Icecat Service Roadmap 2024

By Vazha Abramishvili
February 28, 2024 at 9:00 am

In 2024, Icecat will continue to improve its services by adding new functionalities. You can see a summary of the planned developments in the Icecat Service Roadmap 2024.

Icecat Portal

Development	Value	Metrics for Evaluation	ETA
SEO: FO transfer to SSR	Performance Improvement: SSR can enhance page load times and overall performance by pre-rendering content on the server, resulting in faster initial rendering for users. SEO Optimization: SSR is beneficial for search engine optimization (SEO) as pre-rendered content is more easily crawled and indexed by search engines. User Experience (UX): Faster page loads contribute to a better user experience, reducing bounce rates and improving user satisfaction. Mobile Friendliness: SSR can lead to improved performance on mobile devices, enhancing the experience for mobile users. Scalability: SSR can enhance the scalability of the product by offloading rendering tasks to the server, reducing the client-side workload.	Page Load Time: Measure the time it takes for a page to fully load, emphasizing improvements achieved with SSR. Search Engine Rankings: Monitor changes in search engine rankings and visibility post-implementation of SSR. Mobile Page Load Time: Evaluate the time it takes for pages to load on mobile devices, considering the impact of SSR on mobile performance. User Satisfaction Surveys: Collect feedback from users through surveys to gauge their satisfaction with the improved performance. Scalability Testing Results: Assess the results of scalability testing to ensure that the product can handle increased loads efficiently	Q1 – Q2
FO Search Improvement	Accuracy: Provide accurate search results that match user queries, ensuring relevance and minimizing irrelevant or incorrect information. Speed: Optimize search performance to deliver fast and responsive results, enhancing the overall user experience. User-Friendliness: Design an intuitive and user-friendly search interface, allowing users to easily navigate and find the information they need. Comprehensiveness: Ensure that the search function covers a wide range of products and information within the Icecat database, offering a comprehensive search experience. Customization: Allow users to customize and filter search results based on specific criteria, enhancing personalization and usability.	Search Accuracy: Measure the accuracy of search results by evaluating how well they match user queries. Use FO Search query report for random manual check. Search Speed: Assess the speed of the search function, measuring the time it takes to return relevant results to users. User Satisfaction: Gather user feedback and satisfaction scores to evaluate how well the search function meets user expectations. Conversion Rate: Track the percentage of users who perform a search and then proceed to engage with products, indicating the effectiveness of the search function. Click-Through Rate (CTR): Measure the percentage of users who click on a search result, providing insights into the relevance and attractiveness of the results. Bounce Rate: Evaluate the percentage of users who leave the site after performing a search without further interaction, indicating the effectiveness of the search results. Query Volume: Track the volume of search queries over time to understand user engagement and demand. Search Result Personalization: Assess the effectiveness of personalized search results by analyzing user interactions and preferences. Mobile Responsiveness: Evaluate the search function’s performance and usability on mobile devices, ensuring a seamless experience for mobile users. Search Result Coverage: Evaluate the coverage of search results across different product categories and types to ensure comprehensiveness.	Q2-Q3
Extra services (upsells): – Reviews -Product Stories – etc.	Increased Revenue Opportunities: The introduction of extra services, such as Reviews and Product Stories, provides opportunities for upselling. Users may opt for these additional services, contributing to increased revenue for the platform. Enhanced User Engagement: Extra services add value to the user experience, leading to increased engagement. Users may find additional content like reviews and product stories valuable, resulting in longer session times and more frequent interactions. Improved Customer Satisfaction: Offering extra services enhances the overall customer experience, potentially increasing satisfaction. Users appreciate additional features that provide valuable insights, recommendations, and personalization. Competitive Differentiation: Introducing unique and compelling extra services sets the platform apart from competitors. It can be a key differentiator that attracts and retains users in a competitive market.	Upsell Conversion Rate: Track the conversion rate of users who choose to purchase or use the additional services. Revenue from Extra Services: Revenue generated from the sale of extra services. User Engagement Metrics: Evaluate user engagement metrics, such as time spent on the platform, interactions with extra services, and frequency of use. User Retention Rates: Retention rates of users who engage with extra services.	Q2-Q4
Show Testseek reviews on FO and export files	Comprehensive Product Information: Enhance product listings on Icecat by incorporating Testseek reviews, providing users with a more comprehensive understanding of product performance and quality and attract partners to get this information to their web-stores Competitive Advantage: Gain a competitive edge by offering a unique feature that sets Icecat apart from other product information platforms.	Review Inclusion Rate: Measure the percentage of product listings that include Testseek reviews. User Interaction with Reviews: Monitor user engagement with Testseek reviews, tracking interactions such as clicks, views, and time spent on review content. Conversion Rates: Evaluate the impact of Testseek reviews on conversion rates, measuring the percentage of users who make an upsell after interacting with the reviews. User Satisfaction: Collect feedback from users regarding their satisfaction with the inclusion of Testseek reviews. Export File Inclusion Rate: Measure the percentage of export files that include Testseek reviews.	Q2-Q4
Digital shelf analysis: – Improve of brand and category pages; – Country page; – Taxonomy specific completeness score; – etc.	Enhanced User Experience: Improve the overall user experience by providing a more organized and visually appealing presentation of products on brand, category, and country pages. (Need real user feedback) Competitive Advantage: Gain a competitive edge by offering a superior and more user-friendly digital shelf compared to competitors. Attract brand user with valuable insights: Offer information that will be greatly appreciated by users of the brand and contribute to their efforts in product distribution.	Page Engagement Metrics: Monitor metrics such as page views, time spent on pages, and interaction rates to evaluate user engagement. User Feedback and Satisfaction: Collect feedback from users to understand their satisfaction with the improved digital shelf features and identify areas for further enhancement. Competitive Benchmarking: Conduct regular benchmarking against competitors to evaluate the relative performance of Icecat.biz in terms of digital shelf features.	Q3-Q4

