frequent itemset genration
TRANSCRIPT
Market Basket Analysis
A solution to your marketing analysis problems
Real business scenarios and
problemsWhy didn’t Ithought of this beforeIs it possible,
with the number of products we deal in
What can you do?
• Experiential Approach: Use your experiential learning
• Algorithms Approach: Try a new algorithm from one’s offered by geek squad
Experiential Approach• Product
decision• Managemen
t• Marketing• Employees• Salary• Handling
Competition
Too much to handle??
Algorithms Approach
Sounds even more
Troublesome??
Try Experts
• Pioneers in Marketing and Retail chain solutions
• We value our customers
• We believe in excellence
• Customized solution for problems
• We value your business like our brand
Last few Achievements
Partnered with 50 retails firms which are now enjoying status of being in fortune 500
Signed a deal with the largest retail chain giant “Falmart” for their global chain
Rated one of the best firms in Professional Retail Magazine for retail chain solutions
Used with client's permission
Market Basket Analysis
CB A
Problem at hand: Our distinct products:Product AProduct BProduct C
Output:Customer who buy A also buy B.
Solution:Try bundling A&BPlace the two on adjacent shelvesDiscounts on the package deal
A&B
Benefits of Implementation
All the products that are sold together Promotions Bundling offers Better inventory planning
ResultsHappy customer
Decent ratings
Long term relationship
Boost in sales Term
1Te
rm2Te
rm3Te
rm4
00.5
11.5
22.5
33.5
44.5
Profits
Profits
More Results
Customer Satisfaction
* Offers* Purchase Satisfactions
Better Inventory Management
* Low shelve time* Better planning
Boost in sales
* Product line expansion opportunity* Investment opportunity* …
• Profits• Custome
r base expansion
• Efficiency
Frequent Item-Sets item_1,
item_9, item_35, item_39, item_42
item_2, item_7, item_29
item_3, item_5, item_22
Thank You!
Appendix Association Rules
Apriori Algorithm Eclat FP-Tree
Apriori Algorithm for frequent item sets mining and
association rule learning over database of transactions.
Generate k level of all possible keys and then eliminate the k-1 level combinations from the rejected list for the k-1 level.
Perform the same recursively, to get the final output.
FP-Tree Efficient algorithm; Uses only two database
scans FP-Tree data structure Efficient implementation using hash tree