Cloud Marketing and AI Analytics: How Businesses Use Data to Optimize Advertising

Over the past few years, I have been searching for answers to where digital analytics is headed. Many executives in the field are also thinking about this subject because it affects team formation, skill priorities, and the choice of working tools.

In this article, I will examine how analytics have transformed in response to technology, services, and market changes. I will demonstrate how businesses are adapting to new requirements, increasing data efficiency, and implementing solutions that seemed futuristic just yesterday. Additionally, I will present case studies involving Google Cloud, AI, BigQuery, and GA4.

This article is based on a talk by Oleksandr Konivnenko, the head of the web analytics department at Netpeak Ukraine.

What is cloud marketing?

Cloud marketing is a strategy that uses cloud technologies to collect, process, store, and activate marketing metrics. It covers the entire audience engagement cycle, from receiving signals to creating real-time personalized campaigns.

This approach allows you to centralize data from various sources, such as web, mobile apps, CRM, and advertising accounts, and combine it in cloud storage, like Google BigQuery. You can then analyze the data using machine learning (ML) and artificial intelligence (AI) and send it back to the audience and advertising systems.Cloud marketing has several benefits:

• Reduce time spent on analytics.

• Better understand your customers through integrated data.

• Automate campaign optimization processes.

• Increase your return on advertising investment.

• Adapt to new privacy challenges and the absence of third-party cookies.

Why is digital analytics not just about metrics?

Many people have heard of platforms such as Google Cloud and BigQuery, but few businesses truly understand how to use them and the opportunities they offer.

Data alone does not change anything. To produce results, data must be transformed into insights and then into decisions. This is where the challenges begin. Businesses often lack the following key requirements:

• Clear data architecture

• User-friendly interfaces for working with information

• An analytical culture within the team

• Transparent decision-making processes based on data

These barriers slow down development, reduce efficiency, and hinder growth. This is why analytics should be viewed as a strategic function rather than a mere report maintenance task.

Digital analytics today: challenges and opportunities

In 2025, analytics is much more than a counter on a website. Data comes from everywhere: the web, mobile apps, smart TVs, and the Internet of Things (IoT).

Add to that the various behavioral scenarios, offline interactions, calls, GDPR restrictions, and the absence of cookies, and it's clear why accurate analysis is difficult. This complicates digital marketing. As a result, algorithms work less effectively, attribution is vague, and reporting takes more time.

Another challenge is the changing privacy policies. Users often opt out of tracking, which reduces the amount of data available for advertising systems. With less information, algorithms are less effective, analytics become incomplete, and campaign effectiveness declines.

Therefore, businesses must compensate for these losses by collecting first-party data directly and fostering closer collaboration between analytics and marketing teams.

At the same time, a powerful ally emerges: artificial intelligence. With regard to analytics, AI has several capabilities:

• Writes SQL queries and automates code

• Generates hypotheses about changes in traffic

• Accelerates time to insight for the business

• Produces reports and explanations in plain language

• Works alongside the analyst as a “research assistant”

This is not the future; this is now. That's why companies need to structure their data quickly to be ready for AI-driven marketing optimization.

How Google tools are changing

Services are being redesigned to meet new requirements.

• GA4 is not just for website analytics. It's also a source of events to be sent to BigQuery, Google Ads, or Looker Studio.

• Consent Mode v2 is a Google mechanism for collecting data with user consent and privacy requirements. If a visitor declines tracking, it allows you to compensate for losses with simulated data while keeping privacy restrictions in mind.

• Google Ads is increasingly using machine learning (ML) and first-party data-based models.

• BigQuery is becoming the central hub for storing metrics, creating reports, building segments, transferring audiences, and sending signals back to advertising platforms.

As you can see, the entire Google stack is shifting toward end-to-end systems, from data collection to activation. This makes it important to build a data architecture that helps you scale and adapt to change.

In my experience, the biggest breakthroughs happen when businesses trust analytics.

For example, one Ukrainian e-commerce project achieved significant results after implementing end-to-end analytics based on BigQuery. Specifically, it was able to:

• Calculate ROMI, taking into account LTV (customer lifetime value).

• Automatically stop unprofitable campaigns.

• Run ML models for predictive segmentation.

• Transfer segments to Google Ads via Conversion for Leads.

Digital Maturity Framework: where businesses are heading and why it matters

To understand your current position and adapt to the new reality, consider the Digital Maturity Framework, a concept developed by the Boston Consulting Group. In short, digital maturity is the extent to which a business can achieve its goals through digital solutions.

In its study, BCG analyzed how deeply companies are integrating digital solutions into their processes, especially in data management. They found varying results, ranging from basic levels of integration to personalized, automated campaigns based on integrated data.

