What is Product Clustering & why it’s important for your e-commerce analytics
Discover how product segmentation with e-commerce analytics provides insights. Learn the power of clustering for better retail decisions.
Five Bullet Friday: Karen Sheehy
Meet Karen Sheehy, mother, podcast-lover, and IT Coordinator at Conjura. Discover her interests in TV, podcasts, Git learning, and inspirations.
How Wild Cosmetics Revolutionised Their Stock Forecasting with Data
Wild Cosmetics optimized their stock forecasting using Conjura’s analytics platform. By integrating product and fulfillment data, they gained daily insights into SKU performance, improving their stock forecasting accuracy and pricing strategy. This enhanced their pick and pack operations, sped up fulfillment, and informed a data-driven discounting strategy, leading to better customer acquisition and retention.
We Raised €15 Million In Series A Funding
We raised €15 Million in Series A funding to enhance our e-commerce analytics platform and expand across international markets.
Five Bullet Friday: Bálint Biró
Meet Bálint Biró, Principal Engineer at Conjura. Discover his love for fantasy novels, crypto research, and projects.
Analytics Essentials
Founded in 2015, Legology is a beauty business built around a passion for looking after and celebrating great legs. The brand is dedicated to providing inclusive support for leg shape, comfort and health for all!
Five Bullet Friday: Nicky Moorhead
Meet a new Conjura team member. Discover his interests in finance, audiobooks, TV series, music, work projects, and inspirations.
4 Missed Opportunities in Due Diligence
Use a fine-tooth comb in due diligence for funding or acquiring a company. Granular, fresh, contextual data ensures successful deals.
5 Data Red Flags Keeping Potential Investors Away
Ensure your e-commerce success by leveraging data effectively. Investors demand clean, actionable data for informed funding decisions.
A Match Made in Data: 5 Mistakes to Avoid When Building Your Analytics Dream Team
Many businesses struggle with data analytics due to unrealistic team structures, suboptimal tools, lack of focus, poor hiring, and impatience.
