Unify Fragmented Campaign Data with ChatGPT-Powered Analytics
Marketing teams are drowning in fragmented campaign data from ad platforms, email tools, and web analytics – and still can’t see what actually drives revenue. This page shows how to use ChatGPT on top of your data warehouse to reconcile metrics, automate analysis, and turn scattered reports into one clear, actionable view of performance.
Inhalt
The Challenge: Fragmented Campaign Data
Most marketing teams run campaigns across a growing stack of tools: Meta Ads, Google Ads, LinkedIn, programmatic platforms, marketing automation, CRM, and web analytics. Each system measures impressions, clicks, conversions, and revenue slightly differently, using its own naming conventions and attribution logic. The result is fragmented campaign data that makes it almost impossible to answer a simple question: which activities actually generate pipeline and revenue?
Traditional approaches try to solve this with more spreadsheets and manual reporting. Analysts export CSVs from every platform, attempt to align dates and channels, and build complex Excel workbooks or Looker/Power BI dashboards. But with evolving channel setups, tracking changes, and privacy constraints, these static integrations break constantly. Manual reconciliation can’t keep up with the speed of experimentation, and BI teams are overloaded with ad-hoc queries instead of enabling proactive marketing analytics.
The business impact is significant. Budget decisions are made on incomplete or conflicting numbers, so spend drifts toward the loudest channel owner rather than the best-performing campaign. Campaign optimization gets delayed because insights arrive weeks after the fact. Teams waste hours debating which numbers are correct instead of testing new creative or audiences. Over time, this erodes confidence in data, reduces marketing ROI, and leaves your organisation slower and less precise than competitors who can see performance clearly in near real time.
The good news: while fragmented campaign data is a real and painful challenge, it is solvable. With a modern data foundation and the right use of AI assistants like ChatGPT on top of your warehouse, you can reconcile metrics, standardise definitions, and generate unified insights without adding more manual work. At Reruption, we’ve helped teams move from scattered reports to decision-ready analytics in weeks, not years. In the sections below, you’ll find practical guidance on how to approach this and what it takes to make ChatGPT a reliable partner in your marketing analytics workflow.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From our hands-on work building AI-first analytics workflows inside organisations, we’ve seen that the real bottleneck in marketing analytics is not a lack of data, but the ability to interpret it quickly and consistently. Used correctly, ChatGPT on top of your marketing data warehouse can act as a flexible analytics layer: generating SQL, reconciling definitions, explaining discrepancies, and producing narrative insights that non-technical marketers can trust. But unlocking this value requires the right strategic setup, not just connecting an API and hoping for the best.
Clarify Your Single Source of Truth Before You Add AI
Before involving ChatGPT, you need clarity on where your single source of truth for marketing performance lives. For most organisations, that should be a central data warehouse (e.g. BigQuery, Snowflake, Redshift) where raw platform data is collected and transformed. If your numbers differ between the warehouse, CRM, and platform dashboards, AI will only amplify the confusion.
Work with marketing, sales, and finance to define authoritative metrics: What counts as a qualified lead? Which conversion events matter? Is revenue taken from CRM or from the ecommerce platform? Once those decisions are documented and reflected in your data models, ChatGPT can safely build on top of them. Without this foundation, no AI tool will provide consistent answers to basic performance questions.
Treat ChatGPT as an Analytics Copilot, Not a Replacement
Strategically, your goal is to turn ChatGPT into an analytics copilot for marketers, not an autonomous decision-maker. It should help non-technical users ask complex questions, explore hypotheses, and generate draft reports, while data teams retain control over core models, definitions, and governance.
This mindset shapes the implementation: you expose curated views and metrics to ChatGPT rather than raw, messy tables; you define guardrails around which queries are allowed; and you ensure that critical budget decisions are still reviewed by humans. The payoff is a workflow where marketers can move faster, and analysts can focus on higher-value modelling instead of repetitive reporting.
Invest in Data Literacy and Prompt Literacy Together
Even with strong data models, your team needs the skills to interact with an AI analytics assistant effectively. Data literacy and prompt literacy are now equally important. Marketers should understand the basics of attribution, cohorts, and funnel metrics, while also knowing how to phrase questions and constraints for ChatGPT in a way that yields reliable outputs.
Plan short enablement sessions where you walk through examples: turning a vague question like “How is LinkedIn doing?” into a precise prompt specifying date range, key metrics, comparison periods, and segments. This combination of analytical thinking and prompt design dramatically increases the quality of insights and reduces the risk of misinterpretation.
Design for Transparency and Explainability from Day One
One of the biggest fears around AI-driven marketing analytics is “black box” recommendations. Strategically, you should require that ChatGPT not only provides a number or conclusion but also explains how it got there – which tables it used, which filters it applied, and why metrics differ between platforms and your warehouse.
This means defining system prompts and interfaces that always request rationale, not just results. When your marketing leadership can see why ROAS differs between Meta and your internal reports, trust grows and adoption follows. Explainability also makes it easier to spot when something in the data pipeline has broken, long before it impacts major budget decisions.
