Contact with us<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n\nAI 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.<\/strong> <\/p>\n\n\n\nIn the constantly developing digital marketing industry, adapting to new technologies is key. Companies that fail to leverage AI risk falling behind their competitors<\/strong>. That is why investing in AI-driven skills and implementing secure AI-powered solutions today is crucial for staying ahead. <\/p>\n\n\n\n<\/span>Automation vs. AI: where is the line? <\/span><\/h2>\n\n\n\nIn 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. <\/p>\n\n\n\n
<\/span>Automation <\/span><\/h3>\n\n\n\nAutomation 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.<\/strong> <\/p>\n\n\n\n<\/span>Artificial intelligence <\/span><\/h3>\n\n\n\nAI, 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.<\/strong> <\/p>\n\n\n\n<\/span>Where is the boundary between automation and AI? <\/span><\/h3>\n\n\n\nThe key difference lies in AI\u2019s 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.<\/p>\n\n\n\n
<\/span>AI tools in digital analytics <\/span><\/h2>\n\n\n\nAI is making waves in digital data analysis, and its tools are becoming increasingly advanced. Let\u2019s take a closer look at some of the key solutions available on the market. <\/p>\n\n\n\n
<\/span>Google Analytics 4 (GA4) \u2013 AI-powered insights and predictions <\/span><\/h3>\n\n\n\nGA4 is a leader in AI-driven analytics, offering intelligent features such as predictive metrics, smart alerts, and data-driven attribution. <\/p>\n\n\n\n
Predictive metrics <\/h4>\n\n\n\n\n- Churn probability<\/strong> \u2013 estimates the likelihood that an active user from the past 7 days will not return in the next 7 days. <\/li>\n<\/ul>\n\n\n\n
\n- Purchase probability<\/strong> \u2013 predicts the likelihood of a user (active in the last 28 days) making a purchase in the next 7 days. <\/li>\n<\/ul>\n\n\n\n
\n- Predicted revenue<\/strong> \u2013 forecasts expected revenue from purchases within the next 28 days, based on the activity of users in the last 28 days. <\/li>\n<\/ul>\n\n\n\n
These metrics allow for the creation of predictive audiences, which identify users with a high probability of taking\u2014or not taking\u2014specific actions in the future<\/strong>. This helps businesses detect customers at risk of churning and target them with personalised offers. <\/p>\n\n\n\nAnalytics insights <\/h4>\n\n\n\n
GA4\u2019s 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. <\/p>\n\n\n\n
Data-driven attribution <\/h4>\n\n\n\n
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\u2019s journey. <\/p>\n\n\n\n
<\/span>Google Tag Manager (GTM) \u2013 powering AI with data<\/strong> <\/span><\/h3>\n\n\n\nAlthough 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. <\/p>\n\n\n\n
By centralising tag management and defining trigger rules, GTM ensures data consistency and accuracy, which is essential for AI models to function effectively.<\/strong> <\/p>\n\n\n\nAdditionally, server-side tagging in GTM offers: <\/strong><\/p>\n\n\n\n\n- Enhanced data control and enrichment \u2013 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. <\/li>\n<\/ul>\n\n\n\n
\n- More accurate attribution modeling \u2013 centralised data processing allows for more precise tracking of user journeys, leading to improved attribution and ROI measurement. <\/li>\n<\/ul>\n\n\n\n
\n- Filling data gaps caused by cookie restrictions \u2013 server-side tagging enables setting cookies from the server, helping mitigate tracking restrictions imposed by browsers or user settings. <\/li>\n<\/ul>\n\n\n\n
<\/span>Piwik PRO \u2013 privacy and AI<\/strong> <\/span><\/h3>\n\n\n\nPiwik 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: <\/p>\n\n\n\n
\n- predictive analytics<\/strong> \u2013 forecasting user behaviour based on historical data, <\/li>\n<\/ul>\n\n\n\n
\n- machine learning<\/strong> \u2013 automatically detecting patterns and trends, <\/li>\n<\/ul>\n\n\n\n
\n- advanced AI algorithms<\/strong> \u2013 providing deeper insights into user journeys and interactions with the website. <\/li>\n<\/ul>\n\n\n\n
Additionally, Piwik PRO integrates with A\/B testing and user experience personalisation tools, such as: <\/p>\n\n\n\n
\n- AB Tasty <\/strong>\u2013 allows direct access to A\/B test results within Piwik PRO, <\/li>\n<\/ul>\n\n\n\n
