Number of Employees: 150 - 300Jaffle Shop has now expanded to multiple cities and has a large and growing customer base.You’ve hired specialized teams for marketing, product development, operations, and customer support, each with their own data needs.You’ve also started offering a subscription service, allowing customers to receive regular deliveries of their favorite jaffles.You’ve raised a Series B round of funding and are focused on scaling the business to become a nationwide brand.
To enhance the shopping experience, you implement a recommendation engine that suggests jaffles based on
past purchases and browsing behavior.
This involves using machine learning algorithms to analyze user data and generate personalized recommendations.
To tailor your marketing efforts, you segment your customer base into groups based on demographics,
purchase history, and behavior.
This allows you to create targeted marketing campaigns and offers that are more likely to resonate with each segment.
To reduce customer churn, you implement a churn prediction model that identifies users likely to stop purchasing
from your site.
Analyzing user behavior data helps you identify patterns that indicate a risk of churn and create targeted
interventions, such as special offers or personalized messages, to retain these customers.
As your data volume and complexity grow, you need a robust ETL system to efficiently handle diverse data sources
and ensure accurate data processing.You also need to build comprehensive data models that reflect your businessprocesses, supporting more
sophisticated analysis and decision-making.
To enable use cases such as personalized recommendations and churn prediction, you invest in tools and infrastructure
for data scientists to develop and deploy machine learning models.
As your business handles increasing volumes of sensitive data you need to start working to ensure that your
data is secure and compliant with regulations such as GDPR and CCPA etc.