Start with Narrow, High-Impact Use Cases and Expand
Rather than trying to “AI-ify” all of marketing analytics at once, pick 1–2 focused, high-value use cases where fragmented campaign data is causing real pain. For example: reconciling paid media spend and opportunities created on a weekly basis, or generating unified campaign performance summaries for executive readouts.
Use these as pilot projects to prove that ChatGPT can reliably query your warehouse, handle metric definitions, and produce insights that stakeholders trust. Once this is working, you can expand to more advanced scenarios like anomaly detection explanations, budget reallocation simulations, or cross-channel funnel diagnostics. This staged approach reduces risk while still building momentum.
Using ChatGPT to unify fragmented campaign data is ultimately a strategic move: you’re not just adding another tool, you’re redefining how marketers interact with data and make budget decisions. With a clear source of truth, sensible guardrails, and a focus on explainability, ChatGPT can become the missing layer that turns chaotic channel metrics into a coherent performance narrative. Reruption combines deep AI engineering with an embedded, Co-Preneur style of working to actually wire this into your stack and workflows. If you’re ready to move from spreadsheet reconciliation to proactive, AI-assisted marketing analytics, we’re happy to explore what a concrete implementation could look like for your team.
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Real-World Case Studies
From Healthcare to News Media: Learn how companies successfully use ChatGPT.
Best Practices
Successful implementations follow proven patterns. Have a look at our tactical advice to get started.
Connect ChatGPT to Curated Marketing Views, Not Raw Tables
The most practical way to handle fragmented campaign data is to expose ChatGPT to curated, business-friendly views in your data warehouse rather than every raw export from your ad platforms. Create consolidated tables like fact_campaign_performance with standardised columns (campaign_id, channel, spend, clicks, conversions, revenue, date) and agreed definitions.
Then configure your ChatGPT integration (via an API middleware or custom tool) to query only these curated views. Provide a schema description so the model understands what each field means and which filters make sense. This dramatically reduces hallucinations and empowers non-technical users to ask complex questions without needing to know your underlying ETL logic.
System prompt example for schema context:
You are a marketing analytics assistant. You can only query the following views:
- mart_campaign_performance (one row per channel, campaign, date)
- mart_funnel_stages (aggregated lead, MQL, SQL, opportunity metrics)
Definitions:
- conversions = primary conversion event defined by Marketing Ops
- revenue = closed-won revenue from CRM, mapped to originating campaign
Always base your answers on these definitions and mention when data is limited.Use ChatGPT to Generate and Validate SQL for Common Questions
One high-leverage practice is to use ChatGPT as a SQL copilot for marketing analytics. Marketers or analysts describe their question in natural language, ChatGPT drafts the SQL query against your curated views, and a human quickly reviews and executes it. Over time, you can store approved queries as reusable templates.
This not only speeds up ad-hoc analysis but also standardises how recurring questions are answered. For example, instead of three different people writing three slightly different queries to calculate ROAS, you converge on one canonical version – all assisted by ChatGPT.
Example prompt:
You are a senior data analyst. Write a BigQuery SQL query using the view `mart_campaign_performance`.
Goal: Compare ROAS by channel for the last 30 days vs. the previous 30 days.
Constraints:
- Exclude campaigns with < 100 clicks total.
- Group by channel only.
- Return spend, revenue, ROAS, and % change in ROAS.
Explain your logic in comments inside the SQL.Automate Unified Campaign Summaries and Executive Readouts
ChatGPT is particularly strong at turning numbers into narratives. Once you have consistent metrics from your warehouse, you can automate weekly campaign performance summaries for different audiences: channel owners, marketing leadership, or the C-level.
Set up a workflow where a scheduled job runs a set of SQL queries, passes the results as structured JSON to ChatGPT, and asks it to generate a concise, audience-specific summary with key highlights, anomalies, and recommended next steps.
Example prompt with data payload:
You are a performance marketing lead. Here is JSON with campaign KPIs for the last 7 days and the previous 7 days.
Tasks:
1) Highlight the 3 most important changes in performance.
2) Explain any major discrepancies between platform-reported conversions and our internal revenue metrics.
3) Propose 3 concrete optimisation actions for next week.
Be concise (max 400 words) and avoid technical jargon.Let ChatGPT Explain Discrepancies Between Platforms and Internal Numbers
One of the most frustrating aspects of fragmented campaign data is that numbers rarely match between ad platforms, analytics tools, and CRM. You can use ChatGPT to systematically explain data discrepancies so teams stop wasting time in unproductive debates.
Provide the model with a comparison of metrics (for example, Meta-reported conversions vs. CRM opportunities vs. warehouse conversions) plus metadata about attribution windows, tracking changes, and known data quality issues. Ask it to generate a clear explanation in business language and propose which number should be used for which decision.
