Insights Generation and Customer Segmentation for Board Games Pub

Interactive Dashboard Powered By Machine Learning Increases Attendance, Food and Drink Sales at Board Games Pub


Bazinga Ludoclub is a pub in Catania, Italy, that specializes in board and video games. Established in November 2015, the members-only pub features food, a full bar, and more than 100 board games. In 2017, Bazinga had approximately 5,000 members.

Entrants are required to purchase a yearly membership and for each entrance pay a fee that covers the cost of all board and video games. Members also enjoy themed evenings, tournaments, and food and drink specials.


    • Dashboard for insights generation, customer segmentation and product recommendation.
    • 3 weeks: Scoping and Proof of Concept
    • 6 weeks: Working model
    • 3 months: Production deployment
    • Python, SQL Server, AWS, Tableau
    • K-means clustering
    • Association rule mining


Italian board games pub Bazinga Ludoclub wanted to increase customers attendance and sales, but lacked the capacity to take advantage of the data it had collected about customer purchasing habits.

The manager turned to, as a company specializing in developing bespoke machine learning solutions for business and academia.

The challenge

Improve pub’s revenue and attendance by using large amount of data

Bazinga asked to use the pub’s data to improve business performance, for example by increasing sales and attendance.
Due to Bazinga’s members-only business model, the manager had access to large amounts of data, including date and time of each customer entry and records of items sales.

The Decision-Making Process

Defining meaningful KPIs
Following a preliminary exploratory analysis of the data, the following KPIs were defined in order to consistently monitor the performance of the business:

  • Total profit margin across products
  • Average customer spending
  • Percentage of returning customers
  • Customers’ attendance by day of the week

Defining areas for improvement
To improve on the agreed KPIs, identified three main areas of intervention to focus on:

  • Insights about products: Use product performance data to drive strategic decisions about menu offerings. Linked KPI: Total profit margin across products.
  • Insights about customers: Identify different groups of customers and their preferences by segmenting customers according to their purchasing patterns. Linked KPIs: Average customers spending, percentage of returning customers.
  • Promotions and special offers:Recommendation of promotions and special offers tailored to specific customer segments and seasonalities, such as time of day and day of week. Linked KPIs: Average customer spending, customers’ attendance by day of the week.

The Solution

Interactive Dashboard leveraging Machine Learning to Deliver Product and Customer Insights and Recommend Promotions and Special Offers

To provide actionable insights in real-time, developed a dashboard that leverages machine learning models and interactive tables and charts. The dashboard supports the manager in all his strategic decisions:

  1. Insights about products: Using predictive analytics on products performance and data on profit margins, the dashboard delivers actionable insights on products performance. Managers can use this information to increase revenue by promoting high-margin items and replacing low-performing items.
  2. Insights about customers: The dashboard uses a clustering algorithm to segment customers based on their purchasing patterns. Managers can use this information to increase revenue by formulating specific product offerings for specific customers segments, and to better manage inventory by making more informed decisions about stocking.

3. Recommendations for promotions and special offers: using a Machine Learning-based recommendation algorithm, the dashboard suggests special offers and promotions likely to increase customers spending and net revenues for the pub.


Extract from the dashboard developed for Bazinga

Results created a user-friendly, interactive dashboard that delivers machine-learning based insights. The dashboard allowed the manager to tailor products, promotions, and special events around customer preferences, increasing revenue and customer satisfaction. As a result, different KPIs showed a substantial improvement in the business performance:

  • Total profit margin across products increased by 18%
  • Average spending per customer increased by €1.15
  • Returning customers increased by 12%
  • Attendance during the week (Sunday to Thursday) increased by 8%


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