Predictive analytics consulting
See what’s likely.
Plan for it.
Most reports show what already happened. We define the question, check your data and test each model against a baseline, so you know how far to trust it.
Rated 5 stars on Clutch.
Decision definition / Data assessment / Evaluation and interpretation
Somerset West, Cape Town / Working globally
A few of the brands we’ve worked with
Selected work
Karo Mining / Predictive analytics
Decisions on data,
not gut feel
For every SEO, AEO, GEO and PPC client, we connect Search Console, Bing Webmaster Tools, Bing Places, Google Business Profile, Google Analytics and AnswerThePublic, so each change is planned on data and its impact measured. Karo Mining’s site now sits near the top of Google.
18.8% click-through rate at an average position of 5.1, August to November 2026.
Redox One / Predictive analytics and Google Ads
Ads tuned on data
We measure what every change to Redox One’s Google Ads does, then keep what works. More of the people who see the ads now click them.
Click-through rate from 6.5% in 2025 to 9.8% in 2026.
Start with the decision
A prediction is only as useful as the decision it informs
A prediction built on patchy data, or aimed at no decision in particular, gives false confidence. What you know when you decide, how good your data is and the cost of being wrong all shape the work.
We assess these before recommending a model and explain the limits of its predictions.
From data to decision
Predictive analytics you can check before you rely on it
Many projects begin with a data review. We use it to assess whether the question can be answered and how a model could be tested.
01
Define the decision
We pin down the decision the prediction will support and when it’s made. That shows what information you’ll have at that moment, and gives the analysis a clear purpose.
Decision and prediction brief / What you’ll know at decision time / Success measure
02
Check the data
We check your data for gaps, consistency and relevance, so you understand its limits before investing in a full model.
Data-quality review / Suitability assessment / Data gaps
03
Build and test
We test possible models against a baseline: the simple estimate you’d make without one. Where we can, we test on data the model hasn’t seen, to check it adds something useful to the decision.
Model comparison / Baseline test / Performance findings
04
Explain the findings
Explain the results, assumptions and uncertainty in plain language, so your team can judge how to use the findings.
Findings in plain language / Confidence and assumptions / Recommendations for use
Working together
Built around the people who decide
The people who’ll act on the results help define what would be useful. We pair what they know with a review of the data and a practical way to test it.
01
Define the question
Discuss what you want to anticipate, the decision involved and the information available today.
02
Plan the model
Agree the question, data access, how results will be tested and who acts on them.
03
Run the model
Assess the data, build the analysis and review the assumptions with your team.
04
Review the forecasts
Review the results together and decide what to use, refine or scale.
Good predictions start with good measurement
Predictions also need the right business context. Our digital analytics service improves measurement where needed, giving a predictive model a better foundation.
What do you want to predict?
Tell us the decision behind it and what data you have. We’ll tell you whether a prediction is possible and where we’d start.
A 30-minute call. No brief needed.