Market segmentation approaches applied by small businesses in Kenya have mostly been based on very limited customer factors. This includes geographic, demographic, behavioral, and psychographic characteristics of the customer. These approaches have not entirely brought out the nature of the customers in the business. In some cases, the approaches have been based on incorrect assumptions and have also led to challenges like potentially ignoring new markets and difficulty in keeping up with changing customer needs. This research seeks to use a clustering algorithm to carry out market segmentation based on more variated and integrated data that could give more information on customer habits. The dataset that was used in this research contained integrated data on various customer and business facets. The research used the spectral co-clustering algorithm to bring out various traits on the business customers that could then be used to segment the customers into more effective markets. This research eventually brought out different market segments with varying characteristics all based on the best features in the integrated business data. This study showed that there is more data available in a business that can be used for market segmentation other than just geographical, behavioral, demographic and psychographic data. It also brought out the importance of feature selection in a dataset since different features may have different effects on sales on a business and the overall performance of a market segment. This study also contributes to research by identifying other features other than geographical, demographic, psychographic and behavioral factors that could be used to identify market segments in a small retail business. This approach is also more effective since the use of a clustering algorithm enables the discovery of patterns in business data that would not have been easy to spot with the naked eye, and the use of up-to-date business data aids the business in keeping up with customer habit changes and prevents missing out on potential markets.

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