Fix Unclear Channel Attribution in Marketing Analytics with Gemini
When every customer sees dozens of touchpoints, last-click attribution breaks. This page shows how to use Gemini with GA4, BigQuery and your first-party data to build AI-driven attribution that finally reflects true channel contribution, so you can move budget with confidence.
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The Challenge: Unclear Channel Attribution
Marketing teams are under constant pressure to prove which channels actually drive revenue. But modern customer journeys run across search, social, display, email, marketplaces and offline touchpoints. When a buyer has 10+ interactions before converting, it becomes almost impossible to say which touchpoints really mattered using simple web analytics views. The result is unclear channel attribution, shaky ROI numbers and endless debates about where the next euro of budget should go.
Traditional approaches like last-click, first-click or static position-based models were built for a simpler web. They ignore the sequencing and synergy of touchpoints, treat every user path as if it were identical and cannot reconcile conflicting numbers from Google Ads, Meta, CRM and GA4. Even multi-touch rule-based models quickly become unmanageable as channels, campaigns and formats multiply. In a privacy-first world with partial tracking loss and walled gardens, these methods simply do not capture reality anymore.
The business impact is significant. Effective but early-funnel channels such as YouTube, display, content syndication or awareness campaigns are chronically underfunded, while retargeting and brand search appear artificially overperformant. Budget decisions become reactive and political instead of data-driven. Teams waste time arguing about whose numbers are "right" instead of optimising creative, audiences and offers. Over time, this leads to missed revenue, higher acquisition costs and a competitive disadvantage against organisations that truly understand their channel mix.
The good news: this problem is hard, but it is solvable. With modern cloud data stacks, GA4 exports and AI tools like Gemini, you can move beyond black-box platform reports and build attribution that reflects your unique business reality. At Reruption, we have hands-on experience stitching together fragmented data, validating AI models and turning them into usable tools for marketing teams. In the sections below, you'll find practical guidance on how to approach AI-driven attribution and how to use Gemini to bring clarity back into your marketing analytics.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From Reruption's perspective, using Gemini for marketing attribution is not about replacing your existing analytics stack, but about upgrading it. By connecting Gemini with GA4 exports, BigQuery and your first-party data, you can let the model do the heavy lifting: generating SQL, suggesting attribution and MMM model structures, reviewing Python code and checking data quality across sources. Our hands-on engineering work with AI products has shown that this human-in-the-loop setup is where AI-driven channel attribution creates real value without turning into another black box.
Start with a Clear Attribution Strategy, Not with the Model
Before you open Gemini or write a single line of SQL, align internally on what decisions your attribution model should support. Are you trying to rebalance spend between Google and Meta, defend upper-funnel investment, or understand the role of affiliates? Different questions call for different modelling approaches, lookback windows and granularity. Marketing leadership, performance marketers and data teams need a shared definition of "success" and acceptable uncertainty.
Use Gemini to help document and stress-test this strategy. You can describe your business model, sales cycle and channels, then ask Gemini to propose appropriate multi-touch attribution and marketing mix modelling (MMM) approaches, including trade-offs. This turns vague goals into a concrete blueprint that both marketers and analysts can work from.
Design a Human-in-the-Loop Workflow, Not Full Automation
Trying to fully automate attribution decisions with AI on day one is risky. Instead, design a workflow where Gemini supports analysts and marketers: generating queries, reviewing code, suggesting model variations and highlighting anomalies. Final judgement on model choice and budget shifts should remain with humans who understand the market context, seasonality and campaign goals.
This human-in-the-loop approach also builds trust. When teams see Gemini's reasoning, intermediate outputs and code suggestions, they are more likely to adopt insights. Reruption’s experience building AI tools shows that embedding explainability and review steps prevents the "black box" feeling that often kills advanced analytics projects.
Invest in Data Foundations Before Scaling AI Attribution
Gemini is only as good as the data it can see. If your GA4 implementation is inconsistent, UTM tagging is unreliable, or CRM data does not line up with online sessions, even the most sophisticated model will mislead you. Treat data quality and identity resolution as a strategic prerequisite, not a nice-to-have add-on.
Strategically, this means marketing leaders must prioritise a clean channel taxonomy, tracking standards and stable data pipelines from GA4 into BigQuery. Gemini can assist by generating data quality checks and reconciliation queries, but the organisation must commit to enforcing those standards across teams and agencies.
Balance Short-Term Attribution with Long-Term MMM
AI-driven attribution often focuses on user-level paths, but relying only on path-based models keeps you locked into what is trackable. With increasing privacy restrictions, you need to complement this with marketing mix modelling (MMM) that works on aggregated spend and outcome data. Strategically, think in terms of a dual system: path-based models to optimise in-channel tactics, MMM to calibrate your big budget moves.
Gemini is particularly useful here as a strategic assistant. It can propose MMM model specifications, comment on variable selection, and help your data team implement and iterate models on Vertex AI. This strategic combination gives you resilience against tracking loss and platform biases.
