- Tytuł:
- Prediction of the shoppers loyalty with aggregated data streams
- Autorzy:
- Nikulin, V.
- Powiązania:
- https://bibliotekanauki.pl/articles/91688.pdf
- Data publikacji:
- 2016
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
big data
data streams
data aggregation
classification
regression
shopping
loyalty
churn
marketing
business informatics
strumienie danych
agregacja danych
Klasyfikacja
regresja
zakupy
lojalność
maselnica
informatyka biznesu - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 2; 69-79
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł