From Automation to AI: How Artificial Intelligence is Reshaping Digital Data Analysis

Imagine a world where digital data analysis happens automatically. Instead of spending time on tedious data processing, you can focus on drawing insights and making strategic decisions. This is not a vision of the future—it is the reality shaped by artificial intelligence. The question is, is your company ready for this revolution? 

AI has entered the world of data analysis, providing marketers and analysts with powerful tools for automating processes and uncovering hidden patterns. It enables faster and more efficient data processing while delivering deeper insights and predictive analytics. This opens up new opportunities for campaign optimisation, customer experience personalisation, and increased marketing effectiveness. 

In the constantly developing digital marketing industry, adapting to new technologies is key. Companies that fail to leverage AI risk falling behind their competitors. That is why investing in AI-driven skills and implementing secure AI-powered solutions today is crucial for staying ahead. 

Automation vs. AI: where is the line? 

In digital data analysis, the terms “automation” and “artificial intelligence” are often used interchangeably. While they may seem similar at first glance, they represent fundamentally different concepts. 

Automation 

Automation refers to processes where tasks previously performed manually by humans are taken over by machines or software. In data analysis, automation includes tasks like report generation, data change notifications, and audience segmentation. While automation significantly improves workflow efficiency, it operates based on predefined rules and cannot adapt to changing conditions. 

Artificial intelligence 

AI, on the other hand, enables computers to “think” and “learn” in a way that mimics human intelligence. Technologies such as machine learning and deep learning allow AI to analyse data, recognise patterns, and make decisions based on acquired information. 

Where is the boundary between automation and AI? 

The key difference lies in AI’s ability to learn and adapt. While automation follows set rules, AI adjusts its actions based on new data. This capability allows AI to predict future trends, personalise content for individual users, and automatically optimise advertising campaigns. AI goes beyond traditional automation by offering deeper and more advanced data analysis.

AI tools in digital analytics 

AI is making waves in digital data analysis, and its tools are becoming increasingly advanced. Let’s take a closer look at some of the key solutions available on the market. 

Google Analytics 4 (GA4) – AI-powered insights and predictions 

GA4 is a leader in AI-driven analytics, offering intelligent features such as predictive metrics, smart alerts, and data-driven attribution. 

Predictive metrics 

• Churn probability – estimates the likelihood that an active user from the past 7 days will not return in the next 7 days. 

• Purchase probability – predicts the likelihood of a user (active in the last 28 days) making a purchase in the next 7 days. 

• Predicted revenue – forecasts expected revenue from purchases within the next 28 days, based on the activity of users in the last 28 days. 

These metrics allow for the creation of predictive audiences, which identify users with a high probability of taking—or not taking—specific actions in the future. This helps businesses detect customers at risk of churning and target them with personalised offers. 

Analytics insights 

GA4’s AI-powered smart alerts automatically detect anomalies in data, such as sudden drops in traffic, and notify analysts. This feature leverages machine learning to analyse data patterns and identify unusual events. 

Data-driven attribution 

Data-driven attribution models use machine learning algorithms to analyse user conversion paths. This model precisely determines which marketing campaigns and channels have the greatest impact on conversions, assigning appropriate value to each touchpoint in the user’s journey. 

Google Tag Manager (GTM) – powering AI with data 

Although Google Tag Manager (GTM) itself does not utilise AI, it plays a crucial role in collecting and managing data that AI-powered systems later analyse. GTM allows businesses to manage website and app tags efficiently, ensuring seamless data transmission to analytics and marketing platforms like Google Analytics 4 or advertising platforms. These systems then use the collected data to train AI models and optimise marketing efforts. 

By centralising tag management and defining trigger rules, GTM ensures data consistency and accuracy, which is essential for AI models to function effectively. 

Additionally, server-side tagging in GTM offers: 

• Enhanced data control and enrichment – data is first sent to a server, where it can be processed and enriched before being passed to analytics tools, improving the quality of AI training data. 

• More accurate attribution modeling – centralised data processing allows for more precise tracking of user journeys, leading to improved attribution and ROI measurement. 

• Filling data gaps caused by cookie restrictions – server-side tagging enables setting cookies from the server, helping mitigate tracking restrictions imposed by browsers or user settings. 

Piwik PRO – privacy and AI 

Piwik PRO is an analytics platform that combines advanced data analysis with a strong focus on user privacy. As the demand for compliance with data regulations grows, Piwik PRO integrates AI-driven solutions that enable: 

• predictive analytics – forecasting user behaviour based on historical data, 

• machine learning – automatically detecting patterns and trends, 

• advanced AI algorithms – providing deeper insights into user journeys and interactions with the website. 

Additionally, Piwik PRO integrates with A/B testing and user experience personalisation tools, such as: 

• AB Tasty – allows direct access to A/B test results within Piwik PRO, 

• Optimizely – facilitates easy comparison of A/B test results, helping to optimise conversions. 

Both integrations rely on a data layer, ensuring precise analysis of test results and better adaptation of marketing strategies. 

