Meng Li Email and Phone Number

www.empiricus-analytica.com is a consultancy specialising in extracting tangible value from various data sources. We help retailers, tech start-ups and software companies make informed decisions by creating modern BI and Analytics solutions and improve efficiency using Machine Learning and AI. Areas of expertise include:- Analytics & Data Science consulting- Data extraction & API development- Machine Learning- Data Engineering- Web application backend development- Application UI/UX design- Coaching - Data strategy consulting

Recruited by the Group Director of National Buying, I developed a strategic approach that was centred on data-driven insights. As a team lead, I was responsible for supervising and mentoring a team of analysts, managing projects, and assigning tasks using Agile methodologies. By leveraging customer payment data and integrating it with credit card spending profiles, I gained a comprehensive understanding of customer behaviour, which allowed me to effectively diversify the company's offerings while maintaining competitive prices for key products. The quantifiable successes of my approach were evident in the following areas:- Delivered an assortment optimisation application using PySpark, D3, React, and Material UI, which has become a critical tool for category managers for product deletion/addition simulations, generating an average of $300k of annual incremental sales per decision.- Developed a price optimisation tool that utilised web scraping and price elasticity modelling in Python, unlocking $200 million in additional profit opportunities for the company.- Designed and implemented an insights reporting suite that answers specific business questions about category performance, customer behaviour, pricing strategy, and promotion returns-on-investment. The suite covers all aspects of the category management lifecycle, representing a time saving equivalent to 8 analysts across different departments, which translates to over $1.2 million in value per annum. The suite was developed using PySpark, D3, and React.- Migrated data ETL pipelines for the insights reporting suite from Hadoop to Azure using Databricks.

Nominated by the Head of Commerce, I was transferred to the Australian division to support business expansion. My key responsibility was to build a lean and capable Analytics team. During my short yet impactful tenure, I made the following contributions:- Provided pricing and ranging recommendations for over 25,000 products and simulated commercial scenarios for different pricing strategies using web scraping and financial modelling techniques.- Utilised my expertise in relational database design (SQL Server, Hadoop) and data visualisation (Tableau) to develop self-service key metric reports for category managers. This process cemented a fact-based product ranging process and prepared the team for tender negotiations.- Trained business users on data products and mentored team members on coding and analytics concepts. This effort improved the team's capabilities and increased overall efficiency.

After being promoted to the newly established Business Intelligence team, I had the opportunity to design, experiment, and implement large-scale data products across different business areas. My contributions to the Business Intelligence team enabled the company to gain valuable insights into customer behaviour and preferences, leading to increased sales and profitability. During my tenure, I achieved several significant accomplishments:- Drove sales by 12% through A/B testing of product placements and predicting association purchases.- Reactivated 20% of infrequent loyalty card holders by targeted promotions, utilising churn and customer lifetime value prediction trained on large transaction and loyalty datasets.- Materialised a 7% profit uplift by implementing dynamic pricing according to time, location, competition and customer demand etc in a 3-month trial period.- Developed the simulation module in the range optimisation application, assessing product substitution and cannibalization effects. This effort resulted in a 6% sales increase and a 3% cost reduction.

As part of the Schwarz Group, the world's fourth largest retailer, Kaufland is an international full-range supermarket chain that offers over 30,000 brand and private label products. I joined the company in a newly established analytics team that assists the category management team with shopper insights. Extracting from a diverse and voluminous set of data assets, my contributions were recognised with the following achievements:- Developed the first basket segmentation model in Scala, which fed into a real-time coupon recommendation engine, resulting in a 6% increase in repurchase rate and an 8% increase in incremental basket value.- Provided customer profiling insights for marketing strategy decisions through basket data and survey results.- Analysed consumer panel data to gain insights into customer share of wallet and spending potential.- Programmed category performance reports using R Shiny and Plotly, providing a visually engaging way to present complex data.