot about us, but cannot be processed with conventional methods. Big data allows companies to analyze all the collected data and gain valuable insights.

Using Big Data to Optimize Ads

Targeting a campaign to a specific audience by using the information you have about their interests and preferences is the main goal of personalization. That’s when big data becomes an extremely valuable source of reference information.

A key benefit of using big data in advertising is improved communication. With improved data accuracy, advertising becomes more relevant and less costly.

Name, email address, gender, age, location, payment history and search queries are just a small part of what is stored in the database. 

Big data allows you to analyze, organize and organize information to further use the results to create noticeable advertising algorithms and create proper personalized advertising content. As a result, each user receives a personalized message based on their choice, previously visited websites, related searches, etc.

Because advertising is an integral part of the media industry, it has historically been conducted purely on the basis of speculation. Nowadays, with the help of big data solutions, advertising companies can better understand consumer habits and get a more detailed understanding of their behavior. 

Big data solutions not only predict what customers want to hear from ads, but also predict the performance of high-load systems, making them an essential component of a results-driven advertising system.

Big data and branding

The purpose of branding campaigns is to build reputation or brand awareness. Historically, this has been the domain of television advertising. 

As a result, online advertising uses TV advertising metrics such as net reach and gross rating points. The effectiveness of a branding campaign is measured by maximum contact with a given target audience.

In some cases, social and demographic factors determine which segments are important. Big data is used to accurately predict these characteristics for as many online users as possible.

Provided that the data is correct, the advertiser can drastically reduce their costs. Advertising reaches only interested users, which leads to a significant reduction in costs. Facebook is a prime example of this form of data use. 

Facebook has access to well-verified age and gender data thanks to its users’ login details, and it has huge reach across multiple devices. The base data is ideal for delivering targeted ads to the right people.

Audience Predictions

Big data analytics is gradually becoming the choice of many media organizations around the world. It creates an ecosystem that captures the attention of consumers.

Big data helps deliver the right content to the right people on the right platform at the right time. 

Because consumers can now choose formats such as on-demand, pay-per-view, streaming media, subscription-based, and more, content can now be distributed across a variety of digital channels, allowing media companies to collect, process, and easily and effectively analyze user data.

The amount of data that is collected every day provides ample opportunity for analysis to find out what content users want. Data collected from social media often reveals underestimated patterns that can pique user interest.

hyperlocal advertising

The proliferation of mobile devices provides digital marketers and advertisers with great opportunities to offer mobile ads that target the right customers. For example, stores may send out advertisements promising discounts or other benefits to nearby customers and encourage them to walk through their doors.

Hyperlocal advertising has been proven to increase customer engagement and conversion rates. 

However, there is a risk of annoyance as some consumers may be intimidated by the fact that advertisers know where they are in real time. Consequently, marketers will have to make certain compromises in order to keep their ads profitable and minimize complaints.

Our experience in the design and development of advertising systems

Moving to a system that runs under heavy load, handles thousands of requests per second, and makes the most of big data will definitely increase the reach of your ad campaign. 

Among the solutions created by SCAND is a highly loaded advertising system. A customer came to our company with the aim of creating a system capable of processing hundreds of millions of user requests per day.

The solution is pretty simple and elegant to get the job done, and scalable enough for future challenges. Currently, the customer has in mind the idea of ​​more frequent use of big data, so this possibility had to be initially provided in the design of the system.

SCAND engineers came up with the following scheme. The front-end cluster with SSL and load balancer is connected to the web server cluster. Next, we should mention the DBMS in the cluster, but in our case, the path to the data is slightly different.

First, the data appears in the Redis RAM cache. It is then passed to the statistics parser before entering the database. When the database needs to return data, it routes it through the prefetcher before reaching Redis, the web server, and finally the front end.

This scheme allows the customer to get the following benefits:

  • A high load solution that can remain operational with a large number of requests.
  • The data goes through a statistics analyzer that helps collect important advertising statistics.
  • The solution easily copes with the collection and analysis of big data.

The latter option is implemented using Hadoop, which adds a new layer of analysis to views and click measurements in the system structure. Thus, the entire advertising system can be easily transferred to Big Data in a short time.

High load testing

The solution has been field tested. With an average system load of millions of requests per hour, it works stably and does not even reach the peak load.

Since the client has several million unique users per month, a real dataset requires hundreds of gigabytes of RAM, even in cache, to count user actions.

This is the task of the real big data that we have. The corresponding algorithms are already under development and can be implemented in the shortest possible time.


Big Data Ads development services for marketing and advertising have become a real trend in recent years. It’s hard to imagine accurate targeting without robust algorithms using big data streams. 

While the fundamentals of advertising remain the same, existing advertising systems concepts, products and services must connect sellers with potential buyers in a completely new and data-driven way.

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