Pacific First Aid
DIGITALIZATION PARTNERSHIP
Field
E-Commerce & Marketing
Stakeholders
Pacific First Aid Ltd.
Goal
Digitalization Process
Innovations and Key Achievements
- Revenue Growth: Through a series of strategic initiatives and optimizations, I contributed to a remarkable 68.9% increase in revenue, demonstrating my ability to drive tangible results and business growth.
- Leadership in Website Migration: I led the migration of our website to a new platform, significantly improving digital functionality and user experience.
- Diverse Digital Solutions: I implemented a wide range of digital solutions that spanned from process optimization to improved customer experience, ensuring that every aspect of our operations was aligned with best practices and available technologies.
Comprehensive Index for a Multiclass Classification Project
The purpose of this project is to showcase my capabilities through a track record of achievements gained in my work with Pacific First Aid, a company with which I continue to collaborate closely.
Throughout my time with the company, I have tackled numerous challenges and implemented a series of strategic improvements that have transformed the way we operate and connect with our customers.
Enhancements in Company Digitalization
E-commerce and Digital Platform:
- Redesign and Optimization of the E-commerce Store: I led the creation and enhancement of a new online store that not only improved the user interface but also streamlined the purchasing process, resulting in a smoother and more enjoyable experience for customers.
- Process Automation: I implemented a range of automations that streamlined internal processes, improving efficiency and reducing errors. These automations included inventory management, customer service, and order processing.
Digital Marketing:
- Development of Comprehensive Multichannel Marketing Strategies: I created and implemented comprehensive marketing strategies encompassing email marketing, SEO, PPC, and content marketing. These strategies not only strengthened customer loyalty but also significantly increased conversions.
- Automated Email Marketing Campaigns: I designed and launched automated email marketing campaigns tailored to customer needs and behaviors, improving open and conversion rates.
- Integration of New Marketing Tools: I introduced new digital marketing tools such as a loyalty points program, incentivizing repeat purchases and enhancing customer retention.
SEO and Speed Improvements:
- SEO Optimization: I conducted thorough SEO audits and implemented strategies to improve website rankings and visibility on search engines, resulting in increased organic traffic.
- Website Speed Optimization: I optimized website performance, reducing loading times and improving user experience, which contributed to higher conversion rates and lower bounce rates.
Inventory Optimization:
- Inventory Management Across Multiple Platforms: I developed strategies to keep inventory updated across multiple platforms, improving operational efficiency and reducing costs.
- Predictive Analysis: I used predictive analysis to identify purchasing patterns and segment customer groups. This allowed for personalized marketing strategies and more effective inventory management, ensuring popular products were always available.
Expansion of Online Presence:
- Optimization of Digital Presence: I expanded the company’s online presence by effectively managing platforms such as Google Search Console, Google Merchant, Google Business, and various social media channels. This increased brand visibility and attracted a larger number of potential customers.
- Management of Google Accounts: I managed all Google accounts, including Analytics and Ads, to improve brand visibility, monitor performance, and optimize campaigns for better results.
Tech Aspects Management:
- DNS, Cloudflare, Hosting, Domains: I managed all technical aspects such as DNS configurations, Cloudflare settings, hosting optimizations, and domain management to ensure smooth website operation, security, and performance.
Acquisition and Fundraising Initiatives:
- Fundraising Programs: I designed and implemented two fundraising programs that generated additional revenue and increased community engagement with our mission.
- Booking Tools Enhancement: I developed and implemented two new and improved booking tools that streamlined the booking process, resulting in increased bookings and improved customer satisfaction.
Innovations and Key Achievements:
- Leadership in Website Migration: I led the migration of our website to a new platform, significantly improving digital functionality and user experience.
- Sales Increase and Revenue Growth: Thanks to the various strategies and optimizations implemented, we achieved a 73% growth in sales over a two-year period, as well as a 20% increase in monthly revenue from our e-commerce platform and a 30% increase in Amazon sales.
- Diverse Digital Solutions: I implemented a wide range of digital solutions that spanned from process optimization to improved customer experience, ensuring that every aspect of our operations was aligned with best practices and available technologies.