Example prompt:
You are a marketing analytics consultant. We have three conversion counts for Campaign X:
- Meta Ads dashboard: 1,200 conversions
- Google Analytics 4: 900 conversions
- CRM opportunities: 250 opps, 80 closed-won deals
Here is context on attribution windows, tracking changes, and known issues: <insert JSON>.
Explain in plain language why these numbers differ and which source we should use for:
- In-platform optimisation
- Management reporting
- Revenue attribution
Be specific and pragmatic.Enable Marketers to Self-Serve Segmentation and Cohort Analysis
Once your basics are stable, empower marketing to run their own segmentation and cohort analysis through ChatGPT. Instead of waiting in the data team’s backlog, campaign managers can explore performance by audience, creative theme, device, or funnel stage directly.
Provide prompt templates and a simple interface (for example, a chat embedded in your internal analytics portal) where users can specify segments and time frames. Behind the scenes, ChatGPT generates and runs the queries against your warehouse and returns both tables and commentary.
Example prompt template for marketers:
You are a marketing analytics assistant.
Question: Compare lead-to-opportunity conversion rate by campaign objective
(traffic vs. lead gen vs. conversions) for the last 90 days.
Constraints:
- Use only campaigns with >= 50 leads.
- Show results by objective and channel.
Output:
1) Summary table.
2) 3 key insights.
3) 2 concrete actions to redistribute budget.Define Clear KPIs and Guardrails for AI-Driven Analytics
To ensure ChatGPT is actually improving your marketing analytics, not just adding novelty, track a small set of KPIs for the new workflow: time spent on manual reporting, number of ad-hoc requests to the data team, cycle time from question to decision, and adoption of AI-generated reports.
At the same time, implement guardrails: for example, require human review for any recommendation that implies budget shifts above a certain threshold, and log all AI-generated queries and outputs for auditability. Over the first 2–3 months, you should realistically aim for 30–50% reduction in manual reporting time for your marketing team and significantly faster turnaround on performance questions, while maintaining – or improving – trust in the numbers.
Expected outcomes: marketing teams spend fewer hours in spreadsheets, alignment on “one version of the truth” increases, and budget reallocation decisions can be made in days instead of weeks. These are achievable, measurable improvements when ChatGPT is properly wired into a robust data foundation.
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Frequently Asked Questions
ChatGPT helps by sitting on top of your marketing data warehouse and acting as a flexible analytics layer. Instead of manually exporting spreadsheets from ad platforms, email tools, and web analytics, you centralise the data once, then let ChatGPT generate SQL queries, reconcile metrics, and produce unified reports.
It can explain why Meta, Google Analytics, and your CRM show different numbers, standardise definitions across teams, and turn complex datasets into clear performance summaries and recommendations. The key is that ChatGPT works with curated, trusted views of your data rather than raw, inconsistent exports.
You need three main ingredients: a working data pipeline into a warehouse, basic data modelling capabilities, and someone who can integrate ChatGPT via API or use a suitable middleware. On the business side, you need marketing stakeholders who can define which metrics matter and how they should be calculated.
You don’t need a large data science team to start. A small cross-functional squad (marketing lead, data engineer/BI developer, and a product/IT owner) is usually enough to get a first version running. Prompt design and enablement for marketers can be added incrementally once the technical foundation is in place.
If your campaign data is already flowing into a warehouse, you can see tangible results within a few weeks. In a focused pilot, it’s realistic to go from idea to a working prototype of AI-assisted reporting in 3–6 weeks: connecting ChatGPT, exposing curated views, and delivering the first automated summaries or discrepancy explanations.
Full adoption across the marketing organisation takes longer, typically a few months, as you refine prompts, expand use cases, and build trust in the outputs. The crucial metric is how quickly you can move from manual spreadsheet reconciliation to AI-assisted, on-demand insights for at least one critical reporting process.
The direct cost of using ChatGPT via API is typically low compared to media spend and team salaries. The ROI comes from reducing manual reporting time, speeding up budget and channel decisions, and avoiding misallocation of spend due to inconsistent data. For many teams, even a 10–20% improvement in budget allocation efficiency quickly outweighs the implementation cost.
In practical terms, organisations often realise: 30–50% reduction in time spent building recurring reports, fewer errors from manual data handling, and faster detection of underperforming campaigns or tracking issues. These effects compound over time, leading to clearer insight into what truly drives pipeline and revenue.
Reruption works as a Co-Preneur, embedding with your team to build real AI solutions rather than just advising. We start with an AI PoC (9,900€) that focuses on a concrete use case, such as unifying paid media performance reporting or automating weekly campaign summaries. In this phase we define the use case, assess data feasibility, build a working prototype, and evaluate its performance in your environment.
From there, we support you in hardening the solution: integrating ChatGPT securely with your data warehouse, designing curated marketing views, implementing governance and guardrails, and enabling your marketing team to use the new workflows. Our combination of strategic clarity and deep engineering means you don’t just get a slide deck – you get a functioning AI analytics assistant that helps your marketers move faster and make better decisions.
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