Most interestingly, the study revealed a clear correlation between digital maturity levels and market share and revenue growth. Companies with higher levels of digital maturity demonstrate market share growth 40% more often than those with lower levels of maturity. They also saved up to 29% on their budget without losing advertising effectiveness.

The BCG model identifies four levels of company development based on the extent to which digital technology is used:

• Nascent

• Emerging

• Connected

• Multi-moment

How to understand what BCG level your business is at

I will briefly explain each stage.

1. Nascent: Companies at this level are just starting to use basic digital tools, but they aren't integrating them yet.

At this level, you have a page on Instagram, for example, and drive traffic to it through Facebook ads. There are no integrations or CRM, just a basic set of "out of the box" tools.

2. Emerging level: There are integrations, such as linking Google Analytics 4 (GA4) to Google Ads or uploading expenses from Facebook to Google Analytics.

For instance, you may integrate Google Ads with Google Analytics. This allows you to access advanced reporting, helps you understand the user journey, and partially automates the training of advertising algorithms.

3. Connected: All data is aggregated in one place, such as BigQuery, with end-to-end measurement, reporting, and custom attribution.

At this level, you are combining information from your CRM, analytics, and advertising accounts. End-to-end analytics are in place, providing a comprehensive view of all interactions with the business at all stages, from the first touch to purchase and repeat actions.

4. Multi-moment: The company uses analytics for more than just reporting. It also uses analytics to automate activation, which involves implementing marketing activities such as advertising, mailings, and PR. The company also uses analytics for personalization based on insights.

At the multi-moment level, you don't just collect and analyze data; you automatically use the results of your analysis to optimize your advertising activities, including personalization, segmentation, bid optimization, and dynamic remarketing. The system works in real time and adapts to user behavior.

End-to-end analytics

At Netpeak, one of the most common solutions we offer is end-to-end analytics. We start by collecting data from three key sources: Google Analytics, advertising accounts (e.g., Meta and Google Ads), and the client's CRM system.

We then consolidate this data in the Google BigQuery cloud database, update it daily, and combine it by transaction or user ID. Finally, we visualize the results in Power BI or Looker Studio.

What does the business get?

The business gets a ready-made dashboard system that updates automatically every day. This system provides a clear picture of channels that are delivering results, how profitable they are, and where budgets are potentially being wasted. Users can easily open the report and see accurate data based on real transactions, not just web events.

Once configured, maintaining such a system is easy. However, it's important to understand that this is an ongoing process. End-to-end analytics is like a living organism. You must ensure the scripts are functioning properly, adapt them to new business needs, and consistently test different hypotheses. However, the results are worth it.

How AI helps with analytics and marketing

One notable area is the automation of advertising campaigns. We have partners and clients for whom we have built systems that collect data, analyze campaign effectiveness, and optimize campaigns by adjusting rates and budgets and stopping unprofitable connections.

Such systems can work wonders, but you need a high-quality data structure in place. Without one, no automation will be effective. In our case, we build the entire system on Google Cloud, where we store and process data. Then, we write code that automatically identifies patterns and responds.

This approach offers numerous advantages for businesses:

• Minimize the human factor.

• Respond quickly to changes in indicators.

• Save the budget in a stable and consistent manner.

Netpeak Ukraine has case studies where clients achieved a 20–30% increase in advertising profitability thanks to daily, automatic bid optimization based on actual sales from CRM.

Digital maturity is an endless journey of possibilities. BigQuery integrations are now free, Google Cloud services scale automatically, and AI algorithms are ready to be used for your business. Implementation costs are generally lower than those of ready-made CDPs or SaaS analytics, especially for small and medium-sized businesses. However, you need a team, processes, and a strategy. The best time to start is today.

Recommendations for immediate action

1. Create a Google Cloud account. No payment is required until you use the resources.

2. Set up an export from GA4 to BigQuery. Note that data is only collected from the moment of connection.

3. Encourage the analytics and marketing teams to work together. Teams should collaborate and develop a coordinated strategy.

4. Bring in experts. Working with SQL, APIs, and attribution models requires specialized knowledge.

5. Assess your level of development. Take the Digital Maturity Framework survey to receive specific recommendations on how to proceed.

Conclusions

1. Digital analytics has evolved from acting as a reporting tool to being a critical component of business decision-making architecture.

2. Integrating Google Cloud, BigQuery, GA4, and AI allows you to reduce time to insight, automate optimization, and increase ROMI.

3. Due to the loss of third-party cookies and increasing privacy restrictions, first-party data and end-to-end analytics are becoming the foundation of effective marketing.

4. The Digital Maturity Framework provides a clear understanding of a company's current stage of development and the factors preventing it from scaling.

5. Creating a single hub for metrics, integrating with a CRM system, and using AI solutions is not just a trend; it's the new standard for working with data.

6. To take advantage of these opportunities, take the first steps: set up GA4 exports, assess your current maturity level, and bring in technical expertise.