Dashboards tell you what happened. Analytics tells you why — and what to do next. I dig into your data to find the patterns, risks, and opportunities that aren't visible on the surface.
I use AI-driven pattern recognition alongside hands-on analysis to surface purchasing behaviors, engagement trends, and lifetime value signals that manual reporting misses.
I identify and track the KPIs that actually matter — not vanity metrics, but the numbers that correlate with revenue, retention, and growth.
Problems show up in data before they show up in revenue. I build monitoring frameworks that flag significant drops so you can respond before a small dip becomes a big loss.
When a strategic decision lands on your desk — a new product launch, a pricing change, an acquisition — I run the analysis to give you the evidence behind the choice.
You have data but no insight. Your reports show revenue went up 12% last quarter, but you can't explain why — or whether it'll happen again.
You're losing customers and don't know which ones or why. By the time someone cancels or stops buying, it's too late. You need to see the warning signs months earlier.
You're about to make a big decision and want evidence. Expanding to a new location, changing your pricing, cutting a product line — you want more than a hunch before you commit.
Most analytics projects start with a single question — "why are we losing customers?" or "which of our locations is actually the most profitable?" I scope the question, identify the data sources needed, and deliver a clear, documented analysis within 2–4 weeks.
For ongoing analytics, I build recurring models and scoring systems that update automatically. A customer health score, for example, runs in the background and flags at-risk accounts weekly — no manual analysis required after the initial build.