Application of customer relationship management and data mining in predicting customer purchase behavior in medium and large supermarkets in Kenya
The study topic was inspired by the growth in research dynamics for futurist organizations. Conventional research has concentrated on traditional methods that apply primary research and secondary research. One of the industries that has experienced rapid growth is the retail industry, so much that global brands like South Africa’s Shoprite have had a hard time penetrating the Kenyan market. Supermarkets are great custodians of big data which when mined can provide meaningful insights on basic organizational process as well as consumer behavior. The study thus sought to ascertain the extent of application of customer relationship management and data mining in predicting consumer behavior in medium and large supermarkets in Kenya. Random sampling was used for the study which was carried out amongst 30 supermarket staff in managerial cadre. Data was collected using selfadministered questionnaires and face to face interviews which were also done to get optimal responses from the respondents and analyzed using descriptive statistical measures like frequency distributions and percentages. The study established that while CRM is applied in the retail industry to some extent, basic data mining techniques are still popular for mining insights and are generally not specialized for data mining functions. It thus emerged that Excel is the most widely used software at 92% with usage being mainly for measurement of sales and customer purchase frequency. On sources of customer data and the respective applications, it was established that loyalty cards would ideally provide the most comprehensive customer data. In terms of usage, it emerged that the current retail technologies are used mainly to capture sales at 100%, measure customer purchase frequency at 93% and measurement of stock movement at 90%. However, various variables were not captured due to use of manual systems. Based on the findings various recommendations were drawn, key amongst them the need to invest in advanced data mining systems and diversification of data types that can be captured by such technologies including and not limited to customer demographics, customer complaint handling mechanisms and tracking of lapsed customers. These shall be steered further by use of systems that capture data comprehensively, at best this can be captured by loyalty cards whose usage can be increased through campaign programs by the various retailers. There is still a long way to go for the industry to grow and maximize its data mining potential. Consequent research may look to establish reasons for the low uptake of data mining technologies in the industry given its known potential for a higher return on investment (ROI).