Data Science Consulting Services
Answers to the questions your reporting cannot reach
What is Data Science Consulting?
Dashboards tell you what happened. They are much weaker at explaining why, at predicting what comes next, or at separating a real effect from ordinary variation. Which customers are about to leave and what distinguishes them? Did the campaign work, or was that just seasonality? Which three of the forty factors we track actually move the outcome?
Data science answers that kind of question, and the valuable part is less the modelling than the framing. A clearly stated question with an agreed definition of success produces something a team can act on. A vague brief to "find insights in the data" almost always produces an interesting deck and no decision. We work the question first, then the analysis.
What We Deliver
Exploratory Analysis
Structured investigation of your data to surface patterns, segments and relationships worth pursuing. Usually the first engagement, because it establishes what the data can and cannot support before anything is built on it.
Statistical Modelling
Regression, time series, survival and causal inference methods applied to questions about drivers and effects. Results come with confidence intervals and stated assumptions, not a single number presented as certainty.
Customer Analytics
Segmentation, lifetime value, churn drivers and cohort analysis. Identifies which customers matter most, which are at risk, and what actually differentiates them - as opposed to what everyone assumes differentiates them.
Forecasting
Demand, revenue and capacity forecasts using time series methods that account for seasonality, trend and known events. Delivered as ranges rather than point estimates, because planning against a false precision is worse than planning against uncertainty.
Experimentation and A/B Testing
Test design, sample size calculation and result analysis that distinguishes real effects from noise. Prevents the common and expensive mistake of acting on a difference that was never statistically meaningful.
Optimisation
Pricing, inventory, routing and scheduling optimisation using operations research techniques. Applies where the question is not what will happen but what is the best available choice given your constraints.
Problems This Solves
“We have dashboards but no insight”
Reporting describes; analysis explains. We start from the decision you need to make and work backwards to the question.
“We cannot tell whether a change worked”
Proper experiment design and causal methods separate the effect of the change from everything else moving at the same time.
“Our forecasts are consistently wrong”
Most forecasting failures come from ignoring seasonality or presenting a point estimate where a range was needed.
“Analysis arrives too late to use”
Repeatable analyses are packaged as scheduled jobs or self-serve tools rather than one-off manual work.
How We Work
Frame the Question
Define the decision, the data available and what a useful answer looks like.
Explore and Validate
Assess data quality and test whether the question can be answered with it.
Analyse
Apply appropriate methods, quantify uncertainty, challenge the findings.
Deliver and Operationalise
Present clearly, and where it recurs, automate it.
Talk to us and
get your project moving!
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