In our day-to-day work with clients at DDigitals, working in digital analytics used to mean opening dashboards, comparing time periods, cross-referencing dimensions, creating segments and spending a fair amount of time trying to work out what was going on behind a rise or a fall.
All of that is still necessary. But there is one important difference: we can now do it much more quickly.
The combination of AI and digital analytics It is changing the way in which companies, agencies and marketing teams work with their data. It is no longer simply a matter of visualising information, but of using models capable of detecting anomalies, identifying patterns, summarising large volumes of data or answering questions in natural language.
In other words: moving on from looking at dashboards to talking to the data.
And this, when applied correctly, can significantly reduce the time between identifying a problem and making a decision.
AI in data analysis: a paradigm shift
Traditional analytics has historically relied on a preliminary question.
You want to find out which channel generates the most conversions. You create a report.
You want to know why organic traffic has dropped. You compare time periods, analyse landing pages and segment the data.
You want to check whether a paid media campaign is becoming less effective. You look at CPM, CTR, CPC, conversions and ROAS.
The change introduced by the artificial intelligence in data analysis The point is that part of this process can be reversed.
The tool may first find something relevant and then ask you to look into it.
For example:
- Identify that the mobile conversion rate has fallen abnormally.
- Identify that the drop is concentrated amongst users of a specific browser.
- Discovering an unexpected increase in cart abandonment during the checkout process.
- Identify a campaign whose CPA is beginning to deviate from its usual pattern.
- Link a fall in revenue to a specific product category.
Google Analytics already uses machine learning to identify unusual changes and emerging trends through Analytics Intelligence. Its anomaly detection system compares observed results with the values that would be expected based on historical data.
Analytics is thus moving away from being purely descriptive to gradually becoming diagnostic, predictive and conversational.
What is AI Data Analytics?
AI Data Analytics, or data analysis using artificial intelligence, involves applying techniques such as machine learning, statistical models, natural language processing and generative AI to analyse datasets and extract relevant information.
Its aim is not simply to produce more graphics.
It’s about making data easier to access, interpret and turn into action.
A company can use these technologies to work with information sourced from:
- Google Analytics;
- CRM;
- Google Ads or Meta Ads campaigns;
- e-commerce platforms;
- financial systems;
- customer service tools;
- surveys;
- applications;
- social media;
- internal databases.
The larger the data volumes, the greater the benefit of automating part of the analysis.
Because the main problem facing many organisations is no longer a lack of information.
It’s having too much information and far too little time to make sense of it.
The difference between traditional data analysis and AI-powered data analysis
Traditional analysis usually starts with pre-configured dashboards, queries, filters and visualisations.
AI adds an extra layer.
You can write:
“Why have conversions fallen this week compared with the previous four weeks?”
And the system can begin to explore related dimensions, segments or metrics to find possible explanations.
You don’t necessarily need to be familiar with the tool’s entire architecture or be proficient in SQL to carry out an initial exploration.
This does not mean that analysts will disappear.
Quite the opposite.
We automate some of the routine tasks so that we can devote more time to the work that really adds value: analysing, prioritising and making decisions.
How does AI-powered data analysis work?
Although each platform uses different technologies, we can break the process down into four main steps.
1. Data collection and preparation
First, we need data.
And here comes one of the most important rules in this whole article:
An AI that processes poor-quality data will still produce poor-quality analyses. It just does so more quickly.
The metrics must be set up correctly, the events must make sense, the nomenclatures must be consistent and the sources must be integrated.
2. Identifying patterns
The models analyse historical data and look for recurring patterns.
For example, they may learn that the number of conversions each Sunday is lower than on Friday.
That means a 15 % fall on a Sunday might not be an anomaly.
A 60% fall in % could well be the case.
3. Anomaly detection and relationships
The algorithms compare what is actually happening with what would be statistically expected.
Some platforms go even further.
Amplitude, for example, offers Root Cause Analysis tools that automatically search for properties and segments capable of explaining an outlier on a graph.
