STATISTICA Sequence, Association, and Link Analysis
STATISTICA Sequence, Association and Link Analysis (SAL) is designed to address the needs of clients in healthcare, retailing, banking and insurance, etc., industries. It can be used for model building and deployment. SAL is an implementation of several state-of-the-art techniques specifically designed for extracting rules from datasets (databases) that can be generally characterized as “market-baskets.”
The market-basket problem assumes that there are a large number of products that can be purchased by the customer, either in a single transaction, or over time in a sequence of transactions. Such products can be goods displayed in a supermarket, spanning a wide range of items from groceries to electrical appliances, or they can be insurance packages which customers might be willing to purchase, etc. Customers fill their basket with only a fraction of what is on display or on offer.
Association rules can be extracted from a database of transactions, to determine which products are frequently purchased together. For example, one might find that purchases of flashlights also typically coincide with purchases of batteries in the same basket. Likewise, when transactions are time-stamped, allowing the analysts to track purchases.
Sequence analysis is concerned with a subsequent purchase of a product or products given a previous buy. For instance, buying an extended warranty is more likely to follow (in that specific sequential order) the purchase of a TV or other electric appliances. Sequence rules, however, are not always that obvious and sequence analysis helps you to extract such rules no matter how hidden they may be in your market-basket data. There is a wide range of applications for sequence analysis in many areas of industry and since including customer shopping patterns, phone call patterns, the fluctuation of the stock market, DNA sequence and web-log streams.
Once extracted, rules about associations or the sequences of items as they occur in a transaction database can be extremely useful for numerous applications. Obviously, in retailing or marketing, knowledge of purchase “patterns” can help with the direct marketing of special offers to the “right” or “ready” customers (i.e., those that, according to the rules, are most likely to purchase some specific items given their observed past consumption patterns).
However, transaction databases occur in many areas of business, such as banking, as well as general customer “intelligence.” In fact, the term “link analysis” is often used when these techniques — for extracting sequential or non-sequential association rules — are applied to organize complex “evidence.”
It is easy to see how the “transactions” or “market-basket” metaphor can be applied to situations where individuals engage in certain actions, open accounts, contact other specific individuals, and so on. Applying the technologies described here to such databases may quickly extract patterns and associations between individuals and actions, and hence, reveal the patterns and structure in datasets.