The title of the article is How New AI Tools Can Transform Customer Engagement and Retention. In this report, we will explore the impact of the upcoming cookieless future on the global digital advertising sector and how businesses can adapt to this change. We will also discuss the challenges posed by the use of third-party data and explore the potential solutions offered by AI and machine learning (ML).
For decades, online marketing has heavily relied on third-party cookies and data brokers to collect users’ information and track their online activities. However, this business model is now facing significant challenges due to new privacy laws, restrictions imposed by big tech companies, and evolving consumer privacy trends.
While the demise of third-party cookies is inevitable, many businesses are still dependent on them. According to a report by Statista, 83% of marketers continue to rely on third-party cookies and spend a staggering $22 billion on this outdated technique in 2021.
Using third-party data has become a risky strategy, as companies that fail to observe data privacy laws can face hefty fines for data breaches or misuse. For example, violating the General Data Protection Regulation (GDPR) can result in fines of up to €20 million (about $21.7 million) or 4% of the company’s annual global turnover in 2023.
In addition to legal risks, brands that do not prioritize data privacy face the danger of losing trust and loyalty from their customers. A survey conducted by MediaMath in 2022 showed that 84% of consumers are more likely to trust brands that handle personal information with a privacy-safe approach.
Privacy concerns have been growing among consumers for years, with Pew Research reporting in 2019 that 79% of Americans were concerned about how companies use their data. In 2023, privacy has become a top priority for consumers, and they expect companies to protect their data. Failure to do so can result in a devaluation of brand perception and loss of customers and business partners.
The most significant challenge to the use of third-party data comes from the major online giants themselves. Companies like Apple, Google, and Microsoft are leading the way in ending the era of cookies by imposing stricter restrictions on data collection. This makes it increasingly difficult for marketers to obtain valuable customer data on a daily basis.
To replace third-party data, first-party data has emerged as a trending alternative. First-party data is obtained directly from the customers through a direct relationship, such as when making a payment transaction or agreeing to the terms and conditions during sign-up. It is of higher quality compared to third-party data, as it goes beyond basic demographic information like age, location, and gender. Moreover, companies can leverage first-party data to build modern data marts.
First-party data collected through endpoints like point of sale (PoS) terminals holds immense potential for targeted marketing campaigns focused on lifetime value (LFT) customers. LFT campaigns are becoming popular as companies like Uber, DoorDash, and Spotify explore new avenues to reach their customer base.
However, building, managing, and maintaining first-party data marts pose challenges for both startups and established companies. The process involves analyzing a vast amount of raw data to extract valuable insights. This is where AI and ML come into play. AI and ML applications can automate the process of feature engineering, where critical customer data is identified. This significantly reduces the time required to optimize algorithms compared to the manual testing conducted by data scientists.
ML-powered feature engineering technologies can simultaneously evaluate billions of data points across multiple categories to extract relevant customer information. Companies like Amazon and Netflix have successfully utilized feature engineering to recommend products to their clients, thereby increasing engagement.
Developing and deploying AI and ML models for signals-based targeting marketing campaigns requires continuous maintenance to ensure accurate predictions over time. Data marts also need to be regularly updated to incorporate changes in data and emerging product trends. Automation plays a crucial role in streamlining these processes.
Visualization is another key aspect of AI and ML applications. Business intelligence dashboards can integrate ML models, allowing stakeholders to access and interpret the generated data. This accessibility extends to individuals within the organization who may not possess advanced data science or computing skills. Sales teams, product development departments, and executives can all benefit from BI dashboards.
Despite AI and ML technologies being around for decades, recent advancements have propelled them to new heights. Companies and developers need to stay ahead of the game and explore how these technologies can solve real-world problems. AI can offer unique and customizable solutions to the challenges posed by the end of cookies and the reliance on third-party data. However, it requires dedication, hard work, and a thorough understanding of each organization’s goals and targets.
In conclusion, the cookieless future calls for a transformation in customer engagement and retention strategies in the digital advertising sector. AI and ML present new opportunities for businesses to adapt and thrive in this changing landscape. By leveraging first-party data and employing automated feature engineering and data mart management, companies can establish impactful marketing campaigns that prioritize data privacy and build trust with their customers.
