Databases Reference
In-Depth Information
and services), there can be many ways to design a data warehouse for this industry.
The levels of detail to include can vary substantially. The outcome of preliminary
data mining exercises can be used to help guide the design and development of data
warehouse structures. This involves deciding which dimensions and levels to include
and what preprocessing to perform to facilitate effective data mining.
Multidimensional analysis of sales, customers, products, time, and region:
The
retail industry requires timely information regarding customer needs, product sales,
trends, and fashions, as well as the quality, cost, profit, and service of commodities.
It is therefore important to provide powerful multidimensional analysis and visual-
ization tools, including the construction of sophisticated data cubes according to the
needs of data analysis. The
advanced data cube structures
introduced in Chapter 5
are useful in retail data analysis because they facilitate analysis on multidimensional
aggregates with complex conditions.
Analysis of the effectiveness of sales campaigns:
The retail industry conducts sales
campaigns using advertisements, coupons, and various kinds of discounts and
bonuses to promote products and attract customers. Careful analysis of the effec-
tiveness of sales campaigns can help improve company profits. Multidimensional
analysis can be used for this purpose by comparing the amount of sales and the num-
ber of transactions containing the sales items during the sales period versus those
containing the same items before or after the sales campaign. Moreover, association
analysis may disclose which items are likely to be purchased together with the items
on sale, especially in comparison with the sales before or after the campaign.
Customer retention—analysis of customer loyalty:
We can use customer loyalty
card information to register sequences of purchases of particular customers. Cus-
tomer loyalty and purchase trends can be analyzed systematically. Goods purchased
at different periods by the same customers can be grouped into sequences. Sequential
pattern mining can then be used to investigate changes in customer consumption or
loyalty and suggest adjustments on the pricing and variety of goods to help retain
customers and attract new ones.
Product recommendation and cross-referencing of items:
By mining associations
from sales records, we may discover that a customer who buys a digital camera is
likely to buy another set of items. Such information can be used to form product
recommendations.
Collaborative recommender systems
(Section 13.3.5) use data min-
ing techniques to make personalized product recommendations during live customer
transactions, based on the opinions of other customers. Product recommendations
can also be advertised on sales receipts, in weekly flyers, or on the Web to help
improve customer service, aid customers in selecting items, and increase sales. Simi-
larly, information, such as “hot items this week” or attractive deals, can be displayed
together with the associative information to promote sales.
Fraudulent analysis and the identification of unusual patterns:
Fraudulent activity
costs the retail industry millions of dollars per year. It is important to (1) identify
potentially fraudulent users and their atypical usage patterns; (2) detect attempts
to gain fraudulent entry or unauthorized access to individual and organizational
