AI & BUSINESS ANALYSIS · TORONTO
Good data.
Better decisions.
Explore how business questions become structured data, analytical evidence and AI workflow designs for sales, marketing and finance.
Monthly recurring revenue
$14.43M30 months of context
THE DECISION BEHIND THE DATA
Protect the revenue base.Test the next growth opportunity.
01 / SELECTED WORK
Three problems.
See how they’re solved.
Start with the raw data. Follow the preparation,
calculations and reasoning. Then explore how AI
could help a team act on the findings.
SaaS · Revenue intelligence
Find the growth. Understand the risk.
30 months of subscription data reveal the revenue mix, retention patterns and acquisition economics behind SaaS growth.
Retail · Commercial performance
Make revenue leakage visible.
A commercial review of recorded transactions, product mix and category budgets turns scattered tables into focused sales and marketing decisions.
Finance · AI-assisted reporting
Explain the gap behind the headline.
A financial variance review connects the revenue result to delivery costs, operating profit and a reporting workflow with human review.
PROJECT CONTEXT Industry-collaboration cases completed through a graduate AI/analytics club. Client names are anonymized.
02 / FROM FINDING TO ACTION
The chart is the start.
The decision is the point.
A closer look at the SaaS case.
Choose a team to see how one analysis
leads to different next steps.
RECOMMENDED ACTION / SALES
Protect the Enterprise revenue base.
Prioritize renewal reviews and expansion conversations for Enterprise accounts. Build a separate Business-plan pipeline to test diversification.
Enterprise contributes 64.8% of cumulative MRR and has 112.2% mean NRR.
Track Enterprise renewal coverage, expansion MRR and Business pipeline share.
03 / HOW I THINK
Business context.
Analytical rigor.
Practical AI.
I’m Candice, based in Toronto. My work connects business analysis with AI systems thinking: defining the right problem, understanding the data, and designing a useful response.
Start a conversation ↗Frame the business question
Clarify the stakeholder, decision and success metric before selecting a tool.
Find the signal in the data
Check definitions and data quality. Compare the right segments, time periods and benchmarks.
Make the next step specific
Translate the result into an action, an owner and a measure of success.
Design for repeatable use
Use AI where it helps, with grounded inputs, explicit exceptions and human review.