Retail solutions
Demand Forecasting for Retail
Beryl Analytics builds demand forecasting the way a software team would: version-controlled, monitored, peer-reviewed, and shipped in small slices. retail chains get analytics infrastructure they can debug at 2am, not a black box they can only call us about.
Why retail teams choose Beryl Analytics for demand forecasting
- Senior practitioners. No bait-and-switch — the architects you meet in scoping are the engineers who ship the system. We don't farm work to juniors.
- APAC time zone, APAC context. We understand retail chains regulations, data residency expectations, and the procurement cycles your team actually navigates.
- Honest scope. If a demand forecasting use case isn't ready for ML yet, we'll tell you. Half our highest-impact engagements start by killing initiatives that wouldn't have worked.
- Tool-agnostic. Snowflake, BigQuery, Databricks, Postgres, S3 — we work with what you already run.
- Speed without recklessness. First production slice in 4-6 weeks. Hardened over the next 8-12. No 18-month black-box programmes.
How we deliver demand forecasting engagements
- 01
Data audit (week 1)
A focused review of what data you have, where it lives, and what shape it's in. Outputs a written read with the gotchas and where to start.
- 02
Contract & instrument (weeks 2-3)
We formalise the inputs the system will depend on — schemas, freshness SLAs, ownership — and instrument anything missing. No model without solid inputs.
- 03
Model + interface (weeks 4-7)
The model itself plus the surface your operators will actually use. Built together so the analysts who debug it know exactly what each output means.
- 04
Soft launch & calibration (weeks 8-10)
Live in a small slice of the business. We watch every decision the system informs, calibrate, and only then expand.
- 05
Full rollout
Scale to the full surface area with documentation, training, and an on-call playbook your team owns end-to-end.
Frequently asked questions about Demand Forecasting for Retail
How long does a typical Demand Forecasting engagement take for a retail business?
Most demand forecasting projects for retail chains land a working production slice within 4-6 weeks, then harden and expand over the following 8-12 weeks. Larger retail programmes that touch multiple business units take 4-6 months end-to-end.
What data do you need to start a Demand Forecasting project in retail?
Minimum viable inputs are 12-18 months of historical transactional or operational data, basic entity reference tables, and access to the systems that will consume the output. We can work with messy data — cleaning is part of the engagement.
Can Beryl Analytics integrate demand forecasting with our existing retail chains systems?
Yes. We're tool-agnostic and have integrated with Snowflake, BigQuery, Databricks, Salesforce, SAP, Oracle, custom in-house platforms, and dozens of retail-specific systems. Insights surface inside the tools your operators already use.
How do you measure success on a Demand Forecasting engagement?
Before we model anything, we agree the business decision the output will change and the dollar metric we're targeting — revenue lifted, cost avoided, or risk reduced. Demand Forecasting engagements in retail typically return 4-12x within the first year.
Do you work with retail businesses outside major NZ and AU cities?
Yes. We deliver remotely across New Zealand and Australia and visit on-site for discovery, key workshops, and go-live. Distance is not a blocker — many of our highest-impact demand forecasting engagements have been with regional retail chains.