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Data Mining for Sales Prediction
Data Mining for Sales Prediction

Abstract:-

Connected devices, sensors, and mobile apps make the retail sector a relevant testbed for big data tools and applications. We investigate how big data is, and can be used in retail operations. Based on our state-of-the-art literature review, we identify four themes for big data applications in retail logistics: availability, assortment, pricing, and layout planning. Our semistructured interviews with retailers and academics suggest that historical sales data and loyalty schemes can be used to obtain customer insights for operational planning, but granular sales data can also benefit availability and assortment decisions. External data such as competitors’ prices and weather conditions can be used for demand forecasting and pricing. However, the path to exploiting big data is not a bed of roses. Challenges include shortages of people with the right set of skills, the lack of support from suppliers, issues in IT integration, managerial concerns including information sharing and process integration, and physical capability of the supply chain to respond to real-time changes captured by big data. We propose a data maturity profile for retail businesses and highlight future research directions. Association Rules is one of the data mining techniques which is used for identifying the relation between one item to another. Creating the rule to generate the new knowledge is a must to determine the frequency of the appearance of the data on the item set so that it is easier to recognize the value of the percentage from each of the datum by using certain algorithms, for example apriori. This research discussed the comparison between market basket analysis by using apriori algorithm and market basket analysis without using algorithm in creating rule to generate the new knowledge. The indicator of comparison included concept, the process of creating the rule, and the achieved rule. The comparison revealed that both methods have the same concept, the different process of creating the rule, but the rule itself remains the same.

INTRODUCTION One of the challenges for companies that have invested a lot in consumer data collection is how to mine important information from their vast customer databases and product feature databases, in order to gain economical advantage. Several aspects of market basket analysis have been studied in academic literature, such as using customer interest profile and interests on particular products for one to one marketing, purchasing patterns in a multi-store environment to improve the sales. Market basket analysis has been intensively used in many companies as a means to discover product associations and base a retailer’s promotion strategy on them. A retailer must know the needs of customers and adapt to them. Market basket analysis is one achievable way to find out which items can be place together. Market basket analyses gives retailer good information about related sales on group of goods basis Customers who buys bread often also buy several products related to bread like milk, butter or jam. It makes sense that these groups are placed side by side in a retail centre so that consumers can access them promptly. Such related groups of goods also must be located side-by-side in order to remind customers of related items and to lead them through the centre in a logical manner. Market basket analysis determines which products are bought together and to design the supermarket arrangement, and also to design promotional campaigns. Therefore, the Market consumer behaviours need to be analysed which can be done during dissimilar data mining techniques. Well-versed decision can be made easily about product placement, pricing, endorsement, profitability and also finds out if there are any successful products that have no significant related elements. Similar products can be found so those can beplaced near each other or it can be cross- sold. 

BIG DATA Big data is an “imprecise description of a rich and complicated set of characteristics, practices, techniques, ethical issues, and outcomes all associated with data”. A more technical and complementary definition is “datasets that could not be perceived, acquired, managed, and processed by traditional [infor-mation technology] and software/hard-ware tools within a tolerable time” For companies, big data is a cornucopia of digitalized content about consumers’ cognitions, emotions, behaviors, and reactions critical to the ongoing data-driven industrial revolution. ‘Big data’ has different definitions that focus on size, range, and speed. While some incorporate a dynamic definition as beyond the ability of typical software, some use a static number as 100 terabytes or have a growth rate that is higher than 60% annually.

RETAIL CHALLENGES AND POTENTIAL BIG DATA APPLICATIONS

Although retailers were distributing products passively in the past, with the information about the end customer demand, they are becoming more proactive in the supply chain. The retail supply chain contains four major activities: assorting goods, breaking goods into smaller packages, holding inventory near the customer, and providing value-adding services such as gift wrapping and warranties. Agrawal and Smith mapped the supply chain of two major home furnishing retailers in the US and proposed a comprehensive set of planning processes: product design (private label merchandise) and assortment planning, sourcing and vendor selection, logistics planning, distribution planning and inventory management, clearance and markdown optimization, and cross-channel optimization. Major activities of retailing are to decide what products to carry (assortment, product design, procurement), how to sell products to customers (marketing, including pricing), and how to complete these efficiently (supportive functions, such as logistic planning)

