Database Reference
In-Depth Information
decision tree is so high that a more sensitive underlying algorithm won't alter an observation's
prediction values at all.
DEPLOYMENT
Richard's original desire was to be able to figure out which customers he could expect to buy the
new eReader and on what time schedule, based on the company's last release of a high-profile
digital reader. The decision tree has enabled him to predict that and to determine how reliable the
predictions are. He's also been able to determine which attributes are the most predictive of
eReader adoption, and to find greater granularity in his model by using gini_index as his tree's
underlying algorithm.
But how will he use this new found knowledge? The simplest and most direct answer is that he
now has a list of customers and their probable adoption timings for the next-gen eReader. These
customers are identifiable by the User_ID that was retained in the results perspective data but not
used as a predictor in the model. He can segment these customers and begin a process of target
marketing that is timely and relevant to each individual. Those who are most likely to purchase
immediately (predicted innovators) can be contacted and encouraged to go ahead and buy as soon
as the new product comes out. They may even want the option to pre-order the new device.
Those who are less likely (predicted early majority) might need some persuasion, perhaps a free
digital book or two with eReader purchase or a discount on digital music playable on the new
eReader. The least likely (predicted late majority), can be marketed to passively, or perhaps not at
all if marketing budgets are tight and those dollars need to be spent incentivizing the most likely
customers to buy. On the other hand, perhaps very little marketing is needed to the predicted
innovators, since they are predicted to be the most likely to buy the eReader in the first place.
Further though, Richard now has a tree that shows him which attributes matter most in
determining the likelihood of buying for each group. New marketing campaigns can use this
information to focus more on increasing web site activity level, or on connecting general
electronics that are for sale on the company's web site with the eReaders and digital media more
specifically. These types of cross-categorical promotions can be further honed to appeal to buyers
of a specific gender or in a given age range. Richard has much that he can use in this rich data
mining output as he works to promote the next-gen eReader.
