Franchise Business Drivers Analysis
Franchise customers with low AI maturity looking to be a more data-driven organization
Unraveling Business Success Factors
Identify which behaviors to influence to optimize business performance and gain insights that are simultaneously developed across multiple features, variables, and may have complex interrelationships or dependencies.
Unveiling Revenue Generating Entity Clusters for Targeted Strategies
Embrace targeted actions tailored to address specific challenges, departing from the generic one-size-fits-all approach. Drive your business forward with customized strategies for optimal results.
Machine Learning for Data-Driven Decisions in Franchise Organizations
Establish an actionable and broadly applicable use for machine learning capability within the franchise organization, providing a foundation for developing and maturing data-driven decision-making.
How it Works
Ironside’s Franchise Business Drivers Analysis identifies key performance factors influencing franchise store performance or revenue value of a customer. Using the most important driving factors, it additionally creates clusters or segments of similar stores or customers to enable targeted business actions. DataRobot is used for insights visualization and machine learning modeling.
Module 1: Identify which factors/features most drive a business outcome, such as:
- Revenue per customer
- Profit margin of franchise locations for a large franchisor
Module 2: Based on the most important features discovered, perform a weighted clustering analysis to segment entities by drivers of the business outcome, such as:
- Segment consumers based on their traits which most influence revenue per customer
- Segment franchise locations based on their traits which most influence profitability
This solution introduces a degree of supervision to the unsupervised method of clustering. Based on these clustering results, businesses can develop operational protocols that seek to influence only the most important drivers of business outcomes, ensuring the most efficient data-driven decision making.
Key Deliverables
Summary
- Visualization of business drivers on revenue generation
- Clusters of similar types of revenue generating entities (stores or customers)
- Recommended actions for business to improve the store performance
Detailed artifacts and metrics
Data wrangling
- EDA artifacts for variable distributions and frequencies
- Anomaly detection
- Null imputation
ML Model prediction and insights
- Permutation Importances
- SHAP plot visualization
- Prediction endpoint
Clustering
- Outlier removal via various clustering algorithms
- Deploy clustering model to a production endpoint
- F-Test validation for statistically significant differences between clusters/segments
- Visualizations of statistically significant differences between clusters/segments
About the Partner
Ironside has extensive experience and expertise in franchise business model with multiple stores and/or a high volume of consumers. Ironside also possess strong machine learning and cloud technical talents to implement machine learning solutions. Here are some technical innovations by the team:
- Automated cluster discovery package (No need to dictate number of clusters or cluster density parameters before running clustering algorithms )
- Automated F-Testing to determine which factors/features truly differentiate clusters/segments from one another
Solution Diagram
Success Story
A major national hair salon franchise needed to understand why some of their store locations were performing well and some were having challenges. It was known that a variety of different factors and variables were potentially influencing store performance, but they needed to know the significance of each in order to prioritize operational improvement initiatives.
Ironside performed a business drivers analysis to examine factors including customer appointment no-shows, customer walk-in frequency, day of the week variations, holiday weekends, and more. The insights derived from this analysis led to specific plans to address the franchise labor scheduling practices, as well as salon-floor role management to improve store efficiency.
Ironside leverages the capabilities of DataRobot when developing a business drivers analysis, using advanced machine learning algorithms to get actionable insights from complex data. This solution measures and ranks the important factors driving outcomes as well as identifying categories or groups of revenue generating entities to enable targeted actions.