4. Explanation in natural language
This is where generative AI comes into play.
Instead of simply presenting:
Conversion rate: -17.4 %.
It can make it much easier to understand:
“The conversion rate has fallen, mainly due to a drop in mobile users coming from paid social.”
And that completely changes the accessibility of the analytics.
What does AI bring to digital analytics?
Make a note of these four benefits.
Detect earlier
A traditional dashboard waits for someone to come and look at it.
An automatic detection system can alert you.
This makes it possible to identify issues relating to tracking, campaigns, e-commerce or products before they appear in the monthly report.
Conduct research more quickly
Finding the cause of an anomaly usually requires us to test different hypotheses:
- Channel.
- Device.
- Landing.
- Campaign.
- Country.
- Product.
- Browser.
- New user versus returning user.
AI can greatly speed up that initial exploration.
Democratising data
Not all business managers know how to build a query in GA4 or write SQL.
But anyone might ask:
“Which categories have seen an increase in sales this quarter?”
Conversational interfaces enable marketing, management and product teams to access information that previously relied much more heavily on the data team.
Improving decision-making
The real aim is not to gain more insights.
It’s about making better, data-driven decisions.
AI reduces friction between the appearance of a signal and its interpretation.
And the sooner we understand what is happening, the sooner we can act.
Generative AI vs Predictive AI in data analysis
It is worth distinguishing between them because they do not do exactly the same thing.
Predictive AI
It uses historical data to estimate what might happen.
It can be used for:
- predict churn.
- to uphold the claim.
- detect fraud.
- calculate purchase propensity.
- to forecast revenue.
- detect anomalies.
Generative AI
It focuses in particular on interpreting, summarising, retrieving or generating information.
You can:
- answer questions about our data;
- summarise a dashboard;
- to explain an anomaly;
- generate enquiries;
- propose segments;
- draft an executive summary.
Both technologies complement each other, but in DDigitals We apply a clear principle: AI predicts and explains, but the human team validates. A generative model may put forward a hypothesis as to why traffic is falling, but only the analyst’s experience can ensure that this interpretation is not a delusion or a false positive before a decision is taken that affects the business.
Case studies on AI in digital analytics
This is where things get interesting.
E-commerce: unexpected drop in conversion rates
Imagine that sales fall by 25 % on a Tuesday.
Instead of manually reviewing dozens of reports, the system identifies that:
- traffic remains steady;
- The ‘Add to basket’ button hardly changes;
- the checkout process that has already started continues as normal;
- the reduction is concentrated in the payment;
- It particularly affects mobile devices.
We’ve got a lead.
There may be a problem with a payment gateway.
What used to take hours of research it’s starting to narrow down to a matter of minutes.
Marketing: identifying a campaign that is losing momentum
A campaign maintains the volume of conversions, but its cost gradually increases.
The system can detect that the CPA is moving away from its expected range before the deterioration becomes apparent in the monthly total.
This makes it possible to review target audiences, creative concepts or budget allocation much earlier on.
CRO: identifying friction points
Let’s suppose that visitors to a landing page continue to arrive, but the percentage reaching the form is falling.
AI can help us analyse behaviour and determine that the crash only occurs at a specific screen resolution.
We have a new hypothesis:
Perhaps we don’t have a marketing problem. We have a UX problem.
SEO: detecting unusual changes
We can use automated systems to identify deviations in:
- organic traffic;
- clicks;
- prints;
- CTR;
- conversions;
- performance of groups of URLs.
The key is to distinguish between normal fluctuations and changes that really do warrant our attention.
Customer Experience
We can also analyse reviews, surveys, support tickets or customer service conversations.
Using natural language processing, we can group topics, identify patterns and detect possible causes of a drop in the customer satisfaction.
Suddenly, thousands of open replies are no longer information that is virtually impossible to review manually.
Applications of AI-powered data analysis across various sectors
This isn’t limited to marketing.