Prepare the Team for a Culture Shift in Decision-Making
Implementing AI-driven channel attribution is as much an organisational change project as it is a technical one. Performance marketers, brand teams, finance and leadership need to be ready to challenge long-held beliefs (for example, that retargeting is always the hero) and accept that uncertainty bands and confidence intervals are part of modern marketing analytics.
Use Gemini not only as a modelling tool but also as an explainer. Have it generate plain-language summaries, scenario comparisons and Q&A explanations that non-technical stakeholders can understand. This lowers resistance and helps embed data-informed, AI-augmented decision making into your marketing governance structures.
Used in the right way, Gemini turns unclear channel attribution into a manageable, testable problem instead of a constant source of conflict. By combining your GA4 and BigQuery data with Gemini’s ability to generate and review models, you can build attribution and MMM setups that marketing and finance both trust. Reruption brings the engineering depth and Co-Preneur mindset to help you move from concept to a working AI-driven attribution workflow inside your own stack; if you want to explore what this could look like for your team, we’re happy to validate it with a focused PoC and then scale what works.
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Real-World Case Studies
From Banking to Energy: Learn how companies successfully use Gemini.
Best Practices
Successful implementations follow proven patterns. Have a look at our tactical advice to get started.
Connect GA4, BigQuery and First-Party Data into a Single View
The first tactical step is to centralise all relevant touchpoint and conversion data. Enable GA4 BigQuery export (if not already active) and ensure that all key events (leads, sign-ups, purchases) are flowing into BigQuery with consistent parameters. In parallel, load your CRM or transaction data into BigQuery, including offline conversions and revenue indicators.
Use Gemini to draft and refine the SQL needed to join these tables. For example, you can ask Gemini to generate a user-level or session-level dataset that combines GA4 events with CRM conversions via hashed user IDs or other identifiers.
Prompt example for Gemini:
You are a senior analytics engineer.
We have GA4 export tables in BigQuery (dataset ga4_export) and a CRM
conversions table (dataset crm).
Write SQL to create a user-level table that:
- Links GA4 users to CRM conversions via user_pseudo_id & a hashed email
- Aggregates all channels (source/medium/campaign) seen in the 60 days
before the first conversion
- Outputs: user_id, conversion_date, revenue, list of channels in order
Return optimised Standard SQL for BigQuery.This consolidated view becomes the foundation for all subsequent AI-driven attribution or MMM work.
Use Gemini to Prototype and Compare Attribution Models Quickly
Instead of hard-coding one attribution model and hoping it fits, use Gemini to quickly prototype multiple approaches: time-decay, data-driven (Markov chain or Shapley-like), position-based, and blended models. Describe your constraints (data volume, lookback window, channels) and have Gemini generate Python code or SQL transformations to implement each candidate model.
Prompt example for Gemini:
We have a BigQuery table user_paths with columns:
user_id, conversion_flag, conversion_value, touchpoint_order,
channel, days_before_conversion.
Suggest 3 different multi-touch attribution methods suitable for
this data, and write Python (using pandas) to calculate channel-level
attributed revenue for each method.
Explain the pros/cons of each in comments.Run these variants on your historical data and compare stability, interpretability and alignment with business intuition. Gemini can also help you generate evaluation metrics and visualisations, then refine the winning model for production.
Automate Data Quality and Reconciliation Checks with Gemini-Generated SQL
Attribution fails quietly when data is inconsistent. Use Gemini to systematically create data quality checks and reconciliation queries between ad platforms, GA4 and your first-party conversions. For example, you can validate that total daily conversions from your attribution dataset are within an acceptable range of CRM numbers, or flag sudden drops in tracked touchpoints for specific channels.
Prompt example for Gemini:
Generate BigQuery SQL checks to validate our attribution base table
attribution_base:
- Compare daily total conversions to crm.conversions (tolerance +/- 5%)
- Detect days where a channel's share of impressions or clicks changes
by more than 40% vs. 7-day average
- Output a summary table with flags and severity levels.
Optimise for low cost and readability.Schedule these checks as part of your pipeline and alert the analytics team when anomalies appear. This makes your Gemini-powered attribution more robust and trustworthy over time.
Leverage Gemini and Vertex AI to Build a Lightweight MMM
To complement user-level attribution, implement a lightweight marketing mix model using aggregated spend and conversion data. Start by exporting daily (or weekly) channel spend, impressions and conversions into BigQuery. Then use Gemini to propose an MMM specification (for example, Bayesian regression with adstock and saturation) and generate the code to run it on Vertex AI or in a managed notebook.
Prompt example for Gemini:
We want to build a simple MMM on Google Cloud.
We have a BigQuery table mmm_data with daily rows and columns:
- date, conversions, revenue
- spend_search, spend_social, spend_display, spend_email
- control variables: seasonality_index, promo_flag
Propose a Bayesian MMM specification with adstock & saturation and
write Python code (using PyMC) we can run on Vertex AI Workbench.
