Name
Abstract | ||
|---|---|---|
For a consumer brand, the most valuable customers are those who return after this purchase. Therefore, we want to know if it is possible to predict which shoppers will buy a new item with enough purchase history. Fortunately, while dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers' transaction history to the periods of few months. As an outcome, we compress the given huge volume of data, and transfer the data stream to the standard rectangular format. Consequently, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISKE.2015.28 | 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) |
Keywords | DocType | ISSN |
Big Data,data aggregation,classification and regression,customer loyalty,business informatics | Conference | 2164-2508 |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Nikulin | 1 | 99 | 17.28 |
| Tian-Hsiang Huang | 2 | 50 | 6.12 |
| jiande lu | 3 | 1 | 0.36 |