Stimulation Initiative for European Neural Applications
Esprit Project 9811
Prediction of Newspaper Sales
Company background
De Telegraaf is one of the major
Dutch publishing companies of newspapers and magazines.
The problem
For each new issue of a newspaper or magazine, `De Telegraaf' has
to estimate the number of issues that will be sold in
supermarkets, bookshops, kiosks etc. Too many delivered issues result in a loss of investment.
On the other hand, a sell out is a loss of potential profit and may result in
unsatisfied consumers. The ideal is to deliver the number of issues that will be sold plus
an additional one to verify that each customer has been able to buy it.
Of course, in reality only an approximation of this ideal is feasible,
as the sale of newspaper is highly determined by chance.
Since there are thousands of different sales points an automated system is desirable.
On the other hand, the applied method should be robust, since it should be used for
thousands of different sales points with largely different characteristics. The number
of sales could range from a handful to several hundreds for the largest shops. The
variability of sales could be small or large and might depend on season as well.
Sudden changes, due to a new competitor, a new location or owner, but often even without
any known cause, should be detected and used for new predictions as soon as possible.
Up to now, `De Telegraaf' uses a traditional multiple linear regression method.
Neural network application
The Foundation for Neural Networks (SNN) investigated the use of neural computing to
provide more accurate predictions. A large number of neural networks - one for each
individual sales point - have been trained on the basis of last 3 years of sale figures.
The training procedure has been fully automated. This procedure is designed in such a way
that sell outs - which are more undesirable than unsold issues - are avoided by the networks.
Pilot studies indicate that this newly developed method could avoid up till 40% of the sell outs without
increase of the number of unsold issues. Currently the system is implemented and tested at `De Telegraaf'.
Benefits
The neural networks provides better estimates of the number of issues of newspapers and
magazines that should be delivered to the sales point. Importantly, a reduction of sell outs
is not only an increase in sale, but also implies a reduction of unsatisfied consumers
who might run to a competitor. The fully automated training procedure
allows that the neural networks can easily be retrained with new data.
This, combined with the fact that `De Telegraaf' uses a neural network for each individual sales point,
results in a highly flexible system that easily adapts to new market situations.
Generalization
The prediction of newspaper sales is a typical problem for which
no good numerical model exists. In addition, the problem has a large chance component.
The fact that conventional statistical techniques performed reasonable on this
task was indicative that the performance could be improved by neural networks.
In a similar way, neural computing can be used for sales prediction in the food - and durables
markets for department-stores and supermarket chains.
Contact persons
H.J. Kappen, Foundation for Neural Networks, Postbus 9101, 6500 HB Nijmegen,
The Netherlands. Tel. +31 24 3614241, Fax. +31 24~3541435. E-mail : bert@mbfys.kun.nl
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