Data Driven Decision Making: Case study for craft food marketplace

Background: A mid-sized retail company specializing in craft food and confectionery operates an online delivery marketplace. This multivendor platform connects small craft producers with consumers, allowing them to sell their products nationwide. Initially, the marketplace was built using Bubble for MVP 1 and MVP 2, but as the business scaled, they transitioned to a full-stack version 3. The company wanted to introduce advanced analytics and data-driven decision-making to help their producers maximize profits and stand out in a competitive market.

Challenge: To remain competitive and offer value to their vendors, the company needed to analyze customer behavior and product performance. The goal was to provide actionable insights through data-driven decision-making that would allow producers to optimize inventory, pricing, and marketing strategies in alignment with market trends and their business goals, ultimately achieving better outcomes.

Solution: The company, familiar with the speed and flexibility of low-code development, partnered with the Invental team to implement deep analytics using Directus. This low-code tool allowed them to quickly integrate data from multiple sources and create a comprehensive analytics platform tailored to their needs, facilitating data driven decision making.

Key Questions Addressed with Directus:

• Which craft foods are most popular by season and region? Understanding seasonal and regional trends helps producers plan production to meet demand through big data and data-driven decision-making.

• Which products are less popular and have a short shelf life? Identifying these allows for better inventory management and minimizes waste.

• Which items have a longer shelf life and can afford to wait for demand? Producers can plan production schedules accordingly.

• What marketing strategies (e.g., discount percentages, special offers) yield the best results? This allows for targeted marketing campaigns that drive sales.

• Which products see the highest return rates, and why? Understanding this helps in quality control and customer satisfaction.

• How does the time of day or week affect purchasing behavior? Insights drawn from metrics can optimize promotion timings.

• What impact does product placement on the site have on sales? This helps in designing the site layout for maximum conversions.

• How do different delivery options affect customer satisfaction and repeat purchases? This can inform logistics and customer service strategies.

Results: By implementing these data analysis techniques aligned with business goals, the company was able to optimize its operations significantly through data-driven decision-making. Through data-driven decision making, they adjusted their inventory management, reducing overstock by 15%. These positive outcomes not only minimized waste but also improved the overall profitability of the business by 10%. The ability to provide producers with actionable insights made the marketplace more attractive, leading to stronger partnerships and increased sales.

Conclusion: This case study highlights the power of low-code tools like Directus in transforming a business's data strategy, enabling data driven decision making. By aligning data analysis with business goals, the company was able to deliver tangible value to both its producers and its bottom line. This approach demonstrates the strategic impact of low-code development in driving business success.