Businesses that base their decisions on verifiable data show better results than their counterparts. Data aggregation is a process of gathering large sets of data to improve business decision-making, marketing campaigns, and investment success rates. Currently, 24% of business executives say that their companies use data-driven decisions, with more and more enterprises investing in data solutions.
Primarily enabled by Internet Technologies, gathering Big Data was further amplified by the pandemic and the gradual shift toward e-commerce platforms. Modern consumers turn to social networks and product review sites before making a purchase, and online brand representation is paramount to remain competitive.
However, a data-driven business model does not come without its challenges. Gathering personally identifiable data or misusing it is a serious violation, as the Cambridge Analytica scandal illustrates. Simultaneously, it's a technologically challenging task that requires cooperation from data engineers, analysts, and database architects.
Data aggregation is a technique that helps process gathered data for further analysis and make it usable. In this article, we'll overview how to use data aggregation techniques to streamline data analysis and increase marketing performance.
What is data aggregation?
Data aggregation is used to collect large sets of data from multiple sources. Businesses that utilize data to increase sales monitor user sentiment on social networks, check competitors' reviews, and analyze ad performance.
For all of this to make sense, all data must be gathered in one place in a readable and searchable format. The first step of data aggregation is data extraction, which we'll explain in-depth in the following chapter.
Data extraction
Although the Internet is filled with publically available and useful information, gathering it is no easy task. Firstly, manual information gathering is nearly impossible when dealing with Big Data. Even a large team would find it tedious to go over millions of social media posts and thousands of review sites. Modern businesses use automated data-gathering techniques, such as web scraping and APIs.
Web scraping is a technique of gathering publicly available web data using web scrapers, often coupled with proxies. You can perform Web scraping with javascript or python, depending on your preference and the project's requirements. Web scrapers are highly customizable software applications that can target dozens of websites simultaneously to retrieve desired information.
For example, web scrapers can connect to online shops and discount sites and gather pricing information that helps marketing specialists set adequate and competitive product costs.
Unfortunately, unethical businesses misuse web scraping to gather private or personally identifiable information. For example, collecting service-related posts from public online forums to analyze user sentiment is perfectly legal. On the other hand, if such forums require login via username and password, they are private and cannot be scraped.
Simultaneously, suppose people use their real name, surname, or other personally identifiable information. In that case, it's essential to anonymize gathered data so that no data unit can be traced back to a particular user.
Due to web scraping misuse, many websites attempt to deny access to web scrapers. It's worth noting that most large companies scrape the web, including Google, Microsoft, Facebook, etc., even though they might try to limit information access at the same time. That's why web scrapers often use proxies and scraping APIs to bypass information blocks.
Proxies are highly popular networking tools that obfuscate the original user IP address and reroute online traffic through an additional server called a proxy. It assigns a new IP address and handles all online communication. Websites that notice multiple data requests coming from the same IP address can blacklist it and deny access. Web scrapers use hundreds of proxy servers to obtain a new IP address for each information request making such operations invisible to website detection systems.
API stands for Application Programming Interface, which is used for information sharing between two consenting parties. For example, Scraper APIs can retrieve information from search engines without worrying about detection because the latter agree to share it. Unlike web scrapers, APIs don't have to deal with regular IP rotation or CAPTCHAs. On the other hand, APIs can access only the data that is willingly shared by the source. Meanwhile, web scrapers can target any data within ethical boundaries.
High-quality APIs and web scrapers retrieve data in organized JSON or XML formats ready for further analysis, making them invaluable tools for data aggregation.
Data aggregation levels
There are three levels of data aggregation: beginner, intermediate, and master.
Businesses that use beginner-level data aggregation methods use data-driven decisions but do not collect data themselves. For example, companies that use Google Analytics to optimize conversion channels rely on Google to collect, aggregate, and visualize information. This method is excellent for small businesses and emerging brands because it's time and resource-friendly. Still, it is unfit for large enterprises with unique marketing strategies that require a different approach.
Intermediate-level data aggregation uses a customizable dashboard to analyze data according to individual business needs. It includes using spreadsheets to monitor and analyze data and outline correlations. These spreadsheets are continuously updated with new information to remain relevant.
However, in-house solutions are time-consuming and not always the best. That's why master-level data aggregation instead relies on dedicated third-party software to collect, store, and analyze information. This does not mean outsourcing the whole task to other businesses. Instead, employees master valuable tools, like discussed web scrapers and APIs, to gather and visualize business intelligence.
Data aggregation challenges
As many data analysts know, dealing with it is challenging and susceptible to many mistakes. Furthermore, because businesses rely on aggregated data to drive revenue, these mistakes can be pretty costly.
The biggest mistake is gathering and using personally identifiable data (PII). The General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US define lawful data gathering, storage, and utilization techniques. GDPR recently issued a 1.3 billion fine to Meta for data privacy violations, so this is no laughing matter. Data aggregation must involve data anonymization if your business relies on information that includes PII.
Another critical issue is data security. The same GDPR and CCPA initiatives require businesses to safeguard user data, which is especially important post-pandemic when many business operations are performed online. Data must be kept in an encrypted format to prevent data leaks and unauthorized access. If an enterprise uses proprietary servers to store it, it must ensure physical server security.
However, subscribing to a professional third-party Cloud storage service is a better way. These businesses ensure server safety and data backups for recovery in case of an accident and use advanced encryption algorithms to prevent data leaks.
Lastly, data aggregation must have clear boundaries. Defining exactly what data you gather would be best because it's costly and time-consuming. Furthermore, gathering too much data will complicate data analysis tasks. For example, duplicate data can interfere with and twist the results and worsen the marketing strategy instead of improving it.
What's next: improving marketing performance
Once you master data aggregation and ensure data safety, you can proceed with using it for better marketing performance. Here are its main use cases.
Conversion channel monitoring
Currently, businesses use multiple channels to drive sales. For example, they utilize retail sites, their own website, social networks, search engines, paid ads, and SEO.
Monitoring them manually takes too much time and requires multiple employees familiar with these platforms. Instead, data aggregation software can be customized to retrieve specific data for each channel and visualize it intelligibly.
Customer retention and acquisition
Businesses that want to maintain a steady, long-lasting revenue must know what their consumers like. Using data aggregation, you can verify the performance of each of your channels and see the percentage of new or recurring users.
Statistical analysis will promptly reveal underperforming channels, and you can focus on improving them. Furthermore, it prevents you from spending additional resources on channels that are already performing well, and you can invest the funds elsewhere.
Social media marketing
Social media is one of the biggest user data repositories. Nowadays, many buyers first go online to check service reviews and opinions, often on social networks. Going through thousands of posts manually is inefficient and susceptible to human error.
Web scrapers and Scraper APIs are designed to target specific publicly available information and extract it in a readable format. Instead of getting a .txt document with copied posts that are hard to analyze, you can get a JSON or XML document with extracted user sentiment keywords, brand mentions, and relevant opinions. More so, these file formats immediately be used with data analysis software for further insights.
Conclusion
Data-driven business decisions are here to stay as more and more enterprises rely on business intelligence to improve sales. In reality, most established and emerging brands now collect and analyze user data.
Companies that don't invest in data aggregation are quickly outperformed and pushed out of the competition. We hope this article illustrates the importance of data aggregation and will help you start using it to improve marketing performance.