Future Collaborations
I am open to establishing new collaborations with companies looking to optimize their operations through advanced data analysis techniques and digital transformation. My experience in these fields positions me as a valuable partner for any business seeking innovation and strategic objectives attainment.
Final Model Recommendation
Upon rigorous evaluation, Decision Tree models were excluded from consideration due to consistently inferior performance.
Although KNN and Logistic Regression models exhibited commendable metrics, they were set aside due to a notable disparity between testing and training accuracy, signaling potential overfitting.
Neural Network models faced elimination due to performance divergence and suboptimal metrics.
Exploring the utilization of SVM with feature selection presents a noteworthy possibility.
- Testing Accuracy: 1st position (0.9767).
- ROC AUC: 3rd position (0.9878).
- Mean Precision: 1st position (0.9720).
- Mean Recall: 1st position (0.9784).
- F1 Score: 1st position (0.9745).
- Training & Testing Tradeoff: 3rd position (0.0169).
While the training accuracy stands at an impressive 99.36%, the model delivers compelling results, securing notable positions in various metrics:
On the other hand, another option we can also use is Naïve Bayes with feature selection.
- Testing Accuracy: 6th position (0.9534)
- ROC AUC: 6th position (0.9779)
- Mean Precision: 11th position (0.9467)
- Mean Recall: 5th position (0.9569)
- F1 Score: 8th position (0.9490)
- Training & Testing Tradeoff: 2nd position (0.0085)
As observed, Naïve Bayes exhibits lower metrics compared to SVM; however, it demonstrates superior resilience against overfitting. This is evident in the minimal difference between our training and testing accuracy, all while maintaining commendable results.
By continuously tracking and evaluating their outputs, we can tailor our strategy to maximize the benefits derived from Naïve Bayes’ robustness against overfitting and SVM’s superior metric performance. This adaptive approach ensures a nuanced utilization of both models for optimal outcomes in diverse scenarios.
Improvement Analysis
Upon comprehensive review of the entire codebase, I’ve identified several areas where enhancements could significantly contribute to achieving superior results for our model. Let’s delve into these suggestions:
1. Data Split for Model Training: Given the relatively modest size of our dataset, optimizing the data split strategy becomes pivotal. Instead of the conventional 75/25 split, I propose adopting an 80/10/10 split for training, validation, and testing, respectively. This adjustment ensures a more robust training set while still allocating sufficient data for validation and testing:
- Training: 80% of the Total Dataset
- Validation: 10% of the Total Dataset
- Test: 10% of the Total Dataset
This modification provides a balanced approach, enhancing the model’s capacity to generalize effectively.
2. Improved Encoding Practices: A crucial aspect that requires attention is our encoding strategy. While some variables, like ‘year’ and the target variable, may be appropriately treated as ordinal, ‘island’ appears to be a nominal category. I recommend utilizing the One-Hot Encoder for the ‘island’ variable to accurately represent its categorical nature. This adjustment can significantly enhance the model’s understanding of this feature.
3. Fine-Tuning Hyperparameters: Throughout our hyperparameter tuning process, it became evident that certain parameters consistently hovered near the upper or lower bounds of our grid search thresholds. Although we opted not to redefine these hyperparameters to maintain project efficiency, a potential avenue for improvement is expanding the parameter range. By extending the search space for these critical hyperparameters, we may unlock additional performance gains and refine the model further.
4. Hyperparameters vs our Loss function: This analysis will help us identify the optimal balance between training and testing accuracy. This approach mirrors the successful strategy employed in our previous Neural Network model, where we continuously compared our Loss Function across epochs, effectively mitigating overfitting issues.
While these recommendations are not currently implemented in the existing codebase, they serve as valuable insights for potential improvements. As the primary focus shifts towards new data projects, the opportunity to implement these enhancements remains open. Should you wish to explore and practice with these suggestions to refine the model, feel free to incorporate them at your discretion.
Collectively, these proposed changes aim to elevate the model’s performance and contribute to a more accurate and robust outcome. If you decide to integrate these suggestions into your work, it could serve as an excellent exercise in refining the model’s capabilities!
Project Author Details