\n- Optimizely<\/strong> \u2013 facilitates easy comparison of A\/B test results, helping to optimise conversions. <\/li>\n<\/ul>\n\n\n\n
Both integrations rely on a data layer, ensuring precise analysis of test results and better adaptation of marketing strategies. <\/p>\n\n\n\n
<\/span>Looker Studio \u2013 visualisation and Cloud integration<\/strong> <\/span><\/h3>\n\n\n\nPreviously known as Google Data Studio, Looker Studio is a powerful data visualisation tool that integrates seamlessly with the Google Cloud ecosystem, enabling: <\/p>\n\n\n\n
\n- Connecting predictive models from BigQuery ML \u2013 users can create, train, and deploy machine learning models directly in BigQuery, allowing visualisation of predictive model results in Looker Studio. <\/li>\n<\/ul>\n\n\n\n
\n- Visualisation and monitoring of machine learning predictions \u2013 enables interactive dashboards and reports that showcase predictive models’ outputs, such as time series forecasts and classifications. <\/li>\n<\/ul>\n\n\n\n
\n- Using Gemini for SQL query generation on public and private datasets \u2013 allows users to ask natural language questions about their data, with the system automatically generating corresponding SQL queries in BigQuery, simplifying data analysis. <\/li>\n<\/ul>\n\n\n\n
<\/span>Microsoft Excel \u2013 AI for everyone <\/span><\/h3>\n\n\n\nMicrosoft Excel, one of the most widely used data analysis tools, also leverages artificial intelligence through features such as: <\/p>\n\n\n\n
\n- Analyze Data<\/strong> \u2013 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. <\/li>\n<\/ul>\n\n\n\n
\n- Forecast Sheet<\/strong> \u2013 predicts future values based on historical time series data, creating a dedicated sheet with past and projected values visualised as line or column charts. <\/li>\n<\/ul>\n\n\n\n
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. <\/p>\n\n\n\n
<\/span>Microsoft Power BI \u2013 advanced business analytics<\/strong> <\/span><\/h3>\n\n\n\nPower BI is a business intelligence platform that incorporates AI-powered services, offering: <\/p>\n\n\n\n
AI-driven visualisations<\/strong> <\/h4>\n\n\n\n\n- Key Influencers<\/strong> \u2013 analyses factors that have the most significant impact on a given metric, helping businesses make informed decisions. <\/li>\n<\/ul>\n\n\n\n
\n- Decomposition Tree<\/strong> \u2013 breaks down metrics into hierarchical categories for a better understanding of data. <\/li>\n<\/ul>\n\n\n\n
Natural language Q&A<\/strong> <\/h4>\n\n\n\nThis feature allows users to ask natural language questions about their data, with Power BI automatically generating relevant visualisations, eliminating the need for complex queries. <\/p>\n\n\n\n
Built-in forecasting and anomaly detection<\/strong> <\/h4>\n\n\n\n\n- Forecasting<\/strong> \u2013 predicts future values based on historical data, aiding in strategic planning. <\/li>\n<\/ul>\n\n\n\n
\n- Anomaly detection<\/strong> \u2013 automatically identifies unusual patterns or deviations in data, enabling quick responses to potential issues or opportunities. <\/li>\n<\/ul>\n\n\n\n
Integration with Azure AI services<\/strong> <\/h4>\n\n\n\nPower 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. <\/p>\n\n\n\n
<\/span>Tableau \u2013 data exploration and explanation<\/strong> <\/span><\/h3>\n\n\n\nTableau is a leading data visualisation and analytics tool that offers: <\/p>\n\n\n\n
Ask Data \u2013 natural language queries<\/strong> <\/h4>\n\n\n\nPreviously, 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). <\/p>\n\n\n\n
Explain Data \u2013 automated insights<\/strong> <\/h4>\n\n\n\nThis feature enables users to discover the reasons behind specific data points by dynamically generating visual explanations, facilitating deeper data exploration. <\/p>\n\n\n\n
Integration with Einstein AI \u2013 Einstein Discovery<\/strong> <\/h4>\n\n\n\nEinstein Discovery in Tableau provides AI-powered predictions and recommendations without requiring programming expertise, delivering transparent and reliable insights to all users. <\/p>\n\n\n\n
Tableau Pulse<\/strong> <\/h4>\n\n\n\nA 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. <\/p>\n\n\n\n
Tableau GPT\/Agent<\/strong> <\/h4>\n\n\n\nA 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. <\/p>\n\n\n\n
