Association rules extraction for customer segmentation in the SMEs sector using the apriori algorithm

Data Mining applied to the field of commercialization allows, among other aspects, to discover patterns of behavior in clients, which companies can use to create marketing strategies addressed to their different types of clients. This research focused on a database, the CRISP-DM methodology was applied for the Data Mining process. The database used was that corresponding to the sector of SMEs and referring to customers and sales, the analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and on this model the grouping algorithms were applied: k -means, k-medoids, and SelfOrganizing Maps (SOM). To validate the result of the grouping algorithms and select the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers

New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm

Customer´s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k -means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty

Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified

Association rules in finance management

Association rules allow financial managers to find patterns between related events. These algorithms are used in the financial sector, especially in the field of finance management

A conceptual framework of a cloud-based customer analytics tool for retail SMEs

Since customers are seen as a strategic element in a company’s downstream supply chain, many retail organizations have been employing a customer-centric business strategy and started investing into such technologies and solutions known as customer analytics that are capable of processing huge amount customer data for enhanced decision making. Customer analytics has been of significant importance in most developed economies around the world particularly for large organizations. The off-the-shelf analytics solutions provided by vendors are perceived to be unmanageable, risky and unaffordable especially for Small and Medium Enterprises (SMEs) operating in retail sector. This becomes more vital for the SMEs in developing countries especially in the Eastern part of Europe where they constitute a noteworthy part of the economy. The majority of the SMEs in these countries lack of facilities, infrastructure and abilities to perform such analytical applications. Not being able to extract strategic knowledge using customer data is a missing component for them to be competitive and sustainable in the market from relationship marketing point of view. The aim of this paper is to propose a conceptual model that addresses this problem by providing retail SMEs with a cloud-based open platform for customer data analytics and knowledge extraction. The platform will be able to connect with numerous apps already employed at the retail SMEs, acquire customer data and then perform customer analytics in order to produce a rich set of reports and knowledge

Association pattern of students thesis examination using fp-growth algorithms

The thesis examination is the final project for students to graduate from their majors. This thesis researches scientific work between a student and a supervisor in finding solutions to a problem. In the thesis examination, students must present their research results to be criticized by the examiner. This article aims to analyze the association pattern of student thesis examinations at a private university. Although the thesis's implementation has been carried out following procedures, to determine the composition of the board of examiners needs to be analyzed by examining the pattern of relationships between research topics, supervisors, and examiners. This study uses 448 data and uses FP-Growth Algorithms to find the rules. The research methodology starts from preparing the Dataset, cleansing data, selecting data, loading data into applications, transforming data, itemset frequencies, forming patterns, and analyzing rules. This study found 145 patterns of association rules with a minimum support value = 4 and a minimum trust value = 50%. The association rule pattern of 77.78% is under scientific group data. The benefits of the association pattern produced in this study can determine the composition of the examiners on the student thesis examination according to the research topic and scientific field of the examiners

E-commerce website usability analysis using the association rule mining and machine learning algorithm

The overall effectiveness of a website as an e-commerce platform is influenced by how usable it is. This study aimed to find out if advanced web metrics, derived from Google Analytics software, could be used to evaluate the overall usability of e-commerce sites and identify potential usability issues. It is simple to gather web indicators, but processing and interpretation take time. This data is produced through several digital channels, including mobile. Big data has proven to be very helpful in a variety of online platforms, including social networking and e-commerce websites, etc. The sheer amount of data that needs to be processed and assessed to be useful is one of the main issues with e-commerce today as a result of the digital revolution. Additionally, on social media a crucial growth strategy for e-commerce is the usage of BDA capabilities as a guideline to boost sales and draw clients for suppliers. In this paper, we have used the KMP algorithm-based multivariate pruning method for web-based web index searching and different web analytics algorithm with machine learning classifiers to achieve patterns from transactional data gathered from e-commerce websites. Moreover, through the use of log-based transactional data, the research presented in this paper suggests a new machine learning-based evaluation method for evaluating the usability of e-commerce websites. To identify the underlying relationship between the overall usability of the eLearning system and its predictor factors, three machine learning techniques and multiple linear regressions are used to create prediction models. This strategy will lead the e-commerce industry to an economically profitable stage. This capability can assist a vendor in keeping track of customers and items they have viewed, as well as categorizing how customers use their e-commerce emporium so the vendor can cater to their specific needs. It has been proposed that machine learning models, by offering trustworthy prognoses, can aid in excellent usability. Such models might be incorporated into an online prognostic calculator or tool to help with treatment selection and possibly increase visibility. However, none of these models have been recommended for use in reusability because of concerns about the deployment of machine learning in e-commerce and technical issues. One problem with machine learning science that needs to be solved is explainability. For instance, let us say B is 10 and all the people in our population are even. The hash function’s behavior is not random since only buckets 0, 2, 4, 6, and 8 can be the value of h(x). However, if B = 11, we would find that 1/11th of the even integers is transmitted to each of the 11 buckets. The hash function would work well in this situation

Lumbar Sciatic Pain Clinical Pathways

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsTo provide a better experience to the clients, insurance companies are beginning to identify clinical pathways (clinical overlooks) of different non-oncological pathologies to deliver a better experience to their clients and reduce costs, by already knowing what needs to be done before, during and after a surgery, which may lead to time and money savings. This report presents a proposed approach to the lumbar sciatic pain pathology clients from the company where this internship was held. This internship had a duration of 12 months. The creation of clinical pathways for the lumbar sciatic pain aims to help stakeholders to take a step back and see what diagnosis bring patients to the need of a surgery, which procedures are done before and after the surgery, and the main surgical procedures in this pathology. Additionally, the costs are measured, and the main providers for each procedure are analysed, in order to provide a better experience to the clients, indicating what lies ahead, where they should go to get the best treatment, and how much they will pay. This approach was composed by a series of phases, such as business understanding, data understanding, cleaning and preparation, and modelling. There were biweekly and monthly meetings with health professionals and doctors to adjust some information that would be useful for the mentioned phases. In addition, a Predictive Model to identify which clients usually go through a lumbar sciatic surgery was built based on historical medical data, so that when clients come with specific diagnosis or already had certain pre-surgery procedures, identified in the main project of this internship, it is possible to be prepared for what will soon happen. The most important results obtained from this approach were the main pre- and post-surgery procedures, the principal surgeries performed, the costs of each procedure and of the main pathways (pre-, day, and post-surgery), which medical providers give the best results, and the types of clients that usually suffer from lumbar sciatic pain pathology