Business Strategy Prediction System for Market Basket Analysis

Jain, Sumit; Sharma, Nikita; Gupta, Sanket; Doohan, Nitika

doi:10.1007/978-981-10-5577-5_8

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…One of the most important advantages of the method presented in this article is the accuracy of the service provided to the customers. In addition, the implementation time of the method proposed in this study was moderate but not comprehensible for large and large spaces [12]. Their analysis is very slow and their production model is complex.…”

Section: Related Workmentioning

confidence: 96%

“…Until now, there are various methods for analyzing customers' portfolio such as the method of customer basket analysis based on their transaction records [8], customer basket analysis approach by process category [9], portfolio analysis approach. Customer Acquisition with Apriori Algorithm [10], Customer Basket Analysis Approach Using a Combination of Artificial Intelligence Techniques and Associated Laws and Minimal Spanning Tree [11], Customer Basket Analysis Approach with the Advance System Business Strategy Forecast [12], Improving the approach of customer basket analysis in an efficient way called feasibility Utility Mining [13], is provided.…”

Section: Introductionmentioning

confidence: 99%

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An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

Etebarian

Azmi

Dastjerdi

2020

Preprint

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Value Added Services at Mobile Communications Company provide customers with a variety of services. Value added services generate significant revenue annually for telecommunications companies. Providing solutions that can provide customers of a communications company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customers' carts and provide related services. Despite the many applications that these methods have, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile communications company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile communications company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, it has improved the accuracy of about 7%.

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Section: Related Workmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

Etebarian

Azmi

Dastjerdi

2020

Preprint

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“…Guidotti et al [42][43][44] defined a new pattern named the Temporal Annotated Recurring Sequence (TARS), which seeks to simultaneously and adaptively capture the co-occurrence, sequentiality, periodicity and recurrence of the items in the transaction sequence. Jain et al [45] designed a business strategy prediction system for market basket analysis. Kraus et al [46] proposed similarity matching based on subsequential dynamic time warping as a novel predictor of market baskets, and leverage the Wasserstein distance for measuring the similarity among embedded purchase histories.…”

Section: Related Workmentioning

confidence: 99%

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Chen

2021

Entropy

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Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

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“…In addition, a study by Panjaitan et al (2019) used MBA to develop promotional strategies in product bundling and packaging size changes. Research by Jain et al (2018); Kurniawan et al (2017) compiled an MBA-based transaction data processing system as a basis for companies to formulate their strategies. In this study, the MBA results were focused on as a basis for designing the facility layout.…”

Section: Introductionmentioning

confidence: 99%

Designing Animal Market Layout by Considering Consumer Purchase Behaviors

Setiawan,

Fadlika,

Qurtubi

2024

Spek Ind

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Sales transaction data contains rich information and can support company competitiveness. However, this transaction data is initially unstructured and needs to be processed into insight for the company's decision-making. Market Basket Analysis (MBA) is a data mining technique that can be used to study consumer purchasing patterns. This paper presents a case study on using an MBA to obtain consumer buying behaviors where the result of the MBA is then used to design a proposed layout. The animal market governed by Yogyakarta Province, known as Pasty Market, was used as a case study. Pasty Market is an animal trading center with around 30,000 square meters area and 255 sellers that sell various kinds of animals such as songbirds, dove birds, rabbits, cats, dogs, iguanas, turtles, ornamental chickens, and ornamental fish as well as animal food and cage. With this enormous area and number of merchants, the layout of Pasty Market becomes crucial in customer satisfaction. Association rules result in four priority levels in proposed layout planning, where these rules are used to determine the proximity among items in the proposed layout. These four levels of priority, ordered by the confidence value, are (1) “songbirds” and “bird food” (confidence value 91%), (2) “ornamental fish” and “turtles” (confidence value 80-90%), (3) “birdcages” and “songbirds” (confidence value 70-80%), and (4) “cats” and “dog” as well as “birdcages” and “birds” (confidence value 50-70%). Association rules were then used as the basis for determining the proximity value between merchants, where the proximity rules were then used for designing a proposed layout.

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Business Strategy Prediction System for Market Basket Analysis

Cited by 7 publications

References 3 publications

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

An Analytics Model for TelecoVAS Customers’ Basket Clustering using Ensemble Learning Approach

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Designing Animal Market Layout by Considering Consumer Purchase Behaviors

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