Demand & Revenue Forecasting
Time-series and causal models that project sales, demand, cash flow, and capacity by week, region, or SKU, with confidence ranges your finance team can plan against.
AI Development · 06 Predictive Analytics
Forecasts, recommendations and dashboards you can act on
Forecasting, recommendation engines, and decision dashboards drawn from your data. Built around the questions your team asks every week, and validated hard enough that people actually trust the numbers.
You own the models, code and IP Weekly demos Reply within one business day
Forecast engine
Demand forecast - next 8 weeks
ActualModelledConfidence88%
94.2%
Accuracy
+18%
Vs baseline
8w
Horizon
Retrained nightly on 14 months of order behaviour.
Predictive Analytics, in short
OlDevs builds predictive analytics systems for corporations, associations, government bodies, franchises, and growing businesses in Vancouver and across Canada.The work covers demand and revenue forecasting, recommendation engines, lead and churn scoring, anomaly detection, and decision dashboards, all trained on the organisation's own data and delivered into the CRM, warehouse, or reporting tools where decisions are made. Every model ships with a data pipeline, accuracy monitoring, and documentation so predictions stay reliable as the business changes.
Key facts
- 01Part of
- AI Development, discipline 06
- 02Core outputs
- Forecasts, recommendations, scores, dashboards
- 03Typical data sources
- CRM, ERP, POS, web analytics
- 04First model live
- Six to ten weeks
- 05Ownership
- Your data, code and models
- 06Based in
- Vancouver, serving all of Canada
06 · Predictive Analytics
Turn last year's data into next quarter's decisions.
What we deliver in Predictive Analytics
Recommendation Engines
Product, content, and next-best-action recommenders that learn from behaviour and catalogue data to lift basket size, retention, and engagement.
Lead & Customer Scoring
Propensity models that rank leads, accounts, and members by their likelihood to convert, renew, or upgrade, written straight into your CRM.
Churn & Retention Prediction
Early-warning models that flag customers, members, or subscribers likely to leave, with the drivers behind each score so your team knows what to do about it.
Decision Dashboards
Executive and operational dashboards that put forecasts, scores, and anomalies beside the actuals, in Power BI, Looker, Tableau, or a custom web app.
Anomaly Detection & Alerting
Statistical and machine-learning monitors that spot unusual transactions, usage, or costs as they happen and route an alert to the right person.
Process
Our predictive analytics process
Discover
We map the decisions you want to improve, the people who make them, and the metric that will tell us the model is working.
Data
We connect your sources, profile quality, engineer features, and build the pipeline that keeps training data current.
Model
We train and back-test candidate models against your current baseline, then choose the one that wins on accuracy and explainability.
Integrate
We deliver predictions where they get used: CRM fields, dashboards, APIs, or scheduled reports.
Monitor
We track accuracy, drift, and usage in production, retrain on the agreed cadence, and report results monthly.
How we work
How we build models people trust
01
Start with the decision
Every model is scoped around a specific choice someone makes, how often they make it, and what a better answer is worth.
02
Fix the data first
We profile data quality before modelling and repair gaps, leaks, and inconsistencies at the source, so the model learns from the truth.
03
Beat the spreadsheet
Every model is benchmarked against the simple baseline your team uses today; if it cannot beat that, it does not ship.
04
Back-test on real time periods
We validate on held-out months and seasons rather than random rows, so reported accuracy reflects how the model will actually be used.
05
Explain every prediction
Top drivers, confidence ranges, and plain-language summaries ship beside each score so people understand and act on the numbers.
06
Watch it in production
We monitor drift and accuracy after launch and retrain on a cadence agreed with you, with every run logged and reported.
Who it's for
Predictive Analytics for organisations that have to get it right.
Whether the audience is a customer, a member, a citizen, or your own team, the work has to hold up under real use.
Corporations
Finance, operations, and sales leaders who need forecasts and scores inside the tools they already use, with the governance and audit trails IT expects.
Associations & government
Organisations forecasting membership, programme demand, and service volumes, with explainable models hosted wherever policy requires.
Franchises
Multi-location brands that want location-level forecasts, inventory and staffing recommendations, and one dashboard shared by head office and franchisees.