In the retail sector, it enables demand forecasting and helps to understand purchasing patterns.
In banking, it can help to detect anomalous transactions.
In SaaS, it enables the analysis of activation, engagement, churn and feature usage.
In industry, it helps to detect faults and plan maintenance in advance.
In customer service, it enables conversations to be categorised and sentiment to be measured.
And in e-commerce, it helps to link behaviour, customer acquisition, products and turnover.
The common thread is always the same: transforming large datasets into actionable insights.
Benefits of AI-powered analytics
When implementation is carried out effectively, there are clear benefits:
- speed: less time spent looking for information;
- automation: fewer repetitive analyses;
- scalability: the ability to analyse larger volumes of data;
- early detection: anomalies before they turn into bigger problems;
- accessibility: queries using natural language;
- depth: automatic exploration of segments and relationships;
- productivity: analysts spend more time on strategy;
- better data-driven decisions: the information reaches those who need to take action sooner.
But beware.
None of these benefits eliminates the need for a measurement strategy.
5 AI tools for digital analytics
AI is already integrated into some of the most widely used analytics platforms.
1. Google Analytics 4
Analytics Intelligence automatically identifies trends and unusual changes and allows you to set up customised insights. Google has also introduced AI Overviews, which can summarise relevant changes detected in the reports in plain language.
For digital marketing projects, it is probably one of the most accessible ways to start using AI on existing data.
2. Amplitude
Particularly powerful for product and behavioural analytics.
Amplitude offers alert systems, root cause analysis and AI capabilities to investigate data, create charts or perform analyses using agents.
In 2026, the company also launched its suite featuring more than 25 specific skills for agents.
3. Adobe Analytics
Adobe has been working on anomaly detection and contribution analysis for some time.
Your system can set expected values and upper and lower limits based on historical data, making it easier to identify significant deviations from statistical noise.
Particularly useful for business environments handling large volumes of data.
4. Mixpanel Spark AI
Spark allows you to query the data directly using natural language and continue your investigation by asking follow-up questions.
The idea is simple: chat with your data.
Users can turn business questions into analyses without needing to know all the events in advance or build each visualisation manually.
5. Looker + Gemini
Google has also made Looker a much more conversational platform.
Conversational Analytics allows you to ask questions in natural language about data governed by Looker’s semantic layer.
And the next step is already taking shape: agents who don’t wait for you to ask.
In July 2026, Google unveiled a preview of Looker’s Agentic Workflows, which are capable of automating the monitoring of metrics and root cause analysis based on conversational instructions.
That is probably one of the major trends we will see unfold over the coming years.
Limitations and precautions
So far, it all sounds fantastic.
Next up is the Stop posturing.
AI does not automatically make an organisation data-driven.
A correlation is not a cause
The fact that a tool identifies a drop as coinciding with a particular segment does not necessarily prove that that segment is the cause.
We need some context.
He might make a mistake
Generative models produce plausible responses that are not always correct.
Google’s own documentation recommends verifying the results generated by Gemini in Looker before using them.
It depends on the quality of the data
If we measure events incorrectly, duplicate conversions or have inconsistent UTM parameters, the AI will work with that flawed data.
Rubbish in, rubbish out.
With AI included.
The business context remains human. A tool may be able to identify that sales have fallen, but it does not know that a promotion ended yesterday. This happened to us recently with an e-commerce client: The system flagged a sharp drop in the conversion rate, when in fact it was due to a change to the checkout design that had not been communicated to the data team.
AI-powered analytics and ethical considerations
Introducing artificial intelligence into analytical processes also raises issues of privacy and governance.
We must monitor what information we send to each model. At European level, it is mandatory to verify compliance with the The GDPR and the Artificial Intelligence Act (EU AI Act). At DDigitals We do not send personally identifiable information (PII) to public generative AI APIs without first anonymising it.
Particular care should be taken with data relating to users, employees, financial information or customer data.
