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California Scientific      BrainMaker Neural Network Software
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Maximize Returns on Direct Mail with BrainMaker Neural Network Software

Microsoft, a leading computer software developer based in Redmond, Washington, is using BrainMaker neural network software to maximize returns on direct mail. Each year, Microsoft sends out about 40 million pieces of direct mail to 8.5 million registered customers. Most of these direct mailings are aimed at getting people to upgrade their software or to buy other related products. Generally, the first mailing includes everyone in the database. The key is to send the second mailing to only those individuals who are most likely to respond.

Company spokesman Jim Minervino when asked how well BrainMaker neural network software had maximized their returns on direct mail responded, "Prior to using BrainMaker, an average mailing would get a response rate of 4.9%. By using BrainMaker, our response rate has increased to 8.2%. The result is a huge dollar difference that brings in the same amount of revenue for 35% less cost!"

To get a BrainMaker neural network to maximize returns on direct mail, several variables were fed into the network. The first objective was to see which variables were significant and to eliminate those that were not. Some of the more significant variables were:

  • Recency - the last time something was bought and registered, calculated in number of days. It is a known fact that the more recently you've bought something, the better the chance you're going to buy more.
  • First date to file - the date an individual made their first purchase. This is a measure of loyalty. The longer you've been a loyal customer, the better the chance is you're going to buy again.
  • The number of products bought and registered.
  • The value of the products bought and registered - figured at the standard reselling price.
  • Number of days between the time the product came out and when it was purchased. Research has shown that people who tend to buy things as soon as they come out are the key individuals to be reached.

Additional variables include information taken from the registration card including yes/no answers to various questions - scored with either a one or zero - areas of interest like recreation, personal finances, and such personal information as age, and whether an individual is retired or has children. Microsoft also purchased data regarding the number of employees, place of employment, as well as sales and income data about that business. While Microsoft has designed this neural network for their own specific needs, some of these inputs could be applied to any network.

Prior to training, the information taken from the response cards was put into a format the network could use and yes/no responses were converted to numeric data. Minimums and maximums were also set on certain variables.

Initially, the network was trained with about 25 variables. To make sure the data was varied, it was taken from seven or eight campaigns and represented all aspects of the business including the Mac and Windows sides, from high and low price point products.

The model trained for about seven hours before it stopped making progress. At that point, variables that didn't have a major impact were eliminated. This process was repeated. Currently the model is based on nine inputs. Jim Minervino explains some of the other training considerations: "During training I used 'modify size' and I used 'prune neurons'; as training completes, I used 'add neuron', and we did an experiment with 'recurrent operations' although in the net model we ended up using the default."

The output was a quantitative score from zero to one indicating whether an individual should receive or should not receive a second mailing. Minervino found that anybody scoring above .45 was more responsive to the mailing than anybody below.

The neural network was tested on data from twenty campaigns with known results not used during training. The results showed repeated and consistent savings. An average mailing resulted in a 35% cost savings.

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