Comment on how to interpret channel ROI and diminishing returns curves.Use the resulting channel ROI and diminishing returns curves to validate or adjust your attribution-based budgets. This dual setup (attribution + MMM) provides a more realistic view of true channel contribution, especially for upper-funnel and brand activity.
Generate Plain-Language Insights and Budget Recommendations
Numbers alone rarely change decisions. Once your models are producing attributed revenue and ROI per channel, use Gemini to transform raw outputs into clear, actionable summaries for marketers and leadership. Provide Gemini with aggregated results tables and ask it to generate concise narratives, charts suggestions and budget shift scenarios.
Prompt example for Gemini:
You are a marketing analytics advisor.
Here is a table with channel-level results from our attribution model
and MMM (pasted below).
1) Summarise the main insights in max 10 bullet points for a CMO.
2) Highlight 3-5 specific budget reallocation actions with rationale.
3) Call out any caveats or data limitations we should mention.
Use clear, non-technical language.Embed these narratives into dashboards or regular performance reviews so that AI-driven attribution informs real budget decisions instead of staying in experimental reports.
Institutionalise Versioning and Governance for Attribution Models
As you iterate with Gemini, you will create many variations of models and configurations. Without governance, teams lose track of what is live, what changed and why. Implement a simple but strict versioning approach: store model code in Git, tag each production deployment, and document the assumptions, input data and validation results. Gemini can help you generate and maintain this documentation.
Prompt example for Gemini:
We just updated our attribution model.
Here is a short description of the changes and the validation results.
Draft a change log entry and a 1-page internal documentation including:
- Model version and date
- Key assumptions and parameters
- Data sources and lookback window
- Validation metrics vs. previous version
- Guidance on how to interpret differences in channel ROI.Over time, this governance layer turns your Gemini-based attribution setup into a reliable internal asset rather than a fragile experiment.
When implemented this way, organisations typically see clearer channel ROI within one or two optimisation cycles, more confident budget reallocations, and a meaningful reduction in time spent arguing about attribution definitions. It is realistic to target a 10–20% improvement in marketing efficiency over several quarters as AI-driven attribution and MMM insights are steadily integrated into planning and optimisation routines.
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Frequently Asked Questions
GA4 provides useful attribution views, but it is limited to predefined models and what is trackable inside Google's ecosystem. Gemini improves channel attribution by helping you build custom models on top of your GA4 BigQuery export and first-party data. It generates SQL and Python to combine user paths, CRM conversions and platform spend, then prototypes multi-touch attribution and MMM tailored to your business.
This means you can reconcile conflicting platform-reported conversions with your own data, test different modelling assumptions, and arrive at a channel performance view that reflects your actual customer journeys rather than generic defaults.
You will get the most value from Gemini when you combine it with a basic modern data stack and some data expertise. Practically, you need: access to GA4 BigQuery exports, a place to store CRM/transaction data (often also in BigQuery), and someone with enough analytics or engineering experience to review and run the SQL/Python that Gemini generates.
The advantage is that Gemini significantly reduces the manual coding burden. A small team of a marketing analyst and a data engineer can achieve what previously required a dedicated data science team. Reruption often supports clients by providing the missing engineering depth and setting up reusable templates so internal teams can operate the solution day to day.
Timelines depend on data readiness, but for most organisations with GA4 and basic CRM data in place, you can get to a first working attribution prototype in a few weeks. In our AI PoC format, we typically aim to connect data, define evaluation metrics, and ship a tested prototype model within the scope of a single engagement.
Meaningful business impact on budget decisions usually appears after one or two optimisation cycles, once the team has validated the model against their intuition and seen that recommendations hold up in the real world. From there, Gemini helps you iterate and refine models quickly as campaigns, channels and market conditions change.
The direct technology costs of using Gemini with BigQuery and GA4 are typically modest compared to media budgets: you pay for BigQuery storage/queries, Vertex AI or compute for model runs, and Gemini usage. The larger investment is in setting up the data pipelines, models and governance correctly.
ROI should be evaluated against marketing efficiency: are you reallocating budget from over-credited channels to under-valued ones and seeing lower blended CAC or higher incremental revenue? Even a 5–10% improvement in budget allocation on a multi-million marketing spend far outweighs the cost of building and running the AI-driven attribution setup.
Reruption accelerates this journey by combining strategic clarity with deep engineering execution. Through our AI PoC offering (9.900€), we can quickly test whether a Gemini-based attribution or MMM setup works with your actual GA4 and first-party data: we define the use case, build a functioning prototype, measure performance and outline a production plan.
Beyond the PoC, our Co-Preneur approach means we embed with your team, operate in your P&L and help build the AI-first marketing analytics capabilities directly inside your organisation. We handle the Gemini prompts, BigQuery/Vertex AI pipelines, and governance structures while enabling your marketing and data teams to own and extend the solution over time.
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