Not everything that we are technically able to analyse should be fed into just any AI tool.
Data governance is no longer purely a technical issue; it has become a strategic one.
How to start integrating AI into your workflow
Our advice is simple: Don’t start by trying to automate everything.
Start with a case where there is a clear profit.
Step 1. Check your measurement
Before we talk about AI, have a look at:
- events.
- conversions.
- UTMs.
- consents.
- nomenclature.
- sources.
- integrations.
Step 2. Define your KPIs
You don’t need to monitor 200 metrics.
You need to know which ones could transform the business:
- Revenue.
- Leads.
- Conversion.
- CPA.
- ROAS.
- Withholding.
- LTV.
Step 3. Automate anomalies
Set up alerts for critical metrics.
Don’t wait for the monthly report to find out that something broke three weeks ago.
Step 4. Incorporate natural language queries
Use the AI tools to speed up initial investigations.
Question.
Follow-up question.
Segment.
Look for explanations.
Step 5. Validate the findings
The AI suggests.
The analyst confirms this.
Step 6. Turn insights into action
This is the step that far too many companies overlook.
An insight that does not lead to any decision is simply a curiosity.
True analytics begins when someone changes a campaign, improves a landing page, modifies a product or resolves an issue based on what they have discovered.
«AI in analytics is not here to replace the analyst, but to prevent the analyst from spending 70% of their time searching for data.» Álvaro Vázquez, CEO of DDigitals.
The future of analytics is not just about dashboards
For a long time, we have confused having dashboards with having a data-driven culture.
It’s not the same.
You could have twenty flawless dashboards and still make decisions based on gut feeling.
AI can help bridge that gap because it turns data into something more accessible, more proactive and much quicker to explore.
The dashboard tells you what has happened.
The next generation of tools will alert you when something has changed, investigate the possible causes and suggest where to look.
And that is where the real change lies.
The analyst no longer spends a huge amount of time looking for the signal, so they can devote more time to deciding what to do with it.
At DDigitals, we believe that the opportunity does not lie in replacing traditional analytics with our friends, the LLMs.
It involves building a smart layer on top of a robust measurement strategy.
Because first of all, you need good data.
Next, you need to understand them.
And finally, you need to turn them into a business.
Yes If you want to improve your digital ecosystem, analytics and marketing strategy, you can find out more How we work at DDigitals.
Contact us Working with our team is the first step towards improving your business analytics.
Frequently asked questions about AI in digital analytics
What is AI applied to digital analytics?
It involves the use of artificial intelligence, machine learning and natural language processing technologies to automate or speed up tasks such as data analysis, such as anomaly detection, pattern identification, behaviour prediction or the generation of explanations.
Can AI replace a digital analyst?
It shouldn’t be framed that way. AI can automate repetitive tasks and speed up research, but it is still necessary to interpret the results, understand the business context and validate hypotheses before making decisions.
Do I need to know how to code to use AI in analytics?
Less and less. Platforms such as GA4, Amplitude, Mixpanel, Looker and Power BI are introducing interfaces that allow users to run queries using natural language.
What is the main advantage of using AI in data analysis?
Speed. It enables you to work with large volumes of data, identify patterns and detect anomalies much sooner than would be possible through a fully manual review.
What is anomaly detection?
It involves identifying behaviour that deviates from what would be expected based on historical data. It can be used to detect anything from an unexpected drop in conversions to a technical issue, a change in behaviour or a business opportunity.
Does AI always provide accurate insights?
No. The results need to be reviewed. Models may misinterpret a question, work with incomplete information or identify relationships that do not imply causality.
How should a company go about using AI in analytics?
Firstly, by ensuring effective data collection. Then, by defining key KPIs and using AI in specific processes, such as anomaly monitoring, periodic analysis, segmentation or hypothesis generation.
AI applied to digital analytics is not about letting a machine make all the decisions. It is about ensuring that the people making those decisions can access the information they need more quickly.
And that really could completely change the way we work with data.
