Technology How new AI tools can transform customer engagement and...

How new AI tools can transform customer engagement and retention


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

Online marketing is dominated by third-party cookies — tracking codes placed on websites to extract information from users — and data brokers who sell the information in bulk.

However, this billion-dollar business, which has been around for decades, is now checkmated by a perfect trifecta: new privacy laws, major technical limitations, and global consumer privacy trends.

While the end of cookies is inevitable, companies are still struggling to find new advertising techniques. Statistics The January report shows that 83% of marketers still rely on third-party cookies, spending $22 billion on this obsolete technique by 2021.


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In this report, we dive into the complexities of digital advertising transformation and show how new technologies, machine learning (ML) and AI are opening up new opportunities for the industry.

Using third-party data has become a risky strategy. Companies that fail to comply with data privacy laws can face millions of fines for data breaches or misuse. For example, defying the General Data Protection Regulation (AVG) could reach €20 million (approximately $21.7 million) or 4% of a company’s annual global revenue by 2023.

And the legal data landscape goes far beyond the GDPR; it is diverse, constantly evolving and growing. From state laws such as the California Consumer Privacy Act (CCPA) to federal laws such as the Health Insurance Portability and Accountability Act (HIPAA), businesses must identify the laws that apply to their operations and know the risks.

The dangers of running third-party data campaigns don’t stop in the courts. Brands that fail to meet 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 privacy-safe approach.

The problem is not new – privacy concerns have been mounting for years. 2019, Church pew Research 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 companies to protect their data. Failure to do so will result in a devaluation of brand perception and potential loss of customers and business partners.

The main barrier to third-party data comes from online giants themselves. Companies like Apple, Google and Microsoft are at the forefront of ending cookies. Growing restrictions make it more difficult for marketers to get consumer data on a daily basis.

First-hand data – obtained with consent in a direct relationship with the user, for example when making a payment transaction or agreeing to the terms and conditions when signing up – is trending and expected to replace third-party data. First-hand data is also of better quality, as it goes beyond limited information based on age, location and gender. In addition, companies can use first-party data to create modern data marts.

ML and AI: from raw data to value to action

First-hand data, such as data collected from endpoints such as point of sale (POS) terminals, can generate data and significant 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 reports.

The challenge shared by both start-ups and large companies is building, maintaining and managing the first-party data they collect from their customers in so-called ‘data marts’.

Imagine the huge amount of raw data a company can generate. Even if this is first-hand data – coming directly from their customers – not everything can be used, is accurate or valuable. And that’s what LFT campaign leaders have to deal with. They have to scan a sea of ​​raw data to find highly specific information.

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

Understand data marts

Data marts are a subset of information found in data warehouses. They are built for decision makers and business intelligence (BI) analysts who need quick access to customer-facing data. Data marts can support production, sales and marketing strategies when put together 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 build them. This is why the automation, augmentation, and computational power of machine learning (ML) and AI have become the tip of the sword in the new era of data-driven predictive marketing analytics.

Feature engineering: Building consumer buying signals

Feature engineering is a critical component for AI and ML applications to effectively identify features – valuable data. It can be time consuming to select the right features for the AI ​​algorithm to use to generate accurate predictions. 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. ML-powered feature discovery and engineering can accelerate this process to just minutes or days.

Automated feature engineering can simultaneously evaluate billions of data points across multiple categories to discover the critical customer data needed. Companies can use ML feature engineering technologies to extract vital information from their data marts, such as customer habits, history, behavior, and more. Companies like Amazon and Netflix have mastered feature engineering and use it every day to recommend products to their customers and increase engagement.

They use customer data to create so-called consumer buying signals. Consumer buying signals use relevant characteristics to construct groups, subsets or categories using cluster analysis. Typically, signals are grouped according to customer requirements, for example “women and men who play sports and have an interest in wellness”.

But developing and deploying the AI ​​apps or ML models to run signal-based targeted marketing campaigns is not a one-time task. AI/ML systems need to be maintained to ensure they don’t drift and generate inaccurate predictions over time. And data marts must be constantly updated for data changes, new data additions, and new product trends. Automation in this step is also essential.

In addition, visualization is key. All stakeholders must have access to the data generated by the system. This is achieved by integrating the ML model into the business intelligence dashboards. Using BI dashboards, even those within the company who don’t have advanced data science or computer skills can use 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 taken off. Despite this accelerated pace of innovation, companies and developers must strive to stay ahead. The way forward is easy. Companies need to explore ways the 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 work its way toward a new, never-before-imagined solution unique to each company. But planting the seed of AI ideas is just the beginning of the journey. Craft and hard work are needed to keep going. The potential of ML and AI, in this perspective, is endless and highly adaptable, capable of serving any organization to achieve its unique goals and objectives.

Ryohei Fujimaki is founder and CEO of dotData.

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