How to Smoothly Turn Advertising Systems to Big Data
Big Data is viewed as the next great marketing innovation challenge. The digitization of the marketing and advertising industries is accumulating massive amounts of data that should be processed and analyzed.
Companies can benefit from Big Data through real-time and comprehensive data-based decision-making that allows them to optimize processes and expand opportunities for tailoring and personalizing services. Custom advertising software development provides efficient solutions that are more cost-effective and are better received by the target audience.
Big Data = Big Opportunities
The ability to collect and analyze data from both internal and external sources is critical for successful digital advertising.
The challenge emerges from the fact that 80% of the data is unstructured. Photos, videos, and social media posts are pieces of information that reveal a lot about us but can’t be processed through conventional methods. Big Data enables companies to analyze all collected data and gain valuable insights.
Using Big Data to Optimize Advertising
Targeting the campaign to a certain audience by leveraging the information you have on its interests and preferences is a bottom line of personalization. That’s when Big Data is an utterly valuable source of reference.
The key benefit of using Big Data in advertising is enhanced communication. Due to improved data accuracy, advertising is becoming more relevant and less costly.
Name, e-mail, gender, age, location, payment history, and search queries are just a tiny part that is stored in a database. Big Data allows analyzing, systematizing, and organizing the information to further use the results for creating prominent advertising algorithms and generating proper personalized advertising content. As a result, every single user gets a personalized message based on its choices, previously visited websites, related searches, etc.
With advertising being an essential part of the media industry, it was historically conducted solely based on assumptions. Nowadays, with the help of Big Data solutions advertising companies can better understand consumer habits and get in-depth insights into their behavior. Big data solutions do not only predict what customers want to hear from advertising but also involve the efficiency of high load systems thus becoming an essential component of a results-oriented advertising system.
Big Data and Branding
The goal of branding campaigns is to strengthen the brand’s reputation or awareness. This has historically been the domain of TV advertising. As a result, online advertising has adopted 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 certain cases, social and demographic factors determine which segments are important. Big Data is used to make accurate predictions of these characteristics for as many online users as possible.
Provided that the data is accurate, an advertiser can dramatically reduce their costs. The advertising reaches only interested users, resulting in a substantial cost reduction. Facebook is a vivid example of this form of data usage. Facebook has access to well-validated age and gender data thanks to its users’ login data, and it has a massive reach across multiple devices. The underlying data is perfect for providing targeted ads to the right people.
Big data analytics is slowly becoming a choice for numerous media organizations around the world. It creates an ecosystem that puts consumers in the spotlight.
Big Data helps to deliver the right content to the right people on the right platform at the right time. As consumers nowadays have the choice to select from formats such as on-demand, pay-per-view, streaming media, subscription-based, and many more, content can now be distributed through various digital channels, allowing media companies to gather, process, and analyze user data easily and effectively.
The amount of data being collected every day provides ample opportunities for mining it to learn what content users want. The data collected from social media often reveals overlooked patterns that have the potential to drive user interest.
The proliferation of mobile devices provides digital marketers and advertisers with great opportunities to offer mobile-specific advertising targeted at the right customers. For example, stores can send out advertisements promising discounts or other benefits to customers who are located nearby and thus stimulate them to walk through their doors.
Hyperlocal advertising has been shown to increase customer engagement and conversion rates. However, there is a risk of irritation because some consumers could feel scared off by the fact that advertisers know where they are in real-time. Therefore, marketers will have to make certain trade-offs to keep their advertising profitable while minimizing complaints.
Our Experience in Advertising System Design and Development
Switching to a system that works under high load, processes thousands of requests per second, and uses Big Data to the maximum will definitely empower an advertising campaign. Among the solutions created by SCAND is a high load advertising system. A customer has come to our company with the aim of building a system that can handle hundreds of millions of user requests per day.
The solution is quite simple and elegant to cope with the task and scalable enough for future challenges. Currently, the customer is keeping in mind the idea of using big data more often, so this opportunity should have been initially provided in the system’s design.
SCAND engineers have come to the following scheme. The front-end cluster with the SSL and balancer is connected to the web servers cluster. Next, we should mention the RDBMS in the cluster but in our case, the path of data is slightly different.
Firstly, data appears in the Redis In-Memory Cache. Then, it is transmitted to the statistics analyzer before achieving the database. When the database should return data, it guides it through the prefetcher before reaching the Redis, the webserver, and finally the front-end.
This scheme a