Looker Studio – visualisation and Cloud integration 

Previously known as Google Data Studio, Looker Studio is a powerful data visualisation tool that integrates seamlessly with the Google Cloud ecosystem, enabling: 

• Connecting predictive models from BigQuery ML – users can create, train, and deploy machine learning models directly in BigQuery, allowing visualisation of predictive model results in Looker Studio. 

• Visualisation and monitoring of machine learning predictions – enables interactive dashboards and reports that showcase predictive models’ outputs, such as time series forecasts and classifications. 

• Using Gemini for SQL query generation on public and private datasets – allows users to ask natural language questions about their data, with the system automatically generating corresponding SQL queries in BigQuery, simplifying data analysis. 

Microsoft Excel – AI for everyone 

Microsoft Excel, one of the most widely used data analysis tools, also leverages artificial intelligence through features such as: 

• Analyze Data – automatically detects trends, patterns, and key insights, generating visual summaries. Users can ask natural language questions, and Excel responds with tables, charts, and pivot tables. 

• Forecast Sheet – predicts future values based on historical time series data, creating a dedicated sheet with past and projected values visualised as line or column charts. 

Excel also integrates with generative AI through Copilot for Office, which assists in data analysis by generating formulas, summaries, and visualisations based on user inputs. 

Microsoft Power BI – advanced business analytics 

Power BI is a business intelligence platform that incorporates AI-powered services, offering: 

AI-driven visualisations 

• Key Influencers – analyses factors that have the most significant impact on a given metric, helping businesses make informed decisions. 

• Decomposition Tree – breaks down metrics into hierarchical categories for a better understanding of data. 

Natural language Q&A 

This feature allows users to ask natural language questions about their data, with Power BI automatically generating relevant visualisations, eliminating the need for complex queries. 

Built-in forecasting and anomaly detection 

• Forecasting – predicts future values based on historical data, aiding in strategic planning. 

• Anomaly detection – automatically identifies unusual patterns or deviations in data, enabling quick responses to potential issues or opportunities. 

Integration with Azure AI services 

Power BI seamlessly integrates with Azure AI, allowing users to leverage advanced machine learning models and AI capabilities, including text analysis, image recognition, and predictive modeling. 

Tableau – data exploration and explanation 

Tableau is a leading data visualisation and analytics tool that offers: 

Ask Data – natural language queries 

Previously, this feature allowed users to generate visualisations based on natural language questions. However, it was discontinued in Tableau Cloud (February 2024) and Tableau Server (version 2024.2). 

Explain Data – automated insights 

This feature enables users to discover the reasons behind specific data points by dynamically generating visual explanations, facilitating deeper data exploration. 

Integration with Einstein AI – Einstein Discovery 

Einstein Discovery in Tableau provides AI-powered predictions and recommendations without requiring programming expertise, delivering transparent and reliable insights to all users. 

Tableau Pulse 

A modern approach to analytics that makes data more accessible and personalised. Tableau Pulse is available for Tableau Cloud users and leverages Tableau AI to deliver data in a more contextual and intelligent way. 

Tableau GPT/Agent 

A generative AI feature for data analysis that allows users to interact with data conversationally by asking questions in Tableau. Tableau Pulse uses Tableau GPT to automate insights, presenting them in both natural language and visual formats, making analytics more proactive, and easy to share. 

Advertising platforms – intelligent campaign optimisation 

Advertising platforms such as Google Ads, Facebook/Meta Ads, and LinkedIn Ads utilise AI to optimise campaigns, automating processes like bid adjustments, audience targeting, and ad personalisation for improved performance. Here is how AI enhances each platform: 

Google Ads 

Smart Bidding – AI dynamically adjusts bids in real time to maximise campaign outcomes, such as conversions or conversion value. 

Responsive Ads – AI tests various combinations of headlines and descriptions to find the most effective ad variations for different audiences. 

Insights AI – analyses campaign data and provides optimisation recommendations, helping advertisers identify new opportunities and trends. 

Performance Max – AI optimises ad campaigns across all Google channels (YouTube, Display, Search, Discover, Gmail, and Maps) to achieve marketing objectives efficiently. 

Facebook/Meta Ads 

Ad Delivery Algorithm – AI determines which ads to show to specific users based on their behaviours and preferences, improving campaign performance. 

Advantage+ Campaigns – AI automates ad creation and optimisation, reducing manual effort while enhancing results. 

Lookalike Audiences – AI identifies users similar to existing customers, improving audience targeting. 

Meta Advantage – a suite of AI-powered tools that automate various aspects of ad creation and optimisation, such as creative selection and audience targeting. 

LinkedIn Ads 

Auto-bidding – AI dynamically adjusts ad bids to achieve the best results within a given budget. 

Dynamic Ads – AI personalises ad content based on user profile information, increasing engagement. 

Lead Scoring – AI evaluates lead quality based on behaviour and data, helping prioritise sales efforts. 

Audience Expansion – AI identifies additional users similar to the selected target audience, extending campaign reach. 

AI in User Experience analysis 

AI-powered tools such as Hotjar and Microsoft Clarity help analyse user behaviour and identify UX issues. 

Hotjar AI for Surveys 

• Automated question suggestions – Hotjar uses AI to generate surveys based on the information provided regarding the purpose of the survey. 

