DemandSense

The forecasting product our team deploys on demand engagements — and what working with us on it actually looks like.

The engagement

Four weeks, roughly

You are buying a working forecast and the ability to run it yourselves, not a licence and a login.

We look at your data

Sales data from point of sale plus a product and location master is the minimum input. We tell you early what it can and cannot support.

We add the outside world

Weather, demographic and competitor data go in easily; your social media data integrates alongside the other sources.

We tune it per product

The product selects the best-suited model from several for each line, and surfaces the significant drivers of sale out of hordes of candidate factors.

We hand it over

It runs automatically in the background across all products or a partial list. Your team owns it; we stay available rather than embedded.

What the product does

Forecasts that read more than history

  • Forecasts demand for products or categories
  • Combines enterprise and external data for a more accurate prediction
  • Uses social conversation and sentiment to derive demand
  • Identifies the significant drivers of sale out of hordes of factors
  • Selects the best-suited forecasting model for each product
  • Runs on a software-as-a-service model, with minimal data input to start

What you get out of it

Accuracy where it pays

Prediction by product and location, so planning improves at the level you actually order and allocate at.

Cost out of the middle

Save cost by minimising over-stocking, and improve revenue by stocking the right mix.

A base for what comes next

The output forms the base data for advanced analytics and extended supply chain planning.

Scale

Powered by PetaBolt

For big data analytics the product runs on PetaBolt, an R+ big data platform that handles over 10 billion records — which is why a long history makes the forecast better rather than slower.

Who we build it for

Supply chain, sales, marketing, media planning

  • Retail stores and chains
  • CPG and distribution companies
  • Food and beverages
  • Spares and consumables

Borrow the team, keep the forecast

Tell us what you are trying to predict and we will tell you what your data can support — before you commit to anything.