How new AI tools can transform customer engagement and retention

As the cookie-free future continues to gain momentum, the global digital advertising industry is experiencing a tectonic shift. Companies are being forced to reinvent the way they reach customers.

Online marketing has been dominated by third-party cookies (tracking codes posted on websites to extract user information) and data brokers who sell the information in bulk.

However, this multibillion-dollar business, perpetuated for decades, is now in check by a perfect trifecta: new privacy laws, big tech restrictions, and global consumer privacy trends.

While the end of cookies is inevitable, companies are still struggling to find new advertising techniques. Statesman’s The January report reveals that 83% of marketers still depend on third-party cookies, spending $22 billion on this outdated technique in 2021.

In this report, we’ll delve into the intricacies of digital advertising transformation and reveal how new technologies, machine learning (ML), and artificial intelligence present new opportunities for the industry.

The challenges, risks and new trends of digital marketing

Using third-party data has become a high-risk risk strategy. Companies that fail to comply with data privacy laws can face millions in fines for data violations or misuse. For example, challenging the General Data Protection Regulation (GDPR) can cost up to €20 million (about $21.7 million) or 4% of a company’s annual global revenue in 2023.

And the legal data landscape goes far beyond the GDPR; it is diverse, constantly evolving and growing. From state laws like the California Consumer Privacy Act (CCPA) to federal laws like the Health Insurance Portability and Accountability Act (HIPAA), businesses need to identify which laws apply to their business and understand the risks.

The dangers of running third-party data campaigns don’t end with the courts. Brands that don’t align with consumer expectations risk losing customers and business opportunities. A 2022 MediaMath survey revealed that 84% of consumers are more likely to trust brands that prioritize the use of personal information with a secure approach to privacy.

The problem isn’t new: Concerns about privacy have been growing for years. In 2019, Bench Search reported that 79% of Americans were “concerned about how companies are using their data.” In 2023, privacy has become a top priority and customers expect businesses to protect their data. Otherwise, brand perception is devalued and the potential loss of customers and business partners.

The most significant barrier to third-party data comes from the online giants themselves. Companies like Apple, Google and Microsoft are leading the way in the end of cookies. Growing restrictions make it more difficult for marketers to obtain consumer data on a daily basis.

First-party data, obtained with your prior consent in a direct relationship with you, such as when you make a payment transaction or agree to terms at registration, is trending and expected to replace third-party data . First-party data is also of better quality, as it goes beyond limited information based on age, location, and gender. Additionally, companies can use first-party data to build modern data marts.

ML and AI: From raw data to value to action

First-party data such as that collected through endpoints such as point of sale (POS) terminals can generate significant data and potential to target lifetime value (LFT) customers. LFT campaigns are trending as companies like Uber, DoorDash and Spotify find new ways to reach their customer base, Reuters relationships.

The challenge shared by both startups and large enterprises is to build, maintain and manage the first-party data they collect from their customers in what are known as “data marts”.

Imagine the vast amount of raw data a business can generate. Even when it comes to first-party data, coming directly from your customers, not all of it can be used, is accurate or valuable. And that’s what LFT campaign managers are faced with. They have to scan through a sea of ​​raw data to find very specific information.

This is where AI and ML come into play. AI/ML applications can find that needle in the haystack and do much more when managing data marts.

Understanding data marts

Data marts are a subset of information found within data warehouses. They are designed for decision makers and business intelligence (BI) analysts who need quick access to customer-facing data. Data marts can support manufacturing, sales, and marketing strategies when built efficiently. But building them is easier said than done.

The challenge with first-party data marts is the amount of raw data analysis required to create them. This is why the automation, augmentation, and computing power of machine learning (ML) and artificial intelligence have become the sword’s edge in the new era of data-driven predictive marketing analytics.

Feature engineering: Creating buying signals from consumers

Feature engineering is a crucial component for AI and ML applications to effectively identify features – valuable data. Selecting the right features that the AI ​​algorithm can use to generate accurate predictions can be time consuming. This is often done manually by teams of data scientists. They manually test different features and optimize the algorithm, a process that can take months. Discovering and engineering features powered by machine learning can accelerate this process to minutes or days.

Automated feature engineering can simultaneously evaluate billions of data points across multiple categories to uncover the critical customer data you need. Enterprises can use ML feature engineering technologies to extract critical insights from their data marts, such as customer habits, history, behaviors, and more. Companies like Amazon and Netflix have mastered feature engineering and are using it daily to recommend products to their customers and increase engagement.

They use customer data to create what are known as consumer buying signals. Consumer buying signals use relevant features to create groups, subsets or categories using cluster analysis. Signals are usually grouped according to customer wishes, for example “women and men who play sports and are interested in well-being”.

But developing and deploying AI apps or ML models to run signals-based targeting marketing campaigns is not a one-time thing. AI/ML systems must be maintained to ensure they don’t drift, generating inaccurate predictions over time. And data marts need to be updated continuously for data changes, new data additions, and new product trends. Automation at this stage is also essential.

Also, visualization is key. All interested parties must be able to access the data generated by the system. This is achieved by integrating the ML model into business intelligence dashboards. Using BI dashboards, even those within the company without advanced data science or computer science skills can make use of the data. BI dashboards can be used by sales teams, product development, executives and more.

Final thoughts

While AI and ML have been around for decades, it’s only in the last few years (and months for generative AI) that they’ve really made quantum leaps. Despite this accelerated pace of innovation, businesses and developers must strive to keep up with the game. The way forward is simple. Companies need to look at ways technology can be used to solve real-world problems.

In the case of data privacy, the end of cookies, and the end of third-party data, AI can be used to revisit this original problem and innovate its way to a new, never-before-thought-of solution that is unique to each business. But planting the seed of AI ideas is just the beginning of the journey. It takes craft and hard work to pull off. The potential of ML and AI is, in this perspective, infinite and highly customizable, and able to serve every organization to achieve their unique goals and objectives.

Ryohei Fujimaki is the founder and CEO of pointDate.