Decisions made on intuition
Data sits in the CRM, analytics and accounting, but price, range and marketing decisions are still made on feel.
Data Science
The answers hidden in your data
We study business data to answer a specific question. Findings become decisions, not a report.
Metrics fall and nobody knows why
Churn is rising or sales are slipping, and ordinary reports show what happened but not why.
Segments and forecasts needed
Thousands of customers, treating them all the same no longer pays, and value segments are impossible without analysis.
A business question, with criteria for a useful answer
Data collection and cleaning from CRM, analytics and accounting
Exploratory analysis: patterns, anomalies, relationships
Customer segmentation by behavior and value
Hypothesis tests and A/B test design
Forecasts and an estimate of each decision's effect
A report of findings, readable without statistics
PROCESS
[ 01 ]free · 30 min
[ 02 ]14 business days
[ 03 ]depends on scope
[ 04 ]ongoing, monthly cycle
From request to result
[ 01 ]free · 30 minClarity Session
We frame the business question and check which data can answer it. You leave with a focus for the study. No sales pitch.
[ 02 ]14 business daysResearch
Data collection, analysis and hypothesis tests. You get the first findings and a view of how reliable they are.
[ 03 ]depends on scopeFindings and decisions
Deeper analysis, segmentation or a forecast, a report of recommendations and a plan to test them.
[ 04 ]ongoing, monthly cycleSupport
New questions studied on a regular cycle, test design and analysis, and updates to segments and forecasts.
Frequently asked questions
and answers
How is Data Science different from Machine Learning?+
Data Science answers questions: why customers leave, which factors affect sales, how to split the audience. Machine Learning builds models that forecast or decide inside the product. The study often comes first and shows whether a model is worth building at all.
How is Data Science different from ordinary analytics?+
Analytics shows what is happening: sales, traffic sources, how conversion changed. Data Science looks for causes and forecasts: why a metric moved, what comes next, which decision has the largest effect. Daily control needs analytics. Hard questions need a study.
What if the data is too thin for conclusions?+
We say so plainly and show how reliable the findings are on the data you have. Often the result is a plan: which data to start collecting and which test to run. We will not present weak findings as confident ones.
Who on the team needs to understand the results?+
The people who will make the decision. The report is written in business language: the finding, how reliable it is and what to do with it. Technical detail and code go separately to your analytics team, if you have one.
THE FINAL STEP TOWARD A SYSTEMReady to turn growth
Ready to turn growth
into predictability?
Start with a free 30-minute Clarity Session.
