Machine Learning Approach for Cloud Data Analytics in IoT. Группа авторов

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СКАЧАТЬ in the retail industry. ML algorithms aid in identification of valued customers for a retail industry. These valued customers need to be retained by devising exciting and attractive strategies for success of any business entity. ML also enjoys the facility of customizing personalized customer’s view based on his likings and history [22]. Ability to provide user-oriented customer view further escalates its popularity in the retail industry. Similarly, it can also be utilized for predicting required stock so as to minimize involved risks and uncertainties [23]. The basic model of ML for predictive data analytics in the retail industry performs several functions. A sample description for same is as follows:

      1 It initially gathers the data from diverse sources related to products for training purposes.

      2 Thereafter, an algorithm is chosen to analyze the features of training data. Algorithms also precisely predict the product price.

      3 It is followed by prediction of the right price in comparison to real price of the product.

      4 ML algorithm continuously adjusts the prediction mechanism in order to minimize the gap between predicted price and actual price.

      5 This pre-training is followed by prediction of price of numerous products and a feedback loop is also considered to further enhance the accuracy of the model.

      6 To further refine the model, new product data is added to the system.

      The abovementioned steps are the basic steps to employ predictive data analytics in the retail industry. Few such examples of its applications are as follows [16]:

ML for Demand Prediction: ML uses high computations power to handle highly volatile data to predict the demand in future. For the same prediction, ML uses external and internal sources (structured, semi-structured, and unstructured data) of information so as to make informed decisions. This data may involve historical data, social media data, etc. Here, ML applies complicated mathematical algorithms to uncover hidden patterns in complicated and large datasets and thus provide reliable and accurate forecasts [24].

      ML for Predictive Sales Analytics: Another common application of predictive data analytics is to understand the driving motive behind customer’s purchase and their behavior under particular circumstances. Similarly, data from different sources is aggregated. The aggregated data is cleansed to determine the best forecasting algorithm for the current scenario. It then builds a predictive model to identify relationships among various factors. This is followed by monitoring the model to measure its accuracy with an objective to maximize its prediction accuracy.

      ML for Customer’s Customization: Using ML, recommendation engines are developed which give a customized view to each individual customer based on his likes and requirements. Provision of customized view ensures retention of customers to the same platform. Amazon has got the best recommendation engine which has significantly helped it to ace the competition.

      ML for Supply Chain Planning: As ML can be employed for predicting the future sales, it can also be utilized for maintaining hassle-free supply chain management despite involvement of several uncertain features. Moreover, it can also be used for optimized route planning for delivery of goods or warehouse maintenance. A route suggested by ML algorithms ensures to efficiently optimize cost, time, and carbon emission in comparison to humans.

      ML for price Optimization: ML algorithms can be employed for predicting the optimized price of products considering several factors like amount of discount, product type, competing retailers, and time dimension. Usage of ML in price optimization yields accurate predictions over traditional methods of price optimization. It can also be used for revenue forecasting during a particular month, quarter, or financial year [25].

      3.3.2 Use Cases

As discussed earlier, ML has been employed in several leading retail industries to sustain and excel in this cut-throat competing business world. Traditionally, managers would have predicted sales based on various factors like brand quality, promotion, and discount. Managers used to implement a series of regressions to predict the sales volume. The efficiency of such an approach heavily relied on the capability of the human brain. Traditional methods even led to inefficient forecasting. This inefficiency has been completely handled by incorporating ML approaches. In this subsection, authors discuss few popular use cases of ML employment in retail industries.

      According to [26], a ML model is devised to predict sales in response to promotion by a multinational retailer. An efficient model would enable to garner a huge leap in sales. Here, the retail company wanted to have an idea about the strongly and weakly performing products in the store. In the model, the company used several variables like discount, promotion duration, size of promotional advertisement, placement of products, and seasonality, among others. It was observed in [26] that a traditional method which involved several data analysts and a series of linear regression models predicted the results with 30% to 35% error rate. This error rate was brought down in the first attempt to 24% using ML model, and the error rate is expected to further reduce over time. Thus, integration of ML approaches in prediction models provided exciting and attractive results. It helped to curb the cost involved in generous promotions and maintaining inventory in the warehouse. Using the similar predictive model, Target Corp. also observed the growth of 15%–30% in revenue.

      A renowned retailer Walmart has also incorporated technologies to understand customers’ needs and act accordingly. The company employed facial recognition software to understand the experience level (frustration, happiness, and satisfaction) during checkout. It also triggers an alert for customer representatives to approach frustrated customers in order to provide better customer service. Usage of this facial recognition model eliminates the need of maintaining expensive and appropriate staff for providing enhanced customer service.

      Amazon has been proudly employing ML for predictive data analytics to enhance its sales. Amazon has garnered its outstanding benefit for demand prediction in business management [27]. It has also filed a patent for the process of its anticipatory shipping that predicts sales of a product in a particular region or city. Amazon uses this information to store the targeted products in nearest warehouses. It is also planning to deliver the product to the customers using drones in minimum time thus excelling the experience of shopping.

Authors in [28] have presented the implementation of predictive analytics in the retail banking sector. Here, authors claim that predictive analytics through traditional tools necessitates a specialized skill in statistics and mathematics. However, the same can be performed much easily in R, a language that includes around 4,000 algorithms of ML ranging from basic regression model to advanced model. In the banking sector, predictive analytics can be used to estimate churning rate and product propensity.

      3.3.3 Limitations and Challenges

      ML has observed widespread deployment in various domains including retail industry. In the retail industry also, it has been implemented for numerous purposes as discussed earlier. Despite its widespread deployment, it has some limitations and challenges. The major challenge is handling an ocean of data from diverse sources involving structured, semi-structured, and even unstructured data. This huge data collected from various sources is generally of poor quality, and therefore, efficient data cleaning methods need to be used to infer meaningful data [17]. Thereafter, it also has the challenge of maintaining stringent privacy and security policies for this huge data. It also has a challenge of acquiring trained and competent professionals who have vision of the future data requirement so as companies are able to draw useful insights from historical data.

3.4 Proposed Model

      In this section, authors propose a model for predictive data analytics in retail industries using ML approaches. Ahead of СКАЧАТЬ