Using Applied AI to Turn Data into Executive Decisions
How I use applied AI to analyze large datasets, find patterns, accelerate research and structure the reporting I present to CMO and VP leadership, with a person checking the output before it goes to leadership.
The problem
I own marketing analytics across email, social, webinar and YouTube for four brands and present performance and recommendations directly to the CMO and VP leadership. The raw material is large exports, inconsistent naming across platforms, incomplete records and questions that change from month to month.
Traditional reporting answered the same questions every cycle. The harder part was the new question: why did this segment drop, which channel is actually influencing pipeline, what changed in this account's behavior. Each of those meant a lot of manual work before anyone could write a sentence about it.
What I built
I built a set of AI-assisted workflows for analysis and reporting. A large dataset goes in with a clear question and context about how the business works. The model helps clean and reshape the data, spot patterns, compare periods and draft a first pass at findings. I then check the numbers, challenge the conclusions and decide what actually goes in front of leadership.
- Analysis of large datasets, including reshaping and reconciling exports from different platforms.
- Pattern finding across channels, segments and time periods.
- Research support for vendor evaluations, market questions and platform decisions.
- Structuring executive reports so findings lead and detail follows.
- Turning messy or incomplete information into usable summaries with clear caveats.
The same approach is behind the contact-enrichment workflow: a clear question, good context about the business and a person checking the output.
How it works
The workflow is simple on purpose. Most of the value is in asking a precise question and checking the answer.
- Business question
- Data export and context
- AI-assisted cleaning and analysis
- Draft findings and patterns
- Human validation and judgment
- Executive report
- Decision
The dashboard below is a representative demonstration built with synthetic data. It shows the kind of structure I use for an executive view. None of the figures belong to any company or customer.
Representative demonstration built with synthetic data. No company or customer information is shown.
Quarterly marketing summary (synthetic)
- Blended open rate 34% +3 pts vs prior quarter
- Marketing-influenced opportunities 142 +8% vs prior quarter
- Webinar registrations 1,840 -4% vs prior quarter
- Accounts flagged for review 12 5 within 60 days of renewal
Engagement index, weekly
View as table
| Week | Engagement index |
|---|---|
| W1 | 41 |
| W2 | 43 |
| W3 | 42 |
| W4 | 45 |
| W5 | 47 |
| W6 | 46 |
| W7 | 49 |
| W8 | 52 |
| W9 | 51 |
| W10 | 54 |
| W11 | 57 |
| W12 | 58 |
Share of influenced pipeline by channel
Account risk and opportunity
- Account B ReviewPrimary contact unsubscribed, renewal in 45 days
- Account D At riskNo engagement in 90 days, renewal in 30 days
- Account A OpportunityThree new engaged contacts, renewal in 90 days
Key findings
- Engagement rose steadily over the quarter, with the largest gains in the academic segment after the send-time change in week 7.
- Webinar registrations dipped, but registrants who attended engaged with follow-up email at twice the average rate.
- Two accounts within 60 days of renewal show engagement drop-off and warrant a Sales conversation this week.
Recommended actions
- Keep the revised send schedule and extend it to the remaining segments.
- Shift webinar promotion earlier and add a second reminder to registrants.
- Hand the two flagged accounts to their sales owners with the engagement history attached.
Draft analysis prepared with AI assistance. Figures validated, findings edited and recommendations written by a person before presentation.
My role
I define the questions, prepare the data, design and run the workflows, validate every output and present the results. AI accelerates the analysis. The judgment about what the numbers mean for the business, and what to recommend, is mine.
Business value
- Analysis is faster, so more questions get asked and answered.
- Leadership gets findings and recommended actions, not just charts.
- Reports carry clear caveats about data quality, so decisions are made with eyes open.
- The same workflows support vendor research, platform decisions and budget recommendations.
AI does not make decisions in this process. It helps a person get to the decision faster and with better information.
Tools and capabilities
Confidentiality
No actual executive report, company metric or customer information is reproduced on this page. The dashboard is a representative demonstration built with synthetic data.