Association Rule Learning is used to find relationships between items or events in large data sets. The primary goal of Association Rule Learning is to identify frequently occurring patterns in data to reveal hidden relationships.
How Association Rule Learning works?
Association rules are typically represented in the form of “If {A} then {B}”, where A and B are sets of items or events. The strength of an association rule is usually measured by three key metrics:
- Support: The proportion of transactions in the dataset that contain both A and B. A high support value indicates that the rule occurs frequently in the data.
- Confidence: The probability of B occurring given that A has occurred. A high confidence value means that if A is present, there is a high likelihood of B also being present.
- Lift The ratio of the observed support to the expected support if A and B were independent events. A lift value greater than 1 indicates that the occurrence of A and B together is more frequent than what would be expected if they were unrelated.
Use cases in Marketing Analytics
There are several applications of the Association Learning Rule, in the context of marketing analytics, listed below is a list of common use cases, their examples, and a very brief summary of how Association Rules are used in them.
| wdt_ID | Category | Use Case | Example | Association Rule |
|---|---|---|---|---|
| 1 | Cross-channel Marketing | Channel strategy optimization | Identifying effective combinations of marketing channels for engagement or conversions | If {Email, Social_Media, Paid_Search} then {High_Conversions} |
| 2 | Email Marketing | Content and offer optimization | Identifying effective content and discount code combinations | If {Product_A_Recommendation} then {Discount_Code_X} |
| 3 | Content Marketing | Content strategy optimization | Identifying popular combinations of blog topics to engage users | If {Blog_Topic_A, Blog_Topic_B} then {High_Engagement} |
| 4 | Social Media Marketing | Social media strategy optimization | Finding effective combinations of social media posts, hashtags, or influencers | If {Post_Type_A, Hashtag_X} then {High_Engagement} |
| 5 | Search Engine Optimization (SEO) | SEO strategy optimization | Identifying effective combinations of keywords and content types for organic traffic | If {Keyword_A, Content_Type_B} then {High_Organic_Traffic} |
| 6 | Landing Page Optimization | Conversion optimization | Analyzing the effectiveness of different landing page elements | If {Image_A, Headline_B, CTA_C} then {High_Conversions} |
| 7 | Affiliate Marketing | Affiliate strategy optimization | Identifying the most effective combinations of affiliate offers and traffic sources | If {Affiliate_Offer_A, Traffic_Source_B} then {High_Conversions} |
| 8 | Online Advertising | Ad strategy optimization | Analyzing the impact of different ad creatives and targeting options | If {Ad_Creative_A, Targeting_B} then {High_Clicks} |
| 9 | Customer Segmentation | Targeted marketing optimization | Analyzing associations between customer attributes and marketing responsiveness | If {Demographics_A, Browsing_Behavior_B} then {High_Engagement} |
| 10 | Product Recommendations | Personalization | Analyzing associations between products in an online store to inform recommendations | If {Product_A, Product_B} then {High_Likelihood_of_Purchase} |
Algorithms for Association Rule Learning
To get an idea of various algorithms based on Association Rule Learning, given below is a summary of such algorithms developed over the last 3 decades.
Association Rule Learning – Algorithms Summary
| Algorithm | wdt_ID | What it does | Developed by | Year of development | Total Citations |
|---|---|---|---|---|---|
| Apriori | 1 | Mines frequent itemsets using a breadth-first search | Rakesh Agrawal, Tomasz Imieliński, Arun Swami | 1993 | ~22,000 |
| Eclat | 2 | Mines frequent itemsets using a depth-first search | Mohammed J. Zaki | 1997 | ~3,000 |
| FP-Growth | 3 | Mines frequent itemsets without candidate generation | Jiawei Han, Jian Pei, Yiwen Yin | 2000 | ~12,000 |
| H-mine | 4 | Improves upon FP-growth using a hyper-structure approach | J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, D. Yang | 2001 | ~800 |
| RElim | 5 | Eliminates items recursively to mine frequent itemsets | Christian Borgelt | 2004 | ~200 |
| LCM | 6 | Mines closed frequent itemsets in linear time | Takeaki Uno, Tatsuya Asai, Hiroki Arimura | 2004 | ~400 |
| FARMER | 7 | Uses a matrix-based data structure to mine frequent itemsets | Roberto J. Bayardo Jr. | 2004 | ~100 |
| OPUS Miner | 8 | Discovers the top-K association rules with the highest overall utility | Geoffrey I. Webb, Shichao Zhang | 2013 | ~100 |