Meet Bloomchic

Bloomchic is an online fashion and lifestyle brand that was founded in 2016. The company is based in Singapore and primarily targets young female consumers in Southeast Asia. Bloomchic's core focus is on providing affordable yet trendy fashion items, including apparel, accessories, and beauty products. The brand aims to cater to the evolving style preferences of the millennial and Gen Z demographics by offering a curated selection of stylish, contemporary designs. In addition to its e-commerce platform, Bloomchic also has a strong social media presence, leveraging platforms like Instagram to engage with its customer base. The brand frequently collaborates with social media influencers to promote its products and connect with its target audience. Through its combination of on-trend fashion, accessible pricing, and digital-first marketing, Bloomchic has been able to establish itself as a popular fashion destination for young women in Southeast Asia. The company continues to expand its product range and geographic footprint to cater to the evolving needs and preferences of its growing customer base.

Octoparse CEM Analyzes Consumer Feedback for Personalized Recommendations and Product Optimization

It is difficult to apply the data to the actual marketing activities, unable to achieve refined marketing and personalized product recommendations, affecting the marketing effect.

Octoparse CEM aggregates data from multiple channels, analyzes consumer feedback, and helps marketing departments identify consumer needs, interests, and behaviors through comparative analyses of keyword clouds, indicator volumes, and typical opinions of competing products. Based on the above data insights, we can develop personalized recommendations and marketing programs; by understanding and improving the shortcomings of the product, we can make up for the shortcomings in product marketing. At the same time, according to the taste preferences and needs of different groups, real-time adjustment and optimization of the product. Octoparse CEM to achieve quality tracking and bad review warning, to promote the closed loop of user issues.

Manual collection of consumer evaluation, labeling, analysis, etc., low efficiency and limited coverage.

The granularity of the labels of common e-commerce platforms is not enough to support analysis, and manual reorganization is required; the workload multiplies during the promotion period.Octoparse CEM hourly collection + NLP fine granularity recognition can significantly improve the efficiency of the labeling; at the same time, it can open up various customer service systems to achieve real-time tracking of the quality of product service on different platforms and bad evaluation warning, for high concern/negative feedback can be automated work order flow, urging point-to-point problem closure. Help customer service team to put energy and time on consumer service innovation.