<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incentive purchase. With enough purchase history, it is possible to predict which shoppers, when presented an offer, will buy a new item. 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.</p></abstract>

Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incentive purchase. With enough purchase history, it is possible to predict which shoppers, when presented an offer, will buy a new item. 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.

Prediction of the Shoppers Loyalty with Aggregated Data Streams

Department of Mathematical Methods in Economy, Vyatka State University, Kirov, Russia

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Prediction of the Shoppers Loyalty with Aggregated Data Streams"}

Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial...

Prediction of the Shoppers Loyalty with Aggregated Data Streams

Online veröffentlicht: 10. März 2016

Seitenbereich: 69 - 79

DOI: https://doi.org/10.1515/jaiscr-2016-0007

Schlüsselwörter
Big Data, data streams, data aggregation, classification and regression, shopping, loyalty, churn, marketing, business informatics

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Prediction of the Shoppers Loyalty with Aggregated Data Streams

Online veröffentlicht: 10. März 2016

Seitenbereich: 69 - 79

DOI: https://doi.org/10.1515/jaiscr-2016-0007

SchlüsselwörterBig Data, data streams, data aggregation, classification and regression, shopping, loyalty, churn, marketing, business informatics

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Big Data, data streams, data aggregation, classification and regression, shopping, loyalty, churn, marketing, business informatics