KT announced on the 28th that it has expanded the application of its 'personalized shopping service,' which shows different products based on household characteristics even when watching the same shopping channel on Olleh TV, to the W Shopping channel.

The personalized shopping service is a service that infers household members through big data analysis based on content consumption behaviors, such as broadcast and VOD viewing history of Olleh TV subscriber households, and broadcasts products optimized for each household's consumption preferences. It was first applied to 'K Shopping' in July 2015.



As a result of operating a customized shopping service on 'K Shopping,' monthly viewership increased by 31.8% and monthly purchase rates by 34.5% compared to general programming (4-month average). In other words, because products likely to be of interest to customers are selected and broadcast, both viewership and purchase rates increased compared to when all customers watch the same products.

Along with the expanded application of W Shopping’s personalized shopping features, a product recommendation method tailored to regional characteristics and household viewing time patterns has also been implemented. For instance, daily necessities and miscellaneous goods are primarily broadcast in remote island and mountainous areas where purchasing daily necessities is difficult, while local specialties are mainly featured in urban areas. In addition, households with housewives in their 30s watch the most optimized customized products during the afternoon hours, while single-person households in their 20s and 30s watch them during the late afternoon hours.

Song Jae-ho, Senior Vice President and Head of the Future Business Development Group at KT’s Future Convergence Business Promotion Office, stated, “The expansion of personalized shopping services will enhance viewer convenience and product purchase rates, while contributing to increased sales and market revitalization for home shopping operators.” He added, “We will continue to refine our household inference and product recommendation technologies to further improve customer convenience.”