Masters Degrees (Industrial Engineering)
Permanent URI for this collection
Browse
Browsing Masters Degrees (Industrial Engineering) by Subject "Advertising, Retail"
Now showing 1 - 1 of 1
Results Per Page
Sort Options
- ItemPredicting the next purchase date for an individual customer using machine learning(Stellenbosch : Stellenbosch University, 2020-12) Droomer, Marli; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: We live in a world that is rapidly changing when it comes to technology. Gatheringa customer’s information becomes easier as companies have loyalty programs thattrack the customer’s purchasing behaviour. We live in an era where search enginessuggest your next word, online shopping is no longer scary, and people order aride by means of an application. The fact is that technology is evolving, andgathering information from customers is becoming easier. Given this change,the questions, however, are: How do companies use this information to gain acompetitive advantage? Do they use this information to benefit the customer?How can a company use customer information to give each individual a uniqueexperience?A research study was conducted to determine if an individual customer’s nextpurchase date for specific products can be predicted by means of machine learning.The focus was on fast-moving consumer goods in retail. This next purchase date canthen be used to individualise marketing to customers, which benefits the companyand the customer. In this study, the customer’s purchase history is used to train AbstractWe live in a world that is rapidly changing when it comes to technology. Gatheringa customer’s information becomes easier as companies have loyalty programs thattrack the customer’s purchasing behaviour. We live in an era where search enginessuggest your next word, online shopping is no longer scary, and people order aride by means of an application. The fact is that technology is evolving, andgathering information from customers is becoming easier. Given this change,the questions, however, are: How do companies use this information to gain acompetitive advantage? Do they use this information to benefit the customer?How can a company use customer information to give each individual a uniqueexperience?A research study was conducted to determine if an individual customer’s nextpurchase date for specific products can be predicted by means of machine learning.The focus was on fast-moving consumer goods in retail. This next purchase date canthen be used to individualise marketing to customers, which benefits the companyand the customer. In this study, the customer’s purchase history is used to trainmachine learning models. These models are then used to predict the next purchasedate for a customer-product pair. The different machine learning models that areused are recurrent neural networks, linear regression, extreme gradient boostingand an artificial neural network. Combination approaches are also investigated, andthe models are compared by the absolute error, in days, that the model predictsfrom the target variable.The artificial neural network model performed the best, predicting 31.8% of thedataset with an absolute error of less than one day, and 55% of the dataset withan absolute error of less than three days. The application of the artificial neuralnetwork as the Next Purchase Date Predictor is also demonstrated and shows howindividualised marketing can be done using the Next Purchase Date Predictor.The encouraging results of the Next Purchase Date Predictor showed that machinelearning could be used to predict the next purchase date for an individual customer.