<\/span>Advertising platforms \u2013 intelligent campaign optimisation<\/strong> <\/span><\/h3>\n\n\n\nAdvertising 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: <\/p>\n\n\n\n
Google Ads <\/strong><\/h4>\n\n\n\nSmart Bidding<\/strong> \u2013 AI dynamically adjusts bids in real time to maximise campaign outcomes, such as conversions or conversion value. <\/p>\n\n\n\nResponsive Ads<\/strong> \u2013 AI tests various combinations of headlines and descriptions to find the most effective ad variations for different audiences. <\/p>\n\n\n\nInsights AI<\/strong> \u2013 analyses campaign data and provides optimisation recommendations, helping advertisers identify new opportunities and trends. <\/p>\n\n\n\nPerformance Max<\/strong> \u2013 AI optimises ad campaigns across all Google channels (YouTube, Display, Search, Discover, Gmail, and Maps) to achieve marketing objectives efficiently. <\/p>\n\n\n\nFacebook\/Meta Ads <\/strong><\/h4>\n\n\n\nAd Delivery Algorithm<\/strong> \u2013 AI determines which ads to show to specific users based on their behaviours and preferences, improving campaign performance. <\/p>\n\n\n\nAdvantage+ Campaigns<\/strong> \u2013 AI automates ad creation and optimisation, reducing manual effort while enhancing results. <\/p>\n\n\n\nLookalike Audiences<\/strong> \u2013 AI identifies users similar to existing customers, improving audience targeting. <\/p>\n\n\n\nMeta Advantage<\/strong> \u2013 a suite of AI-powered tools that automate various aspects of ad creation and optimisation, such as creative selection and audience targeting. <\/p>\n\n\n\nLinkedIn Ads <\/strong><\/h4>\n\n\n\nAuto-bidding<\/strong> \u2013 AI dynamically adjusts ad bids to achieve the best results within a given budget. <\/p>\n\n\n\nDynamic Ads<\/strong> \u2013 AI personalises ad content based on user profile information, increasing engagement. <\/p>\n\n\n\nLead Scoring<\/strong> \u2013 AI evaluates lead quality based on behaviour and data, helping prioritise sales efforts. <\/p>\n\n\n\nAudience Expansion<\/strong> \u2013 AI identifies additional users similar to the selected target audience, extending campaign reach. <\/p>\n\n\n\n<\/span>AI in User Experience analysis<\/strong> <\/span><\/h3>\n\n\n\nAI-powered tools such as Hotjar and Microsoft Clarity help analyse user behaviour and identify UX issues. <\/p>\n\n\n\n
Hotjar AI for Surveys<\/strong> <\/h4>\n\n\n\n\n- Automated question suggestions<\/strong> \u2013 Hotjar uses AI to generate surveys based on the information provided regarding the purpose of the survey. <\/li>\n<\/ul>\n\n\n\n
\n- Automated response summaries<\/strong> \u2013 Hotjar uses AI to automatically summarise survey responses, providing valuable insights without requiring manual analysis. <\/li>\n<\/ul>\n\n\n\n
\n- Sentiment analysis<\/strong> \u2013 AI categorises survey responses as positive, neutral, or negative, making it easier to understand user sentiment. <\/li>\n<\/ul>\n\n\n\n
Microsoft Clarity<\/strong> <\/h4>\n\n\n\nBehaviour pattern detection: <\/p>\n\n\n\n
\n
\n- Rage clicks<\/strong> \u2013 identifies instances where users repeatedly click on the same area, indicating frustration with unresponsive elements. <\/li>\n\n\n\n
- Dead clicks<\/strong> \u2013 detects clicks that result in no action, suggesting that users are trying to interact with non-clickable elements. <\/li>\n\n\n\n
- Excessive scrolling<\/strong> \u2013 tracks sessions with an unusually high amount of scrolling, which may indicate content layout issues.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n
Predictive Heatmaps \u2013 AI-driven heatmaps use machine learning to predict where users are most likely to click, scroll, or spend the most time on a page. <\/p>\n\n\n\n
Copilot Insights \u2013 Microsoft’s Copilot AI assists in data analysis and decision-making, streamlining analytical processes. <\/p>\n\n\n\n
<\/span>AI in conversion rate optimisation<\/strong> <\/span><\/h3>\n\n\n\nConversion 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. <\/p>\n\n\n\n
Optimizely <\/strong><\/h4>\n\n\n\nOpal, the AI assistant built into Optimizely One, offers content generation, intelligent analytics, and automated recommendations: <\/p>\n\n\n\n
\n- Campaign idea generation<\/strong> \u2013 AI generates marketing campaign ideas based on brand guidelines and communication tone. <\/li>\n<\/ul>\n\n\n\n