Product Segmentations of Children's Winter Accessories

Abstract

The objective of this study was to analyze sales data and provide better understanding of existing data of companies’ in the clothing industry. New clothing collection planning would be more precise, if analyzed findings from the historical statistical data could be more efficiently used. This study was carried out in cooperation with a Finnish children’s clothing company. Research data was collected between winter seasons 2013-2015. The Research focus was on a country level comparison for complete understanding of user age groups demand and purchase behavior. This study looks at synergies between cluster markets based on the existing secondary data, and looks at solutions for filling the gaps in where market potential exists. The Main data sources used in this study are the case company’s sales statistics, BHC marketing study, and statistics from child populations in research countries. The Study converts sales statistics into age statistics and analyzes these secondary data by following the life-style demands, birth rates, global trends and sales channels feedback in different market areas. Converting sizes to age also enabled seeing the user age curve clearly, while duplicate sizing between age groups are in use in the case company. Secondary data and Quantitative Method were used in this study. A comprehensive literature review from Product and Marketing segments, and Lifestyle combined with the secondary data findings, completed the product segmentation for children’s winter clothing in this study. The study results indicate that seasonality and market demands give frames for agile collection planning process in the clothing industry. However, there are no proper tools for collecting needed market demand data or it is in development stage in companies. Before efficient systems can be used, existing secondary data should be analyzed more efficiently. Existing data must be available and in usable form faster and more often for product teams. This way, agile and transparent product planning is achievable. Collected product segmentations are increasing in importance e.g. helping clear and systematic data to be comparable. Secondary data ought to be collected with clear focus, goal and defined segments. Data collection and main analysis ought to be automatically included in reporting. In clothing companies, where there is high brand loyalty among customers, analyzing only existing historical data, can increase performance. However, if a brand is not well known and does not have loyal customers, demand in the markets should not be predicted based on historical data only.

As a conclusion, the study discovered synergy factors in demands from cluster markets, which helps to develop new seasonal collections towards the correct markets with accurate size range, color pallet and product range. Cluster level differences are a good base for collection structure in the market needs of the case company. Adding country and cluster segmentations next to the product segmentations will improve transparency for product portfolio planning. Lifestyle affects customer demand, especially in children’s clothing. Offering service to a loyal customer base, based on users’ life cycle and lifestyle can create new potential for market growth. Product and market segmentations together create a base for efficient product planning in children’s winter accessories collections.