Page 388 - Using MIS

356 Chapter 9 Business Intelligence Systems

of the customers having the next most recent orders are given an R score of 2, and so forth, down
to the last 20 percent, who are given an R score of 5.
The tool then re-sorts the customers on the basis of how frequently they order. The 20 per-
cent of the customers who order most frequently are given an F score of 1, the next 20 percent
of most frequently ordering customers are given a score of 2, and so forth, down to the least fre-
quently ordering customers, who are given an F score of 5.
Finally, the tool sorts the customers again according to the amount spent on their orders.
The 20 percent who have ordered the most expensive items are given an M score of 1, the next
20 percent are given an M score of 2, and so forth, down to the 20 percent who spend the least,
who are given an M score of 5.
Figure 9-16 shows sample RFM results. The first customer, Big 7 Sports, has ordered recently
and orders frequently. Big 7 Sports’ M score of 3 indicates, however, that it does not order the
most expensive goods. From these scores, the sales team can conclude that Big 7 Sports is a good,
regular customer and that it should attempt to up-sell more expensive goods to Big 7 Sports.
The second customer in Figure 9-16 could represent a problem. St. Louis Soccer Club has
not ordered in some time, but when it did order in the past, it ordered frequently, and its orders
were of the highest monetary value. This data suggests that St. Louis Soccer Club might have
taken its business to another vendor. Someone from the sales team should contact this cus-
tomer immediately.
No one on the sales team should even think about the third customer, Miami Municipal.
This company has not ordered for some time; it did not order frequently; and, when it did order,
it bought the least expensive items and not many of them. Let Miami Municipal go to the com-
petition; the loss will be minimal.
The last customer, Central Colorado State, is right in the middle. Central Colorado State is
an OK customer, but probably no one in sales should spend much time with it. Perhaps sales
can set up an automated contact system or use the Central Colorado State account as a training
exercise for an eager departmental assistant or intern.

Online Analytical Processing (OLAP)
Online analytical processing (OLAP), a second type of reporting application, is more generic
than RFM. OLAP provides the ability to sum, count, average, and perform other simple arith-
metic operations on groups of data. The defining characteristic of OLAP reports is that they are
dynamic. The viewer of the report can change the report’s format, hence the term online.
An OLAP report has measures and dimensions. A measure is the data item of interest. It
is the item that is to be summed or averaged or otherwise processed in the OLAP report. Total
sales, average sales, and average cost are examples of measures. A dimension is a characteristic
of a measure. Purchase date, customer type, customer location, and sales region are all exam-
ples of dimensions.
Figure 9-17 shows a typical OLAP report. Here, the measure is Store Sales Net, and the
dimensions are Product Family and Store Type. This report shows how net store sales vary by
product family and store type. Stores of type Supermarket sold a net of $36,189 worth of noncon-
sumable goods, for example.

Customer RFM Score
Big 7 Sports 113
St. Louis Soccer Club 511

Miami Municipal 545
Figure 9-16 Central Colorado State 333
Example RFM Scores