Predicting customer shopping lists from point-of-sale purchase data

Cumby, Chad; Fano, Andrew; Ghani, Rayid; Krema, Marko

doi:10.1145/1014052.1014098

Cited by 39 publications

(38 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…State-of-the-art methods [5,21,26,29] must fix the size of the predicted basket. Here choices of n = 5 or n = 10 are common; however, we found that this impedes the performance given the large assortment in our experiments.…”

Section: A3 Estimation Detailsmentioning

confidence: 99%

See 1 more Smart Citation

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Kraus

Feuerriegel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i. e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-ofthe-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers.In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns.(2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on stateof-the-art decision rules from the literature is outperformed by a factor of 4.0.

show abstract

Section: A3 Estimation Detailsmentioning

confidence: 99%

“…. , 30,35] Number of k -nearest neighbors [1,2,5,10,20] Table 6: Hyperparameters with tuning ranges. Note that the small number of hyperparameters contributes to the robustness of our approach, as well as our evaluation.…”

Section: Parametermentioning

confidence: 99%

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Kraus

Feuerriegel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…To solve this problem in the literature various methods have been adopted: collaborative filtering [20], Markov chains [21], supervised classification algorithms [22], deep neural networks [23], and temporal frequent pattern mining algorithms [24]. However, all these methods only exploit the temporal dimension but do not provide a way to understand how the time affects the customers decisions and which are the typical temporal shopping patterns.…”

Section: Related Workmentioning

confidence: 99%

Discovering temporal regularities in retail customers’ shopping behavior

et al. 2018

View full text Add to dashboard Cite

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer's temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.

show abstract

“…A popular task has been to generate personalized product recommendations based on customer transaction data. For example, Cumby et al [2004] predict customers' shopping lists using individualized classifiers that are trained using their recent transaction histories. The predictions are used to remind customers of forgotten products and to target promotions.…”

Section: Related Researchmentioning

confidence: 99%

PromotionRank

Nurmi

Salovaara

Forsblom

et al. 2014

ACM Trans. Interact. Intell. Syst.

View full text Add to dashboard Cite

We present PromotionRank, a technique for generating a personalized ranking of grocery product promotions based on the contents of the customer's personal shopping list. PromotionRank consists of four phases. First, information retrieval techniques are used to map shopping list items onto potentially relevant product categories. Second, since customers typically buy more items than what appear on their shopping lists, the set of potentially relevant categories is expanded using collaborative filtering. Third, we calculate a rank score for each category using a statistical interest criterion. Finally, the available promotions are ranked using the newly computed rank scores. To validate the different phases, we consider 12 months of anonymized shopping basket data from a large national supermarket. To demonstrate the effectiveness of PromotionRank, we also present results from two user studies. The first user study was conducted in a controlled setting using shopping lists of different lengths, whereas the second study was conducted within a large national supermarket using real customers and their personal shopping lists. The results of the two studies demonstrate that PromotionRank is able to identify promotions that are considered both relevant and interesting. As part of the second study, we used PromotionRank to identify relevant promotions to advertise and measure the influence of the advertisements on purchases. The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.

show abstract

Predicting customer shopping lists from point-of-sale purchase data

Cited by 39 publications

References 16 publications

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Discovering temporal regularities in retail customers’ shopping behavior

PromotionRank

Contact Info

Product

Resources

About