Unify Fragmented Campaign Data with Claude for Smarter Marketing Analytics

Standardize Your Data Exports Before They Reach Claude

The quality of Claude’s analysis depends heavily on the consistency of the inputs. Before involving AI, define a minimal set of standard export templates for your main platforms (e.g., Google Ads, Meta Ads, LinkedIn, email, CRM, web analytics). Ensure each export includes dates, campaign/ad set/ad names, spend, impressions, clicks, key conversions, and revenue where available.

Use a simple internal guideline: same time zone, same date format, same currency, and, if possible, the same column headers across platforms for similar metrics. This reduces the time Claude spends guessing mappings and increases the reliability of the unified view.

Example instruction to your team:
Export weekly data for <week_start> - <week_end> from:
- Google Ads: Campaign performance report, include cost, clicks, conversions, conversion value
- Meta Ads: Campaign performance report, same metrics
- CRM: Opportunities created/closed with campaign or UTM source/medium
- Analytics: Sessions, transactions, revenue by source/medium/campaign
Save all as CSV with UTF-8 encoding and identical date range.

Use Claude to Build a Cross-Channel Naming and Metric Map

Once you have exports, the first tactical step is to let Claude create a “data dictionary” that harmonizes channel naming and metrics. Upload a few representative CSVs and ask Claude to identify equivalent dimensions and metrics across platforms and propose a unified schema.

Example prompt for schema harmonization:
You are a marketing data analyst. I will upload CSV exports from multiple platforms.
Tasks:
1) Detect which columns represent similar concepts (e.g., campaign name, ad group, cost, impressions, clicks, conversions, revenue).
2) Propose a unified schema with standard column names and data types.
3) Suggest mapping rules for each platform to this unified schema.
4) Identify any ambiguities or conflicts and ask clarifying questions.
Output the schema and mappings in a clear markdown table.

Use Claude’s suggested schema as the foundation for future exports and automations. Over time, you can refine this mapping and even codify it into scripts or ETL jobs that pre-transform data before sending it to Claude.

Let Claude Merge Spreadsheets and Resolve Conflicts Explicitly

Instead of manually copying and pasting between sheets, use Claude to merge campaign spreadsheets and highlight discrepancies. Upload your standardized exports and instruct Claude to join them on agreed keys (e.g., date + campaign name or date + UTM parameters), then surface conflicts.

Example prompt for merging and conflict detection:
You are helping me build an omnichannel marketing performance table.
I will upload:
- Google Ads weekly export
- Meta Ads weekly export
- CRM opportunities with campaign / UTM info
- Web analytics sessions and revenue by source/medium/campaign
Tasks:
1) Join these datasets on date and campaign (or UTM) where possible.
2) Create a combined table with columns: date, channel, campaign, spend, clicks,
   sessions, leads, opportunities, revenue.
3) Highlight any conflicts (e.g., different revenue figures for same campaign/date)
   and propose how to reconcile or flag them.
4) Provide the final table as CSV text I can paste into a sheet.

This workflow turns a multi-hour manual task into minutes, while still letting you review and refine how conflicts are handled before using the data for decisions.

Automate Weekly Performance Summaries and Anomaly Detection

Once merged tables are in place, use Claude to generate consistent weekly marketing performance summaries with anomaly detection. Provide the unified dataset and ask Claude to compare against previous periods, identify outliers, and propose hypotheses for changes.

Example prompt for weekly summary:
You are my marketing analytics assistant. Here is a CSV with unified performance data
for the last 8 weeks. Columns include: date, channel, campaign, spend, clicks,
conversions, revenue.
Tasks:
1) Summarize performance for the latest week vs. the previous 4-week average by channel.
2) Highlight significant anomalies (e.g., >30% change in CPA, ROAS, or conversion rate).
3) Suggest 3-5 hypotheses for the most important anomalies.
4) Recommend 3 concrete optimization actions for next week.

Over time, you can standardize this prompt and combine it with a simple automation (e.g., export + upload workflow) so every Monday the team reviews a consistent AI-generated performance brief instead of manually assembling one.

Create Executive-Ready Views Without Rebuilding Dashboards

Executives often need a simplified, narrative view of marketing performance across channels. Rather than creating and maintaining separate decks, use Claude to turn your merged data into concise executive summaries, tailored to non-technical audiences.

Example prompt for executive summary:
You are writing for a CMO who has 10 minutes to review performance.
Using the attached unified performance CSV, please:
1) Provide a 1-page narrative summary of overall performance this month vs. last month.
2) Focus on revenue, CAC, ROAS, and any major shifts by channel.
3) Use clear, non-technical language.
4) Add a short bullet list of key risks and opportunities.
5) Include a simple table with only the 5 most important metrics.

This reduces the time your senior marketers spend “translating” analytics into business language, while still letting them edit and refine the story before sharing.

Capture and Reuse Successful Prompts as Internal Playbooks

As your team iterates, some prompts will consistently deliver high-quality analysis (e.g., for budget reallocation recommendations or channel-level deep dives). Don’t let this live only in personal chats—turn them into shared playbooks.

Example internal playbook entry:
Playbook: Channel Budget Reallocation
Use when: Planning next month’s budget.
Prompt:
You are a performance marketing strategist. Using the attached unified performance
CSV for the last 60 days, please:
1) Calculate CAC and ROAS by channel and campaign.
2) Identify underperforming campaigns that should be paused or reduced.
3) Propose a reallocation of the same total budget across channels to maximize
   expected revenue, explaining your assumptions.
4) Present results as a table and a short narrative.

Documenting and sharing these prompts turns Claude into a repeatable team capability instead of a one-off experiment owned by a single power user.

Implemented together, these practices typically reduce manual reporting time by 30–50%, accelerate weekly performance reviews from hours to minutes, and improve confidence in cross-channel budget decisions. The exact metrics will vary by organisation, but the pattern is consistent: less time wrestling spreadsheets, more time optimizing campaigns based on a unified, AI-assisted view.