The future of cookies is gaining momentum as the digital advertising market is experiencing a tectonic change. Companies are required to change the way they interact with clients.
Marketing online has been dominated by third-party cookies — tracking codes posted on websites to collect the information of users — and data brokers that sell the data in large quantities.
The multibillion-dollar enterprise that has been going on for a long time is now under threat with a perfect trio of factors: updated privacy legislation, massive technological restrictions, and global privacy concerns.
Although the demise of cookies is close, businesses are still looking for innovative advertising strategies. Statistics’ January report revealed that 83% of marketers rely on third-party cookie providers, which will cost $22 billion for the outdated method by 2021.
The risks, challenges, and emerging trends in digital marketing
Utilizing third-party data is now an aggressive risk-taking strategy. Businesses that fail to adhere to the privacy of data regulations could be subject to millions of dollars in fines due to data breaches or abuse. In particular, violating regulations like General Data Protection Regulation ( GDPR) could cost the company up to EUR20 million (about $21.7 million) or 4 percent of an organization’s annual global turnover in 2023.
And the legal landscape for data is much more extensive than the GDPR. It’s diverse, changing, ever-changing, and growing. From state laws such as those governing the California Consumer Privacy Act (CCPA) to federal laws such as the Health Insurance Portability and Accountability Act (HIPAA), Businesses should know which laws apply to their operations and understand the potential risks.
The risks of using third-party data for marketing do not end with court decisions. Brands not complying with consumer expectations could lose customers or business potential. In 2022, a MediaMath study found that 84% of customers trust brands committed to protecting personal data in a safe approach.
The issue isn’t new; privacy concerns have increased over the past few years. In 2019, Pew Research found that 79 percent of Americans were “concerned about how companies use their data.” In 2023, privacy has been made the top concern, and consumers expect businesses to safeguard their personal information. In the absence of this, there is a low perception of brands, which could result in the loss of clients and partners.
The biggest obstacle to third-party data is from the online giants themselves. Companies such as Apple, Google, and Microsoft are leading the way in ending cookies. The increasing restrictions make it more difficult for advertisers to access their customers’ data daily.
First-party data — gathered through consent, directly to the person using it, such as in the case of a payment transaction or accepting the terms of service when sign-up -is gaining popularity and will soon replace data from third parties. First-party data are also more high quality because it is more comprehensive than the limited data based on age, place of residence, and gender. In addition, companies can use first-party data to build modern data stores.
ML and AI From raw data to value to
Data from first-party sources, such as those collected by endpoints such as points of sale (PoS) terminals, could produce data and potentially target lifetime value (LFT) customers. Reuters reports that LFT campaigns are trending as businesses like Uber, DoorDash, and Spotify discover new ways to connect with their clients.
Large and startups’ main challenge is establishing, maintaining, and managing the first-party information they gather from their customers. This is referred to by the term “data marts.”
Imagine the enormous quantity of raw data companies can produce. Even if this is first-party data derived directly from their customers, some of it can be used to be accurate, reliable, or valuable. This is the problem LFT campaign managers must face. They must search through a vast ocean of data to discover specific data.
This is the point where AI and ML can be of help. AI/ML tools can help you find the right needle and do much more when controlling data marts.
Understanding data marts
Data marts are one of the subsets of data found in databases. They are created for decision-makers and business intelligence (BI) analysts who are required to quickly access data relevant to clients. When assembled effectively, data marts can help with marketing, sales, and production strategies. However, it is much easier to say than do.
The problem with first-party data marts is the data analysis required. This is why the technology of automation, augmentation, and the computing processing capability that is machine learning (ML) as well as AI is now the top of the line in the period of marketing driven by data analytical predictive.
The art of feature engineering is to create consumer buying signals
Feature engineering is essential for AI and ML applications to identify important features – valuable information. The selection of the appropriate features the AI algorithm can utilize to produce accurate predictions could be lengthy. It is typically performed manually in teams composed of data experts. They manually test features and improve the algorithm, which could take months. Engineering and feature discovery using ML can speed this process up to a matter of minutes or days.
Automated feature engineering can analyze billions of data points from various categories to identify crucial customer information that is required. Companies can utilize ML feature engineering techniques to obtain crucial details from data warehouses, including customer habits, behavior, history, and others. Businesses like Amazon or Netflix have perfected feature engineering and utilize it daily to suggest products to customers and improve engagement.
The use of customer data to produce what’s called buying signals for consumers. These signals are based on pertinent features to create groups, subsets, or categories through cluster analysis. Most signals are grouped by the customers’ preferences, like “women and men who practice sports and have an interest in wellness.”