ASSORTMENT, PRICING AND STORE LAYOUT Assortment is one of the most difficult tasks in the retail supply chain and has received high priority due to its impact on sales. Big data is used to micro-segment customers and optimize assortment. Common practices include analysis of the correlations between items purchased as well as time- and location- dependent purchase patterns. The output helps retailers understand customer preferences and compositions, and improve forecast processes

MINING ASSOCIATION RULES

Till now, we have looked at the Apriori algorithm with respect to frequent itemset generation. There is another task for which we can use this algorithm, i.e., finding association rules efficiently. For finding association rules, we need to find all rules having support greater than the threshold support and confidence greater than the threshold confidence. But, how do we find these? One possible way is brute force, i.e., to list all the possible association rules and calculate the support and confidence for each rule. Then eliminate the rules that fail the threshold support and confidence. But it is computationally very heavy and prohibitive as the number of all the possible association rules increase exponentially with the number of items.

SYSTEM MAINTENANCE AND EVALUATION

Types of Maintenance: • Corrective Maintenance • Adaptive Maintenance • Perfective maintenance • Preventive maintenance Corrective Maintenance :-

Even with the best quality assurance activities, it is likely that the customer will uncover defects in the software. Corrective maintenance changes the software to correct defects. Adaptive Maintenance :- Over time, the original environment (e.g., CPU, operating system, business rules, external product characteristics) for which the software was developed is likely to change. Adaptive maintenance results in modification to the software to accommodate changes to its external environment.

Perfective maintenance :- As software is used, the customer/user will recognize additional functions that will provide benefit. Perfective maintenance extends the software beyond its original functional requirements.

Preventive maintenance :- Computer software deteriorates due to change, and because of this, preventive maintenance, often called software reengineering, must be conducted to enable the software to serve the needs of its end users. In essence, preventive maintenance makes changes to computer programs so that they can be more easily corrected, adapted, and enhanced.

Evaluation: System evaluation provides framework for classification scheme to identifying sets of similar systems. The framework integrates previous studies on software evaluation, productivity models, software quality factors, and total quality models. It also classifies information about software systems from the perspective of the project, the system, and the environment.

CONCLUSION

Market basket analysis generates the frequent item set i.e. association rules can easily tell the customer buying behaviour and the retailer with the help of these concepts can easily setup his retail shop and can develop the business in future. The main algorithm used in market basket analysis is the Apriori algorithm. It can be a very powerful tool for analysing the purchasing patterns of consumers. The three statistical measures in market basket analysis are support, confidence. Support measures the frequency an item appears in a given transactional data set, confidence measures the algorithm’s predictive power or accuracy. In our example, we examined the transactional patterns of grocery purchases and discovered both obvious and not-so-obvious patterns in certain transactions. Association rules and the existing data mining algorithms usage for market basket analysis , also it clearly mentioned about the existing algorithm and its implementation clearly and also about its problems and solutions. Predictive modelling offers the potential for firms to be proactive instead of receptive. Predictive modelling using transactional data create particular challenges which need to be carefully addressed to develop valuable models. With MBA, leading retailers can drive more profitable advertising and promotions, attract more customers, increases the value of market basket and much more. Consumers, planners, merchandisers and store administrators have started to recognize how this new era of easy-to-use market basket analysis tools helps to work more intelligent and compete more successfully. Our future work would be to design and develop intelligent prediction models to generate the association rules that can be adopted on recommendation system to make the functionally more operational. Better and effective rule mining techniques can be used for better performance of the recommendation system. In future the same algorithm can be modified and it can be extended in the future work which also decreases the time complexity.

FUTURE SCOPE Since this system provides solutions to small supermarkets and is compatible to limited size of data there can be a wide range of scope possible 

• With stronger device storage and proper ram management devices it can hold data of larger supermarkets and can hold data for a longer duration

• Expanding the limitations will allow the user to directly access the data from the cash counter as the product is scanned from the barcode

• A system can be designed in which Using the history of the previous bills of the customer a new shopping list and list of recommendations can be provided for shopping next time, even the customer can manually edit the shopping list.  

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