1. 1 Profit Mining: From Patterns to Action Ke Wang, Senqiang Zhou, Jiawei Han Simon Fraser University

2. 2 Why Profit Mining? n A major obstacle in data mining application is the gap between: – statistic-based pattern extraction and – value-based decision making n Profit mining: – value-based data mining

3. 3 An Example n Suppose we want to maximize profit. Association rules [AIS93] {Perfume}->Lipstick (more often) {Perfume}->Diamond (more profit) do not suggest which items (and prices) to recommend to a customer who bought Perfume. n Similar problems with correlation, classification, etc.

4. 4 The Problem n Given: several transactions of form: – {,…, | }, for Item, Promotion code, and Quantity. | separates non- target items and target items. – { | } n Recommend target to customers who buy non-target items, to maximize profit.

5. 5 Not Prediction Problem n An example: – 100 customers each bought 1 pack for $1/pack. Profit=100(1-0.5)=$50. – 100 customers each bought 4 packs for $3.2/4-pack. Profit=100(3.2-2)=$120. n Prediction repeats the history. n Profit mining gets smarter from the history, by n recommending “right items” and “right prices”.

6. 6 Challenge I - notion of profit n Pure statistic approach favors – {Perfume}-> Lipstick n Pure profit approach favors – {Perfume}-> Diamond. n Profit mining considers: – both statistical significance and profit significance.

7. 7 Challenge II - customer intention n Mining On Availability (MOA): – Paying a higher price implies the willingness to pay a lower price. n { } -> can be extracted from transaction { | } n Recognizing this behavior brings new sales opportunities (at lower price).

8. 8 Challenge III - search space n Thousands of items, and much more sales. Any combination can trigger a recommendation. n Search at alternative concepts (food, meat, etc) and prices makes it worse.

9. 9 Step 1: generating rules n Association rules – {Diaper -> Beer}, supp=10%, conf=80% n Recommendation rules: – {g1,…,gk} ->, where gi is, or Item, or Concept. – { } -> – {FlakedChick.} -> – {Meat} ->

10. 10 Handle alternative concept and prices

11. 11 Step 2: building the model n We rank rules by the “average profit” made by the recommendation of a rule. – { } -> matches n t1: { | } (a hit) n t2: { | } ( a miss) – If the cost of Sunchip is $0.7, the average profit is $0.15. n To recommend, we select the matching rule of the highest possible rank.

12. 12 Step 3: Pruning the model n The model favors “high average profit” rules. n Such rules may bring a large profit. n Such rules may be random noise. n Cannot prune them simply based on statistical frequency.

13. 13 Pruning the model n We prune rules to increase the estimated profit on the whole population. n We organize rules into specificity tree: the parent is the highest ranked general rule of a child. n We cut off the tree to maximize the estimated profit.

14. 14

15. 15 Evaluation n Synthetic datasets: IBM synthetic data generator, modified to have price and cost. n 1000 items and 1000K transactions n For non-target item i: – cost(i)=c/i – price j=(1+j*10%)cost(i), j=1,2,3,4. n For target items: – Dataset I has 2 target items – Dataset II has 10 target items

16. 16 Profit Gain on Dataset I

17. 17 Hit Ratio on Dataset I

18. 18 Hit Ratio on Dataset I

19. 19 Profit Gain on Dataset II

20. 20 Hit Ratio on Dataset II

21. 21 Hit Ratio on Dataset II

22. 22 Conclusion n Proposed a new direction of data mining: Mining for profit. n Directly factor in business goal into data mining n Related work: microeconomic view of data mining [KPR98]