• Automated response summaries – Hotjar uses AI to automatically summarise survey responses, providing valuable insights without requiring manual analysis. 

• Sentiment analysis – AI categorises survey responses as positive, neutral, or negative, making it easier to understand user sentiment. 

Microsoft Clarity 

Behaviour pattern detection: 

Predictive Heatmaps – AI-driven heatmaps use machine learning to predict where users are most likely to click, scroll, or spend the most time on a page. 

Copilot Insights – Microsoft’s Copilot AI assists in data analysis and decision-making, streamlining analytical processes. 

AI in conversion rate optimisation 

Conversion rate optimisation (CRO) tools like Optimizely, VWO, and AB Tasty leverage AI to automate A/B testing, personalise content, and provide product recommendations. Below is an overview of AI-powered features in each platform. 

Optimizely 

Opal, the AI assistant built into Optimizely One, offers content generation, intelligent analytics, and automated recommendations: 

• Campaign idea generation – AI generates marketing campaign ideas based on brand guidelines and communication tone. 

• Automated content creation – Ensures AI-generated content aligns with branding, tone, and predefined instructions for consistency. 

• Audience segmentation suggestions – AI analyses user data to propose relevant audience segments for marketing campaigns. 

Contextual Bandits – these multi-armed bandit algorithms dynamically personalise user experiences based on real-time behavioural data and contextual attributes. 

AI-driven personalisation – Optimizely’s AI-powered personalisation enhances engagement and conversions by tailoring experiences to individual users. 

VWO 

Generative AI Ideation – AI analyses website data and user behaviour to suggest A/B test ideas that can improve conversion rates. 

AI-powered test result analysis – VWO applies AI to analyse A/B test results, identifying key factors that influence user behaviour and providing optimisation recommendations. 

Personalisation: 

• VWO Personalize – Enables personalised experiences for different user segments, enhancing engagement and conversions. 

• VWO Insights – Provides heatmaps and session recordings to support behaviour analysis for personalised optimisations. 

AB Tasty 

AI-powered product recommendations – AB Tasty’s AI-driven recommendation engine analyses user behaviour and preferences to suggest relevant products, increasing cart value and conversions. 

EmotionsAI – this AI-powered feature segments users based on their emotional needs, enabling the creation of more engaging and personalised experiences. 

AI-driven segmentation – AB Tasty leverages AI to automatically generate audience segments based on user behaviour and preferences, enabling precise campaign targeting. 

Adobe Analytics – AI-powered analytics 

Adobe Analytics, powered by Adobe Sensei AI, offers a broad range of AI-driven features for in-depth data analysis and marketing optimisation: 

• Anomaly detection – automatically identifies statistically significant deviations in key metrics, such as unexpected spikes or drops. 

• Intelligent alerts – real-time notifications about anomalies, enabling quick responses to critical changes. 

• Contribution analysis – identifies hidden data patterns and explains factors influencing anomalies or unexpected customer behaviours. 

• Audience clustering – uses machine learning to dynamically group users with similar behaviours for precise targeting and personalisation. 

• Propensity scoring – predicts the likelihood of users completing specific actions (e.g., making a purchase), helping refine marketing strategies. 

• Algorithmic attribution – machine learning analyses customer journeys to accurately attribute value to each touchpoint, optimising marketing spend. 

• Adobe Experience Platform integration – centralises data from multiple sources to provide a comprehensive customer journey overview. 

The benefits of AI in digital analytics 

Artificial intelligence is transforming data analysis by automating tedious processes, freeing analysts to focus on interpreting results and making informed decisions. It enhances trend forecasting and customer behaviour prediction, enabling proactive strategies. Automated reporting not only saves time but also reduces errors, while advanced AI algorithms facilitate precise audience segmentation and personalised customer experiences. Moreover, AI optimises marketing campaigns, boosting their effectiveness and maximising return on investment. 

The future of AI in digital analytics 

Artificial intelligence is already playing a pivotal role, and the future promises even greater transformations. What opportunities does AI unlock for digital analytics, and what challenges must we overcome? 

AI enables real-time analytics, allowing marketing campaigns to instantly adapt to shifting market dynamics. Hyperpersonalisation will become the norm, with AI systems precisely tailoring content, offers, and recommendations to individual user preferences. 

Automated decision-making will empower AI to optimize marketing campaigns autonomously, while advancements in machine learning will enhance analytical precision and uncover hidden patterns. However, these innovations also bring challenges—ensuring ethical AI use, data privacy, and security will be more critical than ever. 

Summary

As the volume of digital data continues to grow, AI is becoming an essential tool for marketers and analysts. AI-driven tools not only automate time-consuming tasks but also unlock deeper data insights, predict trends, and enable personalised customer experiences. 

Platforms like Google Analytics 4, Google Ads, and Meta Ads are already integrating AI-powered features. The future of digital analytics will be shaped by real-time analysis, hyper-personalisation, and AI-driven automation. 

Need support with AI-driven analytics? Explore our data analytics services—our experts can help integrate AI into your business, identify key optimisation areas, and develop AI strategies to achieve your goals. Reach out to us via the form below.

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