DemandSens Press

Drastically reduce unsold Copies while protecting single-issue Sales of your Newspapers and Magazines with DemandSens Press

The modern press industry faces two kinds of challenges:

#1 – OVERSUPPLY
Every day, millions of newspapers end up being thrown away without ever having been sold, let alone read. Many press vendors have a high rate of unsold newspapers on a recurring basis. They are oversupplied. The key is to reduce the costs of production and distribution by minimizing the number of unsold copies.

#2 – UNDERSUPPLY
Others sell frequently their entire stock. This means that they would have maybe sold more if they have received a higher supply of newspaper. They might be undersupplied. Missed opportunity to make profit?

It's hard to keep up with demand, but AI-Forecasting can help

Forecasting the right products in quantities suited for any given day is not an easy task, but we have made this our sole focus. Our DemandSens solution uses Artificial Intelligence (AI) to predict the optimal daily stock of newspapers and magazines to be sent out to each newspaper and press vendor. These predictions are based on historical data as well as various other factors like demand patterns or seasonality trends.

This way, we can reduce your costs of production and distribution by minimizing the number of unsold copies, while reducing the cases where copies are sold-out too fast.

DemandSens Press helps you lower distributed quantities and limit shortages while speeding up the forecasting process.

Get in Touch with our Team

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DemandSens Press in a Nutshell

#Forecast Calculate the optimal number of copy of any type of press title to be supplied for every single point of sales across your distribution network.

#Challenge Let human expertise input expertise by adjusting quantities with the help of contextualized information and advanced filtering/sorting features.

#Analyze Investigate sales trends and key performance indicators thanks to an advanced Business Intelligence module, computing on-the-fly what you're looking for.

The DemandSens Press Model

Data Pre-Processing	Modelling	Model Optimization
1a Data Cleaning Delete non-logical, aberrant, or incomplete data. 1b Feature Enrichment Add calendar and PoS additional information (optional). 1c Data Normalization Choose a standardization method, often depending on PoS seasonality. 1d PoS Clustering By average sales, by tranche, or by profile. 1e Additional Step Add sales lags and estimate potential sales.	2a Split of Data Separate training data into two: a learning set and a validation set. 2b Models’ Generation Generating a model for each cluster with a different quantile.	3a Optimize Parameters Optimize the model hyperparameters. 3b Best Cluster Choose for each cluster the best model, that reduces both the unsold and sold-out rates.

Lifting the Hood

DemandSens is your unique fully automated AI-driven Forecasting Engine.

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  Free PDF Download: DemandSens.Press Introduction 7.67 MB Download

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