How new AI tools can transform customer engagement and retention

The global digital advertising sector is undergoing a seismic shift as the cookieless future continues to gain momentum. Companies are being forced to rethink how they approach their customers.

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

But this multi-billion dollar business that has existed for decades is now being put to rest by the perfect trifecta of new privacy laws, regulation of big tech, and global consumer privacy trends. is being applied.

Despite the inevitable cookie demise, businesses still struggle to find new ways to advertise. His January report from Statista revealed that 83% of his marketers still rely on third-party cookies, and that he will spend $22 billion on this outdated technique in 2021. became.

This report delves into the intricacies of digital advertising transformation and reveals how new technologies, machine learning (ML), and AI are opening up new opportunities for the industry.

Digital Marketing Challenges, Risks and Emerging Trends

Using third-party data has become an all-or-nothing risk strategy. Companies that fail to comply with data privacy laws can face millions of dollars in fines for data breaches and misuse. For example, a breach of the General Data Protection Regulation (GDPR) could result in losses of up to €20 million (approximately $21.7 million), equivalent to 4% of a company’s global annual turnover in 2023.

And the legal data landscape goes far beyond 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 can identify the laws that apply to their business and understand their risks need to do it.

The dangers of running third-party data campaigns don’t end in court. Brands that do not meet consumer expectations risk losing customers and business opportunities. His MediaMath survey in 2022 revealed that 84% of consumers are more likely to trust brands that prioritize the use of their personal information with a privacy-safe approach.

This issue is not new. Privacy concerns have been on the rise for years. In 2019, Pew Research reported that 79% of Americans are “concerned about how companies use their data.” Privacy will be a top priority in 2023, and customers expect companies to protect their data. Otherwise, you may lose brand recognition and lose customers and business partners.

The biggest barriers to third-party data are posed by the online giants themselves. Companies like Apple, Google, and Microsoft are leading the way in phasing out cookies. Increasing regulation makes it difficult for marketers to capture consumer data on a daily basis.

First-party data (for example, data obtained in a direct relationship with a user and based on consent, such as when making a payment transaction or agreeing to terms upon sign-up) is trending, and third-party data expected to be replaced. First-party data goes beyond limited information based on age, location, and gender, so quality is also better. Additionally, enterprises can create modern data marts using first-party data.

ML and AI: From raw data to value to action

First-party data collected through endpoints such as point-of-sale (PoS) terminals can create data and great potential to target lifetime value (LFT) customers. LFT campaigns are trending as companies like Uber, DoorDash and Spotify look for new ways to reach their customer base, according to Reuters.

A challenge shared by both start-ups and large enterprises is building, maintaining and managing first-party data collected from customers in what are known as “data marts”.

Imagine the sheer amount of raw data a company can generate. Even if this is first-party data obtained directly from the customer, not all of it is usable, accurate, or valuable. And that’s what LFT campaign managers have to deal with. Finding very specific information requires scanning large amounts of raw data.

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

Understanding data marts

A data mart is a subset of the information found in a data warehouse. They are built for decision makers and business intelligence (BI) analysts who need quick access to customer-facing data. A data mart can support you by efficiently orchestrating your production, sales, and marketing strategies. But building them is easier said than done.

The challenge with first-party data marts is the amount of raw data analysis required to build them. This is why the automation, augmentation, and computing power of machine learning (ML) and AI are at the forefront of a new era of data-driven marketing predictive analytics.

Feature Engineering: Building Consumer Buying Signals

Feature engineering is a key component for AI and ML applications to effectively identify features (valuable data). It can take some time to choose the right features that an AI algorithm can use to generate accurate predictions. This is often done manually by a team of data scientists. We manually test different features and optimize our algorithms, but this process can take months. ML-powered feature discovery and engineering can reduce this process from minutes to days.

Automated feature engineering can simultaneously evaluate billions of data points across multiple categories to discover the critical customer data you need. Businesses can use ML feature engineering technology to extract important information such as customer habits, history, and behavior from data marts. Companies like Amazon and Netflix have mastered feature engineering and use it routinely to recommend products to their clients and increase engagement.

They use customer data to create so-called consumer buying signals. Consumer buying signals use related features to build groups, subsets or categories using cluster analysis. Signals are typically grouped according to the customer’s wishes, such as “athletes and health-conscious women and men”.

But developing and deploying AI apps and ML models to run signal-based targeted marketing campaigns is not a one-time job. AI/ML systems must be kept from producing inaccurate predictions and drifting over time. Data marts also need to be continuously updated as data changes, new data is added, and new product trends are introduced. Automation of this step is also essential.

Additionally, visualization is important. All parties should have access to the data that the system generates. This is achieved by integrating ML models into business intelligence dashboards. BI dashboards make data available to people in your company who may not have advanced data science or computing skills. BI dashboards can be used by sales teams, product development, executives, and more.

final thoughts

AI and ML have been around for decades, but it’s only been in the last few years (or months, in the case of generative AI) that they really took off. Despite this accelerating pace of innovation, companies and developers must strive to stay ahead. The way forward is simple. Businesses need to consider how technology can be used to solve real-world problems.

In the case of data privacy, cookie termination, and third-party data termination, using AI to revisit this original problem and innovate avenues for new, previously unthinkable solutions that are unique to each company. I can. But planting the seeds of AI ideas is only the beginning of the journey. It takes skill and effort to make it to the end. From this perspective, the possibilities of ML and AI are endless and highly customizable, helping each organization achieve its own goals and objectives.

Ryohei Fujimaki is the founder and CEO of dotData..