Entrepreneurs & startups
Founders with growing data who want a first forecasting or recommendation model that proves its value quickly and scales as the business does.
12+
years turning business data into working software
6–10 wks
typical time to a first model in production
18%
median forecast error reduction versus client baseline
Works well with
Related services
01
Custom ML Solutions
Bespoke models trained on your data, taken from prototype to production-grade MLOps.
Explore02Generative AI & LLMs
Chatbots, copilots, and RAG systems on leading language models, fine-tuned to your domain.
Explore03Natural Language
Classification, sentiment, entity extraction, semantic search, and translation.
ExploreSelected work
Selected predictive analytics work.
Client names are withheld; the problem and the outcome are the point.
01AI
A propane and heating fuel distributor delivering to roughly 9,000 rural households and farms across New Brunswick and Nova Scotia
- Industry
- Energy distribution
- Services
- Predictive analytics · Custom ML solutions
- Duration
- 4 months
“We stopped arguing about the weather on Monday morning, because the route plan is already on the screen when dispatch opens.”
Challenge
Delivery volume swung with the weather, but the weekly plan was a spreadsheet one person rebuilt every Monday from last year's numbers and a feel for how cold it was going to get. Trucks ran half empty in mild weeks and missed customers during cold snaps, and the emergency refills that followed consumed the margin on long rural routes. Nobody could say in advance which of the six depots was about to run short.
Solution
We built a demand model at the level of the individual customer tank, trained on three years of delivery history, tank telemetry and degree day records, using gradient boosted trees in Python. Forecasts run nightly and land in the dispatch board the planners already work from, each with a confidence band so they know when to hold capacity in reserve. Planner overrides are captured and fed back as a training signal rather than lost in a side file.
FAQ
Predictive Analytics — questions we hear first.
A first forecasting or scoring model typically reaches production in six to ten weeks, depending on how ready your data is. Discovery takes one to two weeks, data preparation two to three, modelling and validation two to three, and integration one to two. Recommendation engines and multi-site dashboards can run longer because they touch more systems. You receive a dated plan after discovery and we report against it every week.
Access to the historical data behind the decision you want to improve, usually 18 to 36 months of records from your CRM, ERP, point of sale, or web analytics, plus a stakeholder who owns that decision. We also need success defined in plain terms, such as forecast error under a set threshold or a measurable lift in conversion from scored leads. If your data lives in spreadsheets or across several systems, that is fine; consolidating it is part of the work.
We build in Python with libraries such as scikit-learn, XGBoost, Prophet, and PyTorch, and orchestrate pipelines with dbt, Airflow, or the native tooling of your warehouse. We work in Snowflake, BigQuery, Databricks, Azure Synapse, and PostgreSQL, and deliver dashboards in Power BI, Looker, Tableau, or a custom web app. Where you already have a platform, we build on it rather than replacing it.
Every model has an agreed accuracy metric, such as MAPE for forecasts or precision and recall for scoring, plus the business metric it is meant to move. During the build you receive weekly progress notes with validation results against your baseline. After launch you receive a monthly report covering prediction accuracy against actuals, data drift, usage, and business impact, alongside a live monitoring dashboard you can open at any time.
You do. Models are trained on your data in cloud accounts you own or that we set up in your name, and the code, trained models, feature pipelines, and documentation are handed over at every milestone. We never reuse your data to train models for another client. If you later bring the work in-house, your team already has everything it needs to run, retrain, and audit the system.
We run the project as one team with yours. Your analysts and engineers join the weekly reviews, see every model comparison, and can pair with our data scientists during the build. We document decisions and code as we go and finish with a handover session, so your team can retrain, extend, or audit the models without us. If you would rather we keep monitoring and retraining running, that is available as an ongoing engagement.
Cost depends on the number of models, the state of your data, and how many systems the predictions need to reach, so we scope each project individually rather than quoting a flat rate. Tell us the decision you want to improve and the data you have, and we will reply within one business day with a proposed scope and quote. Request a quote and we will take it from there.
Let’s connect
Let’s scope your predictive analytics project.
Tell us what you’re building. We’ll reply within one business day with next steps and a tailored quote — no obligation.