Icecat Brand Cloud (PIM)

Development	Value	Metrics for Evaluation	ETA
Shop users automatic assignment (COMPLETED)	Efficiency and Streamlining: Automating the assignment process eliminates the need for manual intervention, streamlining the onboarding of shop users to brands and reducing the time and effort required for this task. Enhanced User Experience: The automated assignment process provides a more user-friendly experience for both shop users and brand users, fostering smoother collaborations and interactions. Real-Time Collaboration: Automatic assignment ensures that shop users gain immediate access to the resources and functionalities associated with the brand, promoting real-time collaboration and engagement. Reduced Human Error: Manual processes are prone to human errors. Automation minimizes the risk of errors in user assignments, leading to more accurate and reliable data management.	Automation Adoption Rate: Track the percentage of authorization requests that are automatically accepted and result in the automatic assignment of shop users to brands. A higher rate indicates successful automation adoption. User Onboarding Time: Measure the time it takes for a shop user to be fully onboarded to a brand after the authorization request is accepted. A decrease in onboarding time signifies the efficiency of the automated process. Error Rates in Assignments: Track the occurrence of errors or issues in automatic user assignments. A low error rate demonstrates the reliability and accuracy of the automated process. Number of Manual Interventions: Compare the number of manual interventions required before and after the development. A decrease in manual interventions indicates successful automation and reduced workload.	Q1-Q2
Feature Embedding	Consistency: Ensure uniformity and consistency in feature values across different products, providing a standardized and seamless user experience. Benefit for partners. Efficiency: Streamline the process of embedding features by automating the extraction and import of data, saving time and resources. Benefit for editors. Accuracy: Guarantee the accuracy of embedded features to provide reliable and trustworthy information to users. Benefit for QA team. Data Integrity: Maintain the integrity of the data throughout the embedding process, avoiding errors or discrepancies. Benefit for QA. Customization: Allow for flexibility in selecting source products, enabling customization for different target products. Enhanced User Experience: Improve the overall user experience by enriching product information with relevant and detailed features. Competitive Advantage: Gain a competitive edge by providing in-depth and accurate product information, meeting or exceeding user expectations.	Data Completeness: Evaluate the completeness of data before feature embedding and after. Processing Time: Evaluate the efficiency of the embedding process by measuring the time taken to add the features manually and import features into target products. Data Consistency: Check for consistency in feature values across different products to ensure a unified presentation of information. User Engagement: Track user engagement with products that benefit from embedded features to gauge the impact on user interaction. Feedback and User Satisfaction: Gather feedback from users to understand their satisfaction with the embedded features and the overall product information.	Q2-Q3
Completeness score upgrade	Improved Maintainability: Migrating from Perl to PHP can enhance maintainability. Enhanced Flexibility: The introduction of flexible rule-setting allows for greater adaptability to different scenarios. Users can customize rules based on their specific needs. Customization for Partners: Partner-specific completeness scores enable individual partners to tailor the system to their unique requirements. This customization fosters stronger partnerships and provides a more personalized experience. Scalability: The development focuses on partner-specific scoring, suggesting scalability for diverse business relationships. This is valuable for systems that need to accommodate different partners with varying criteria for completeness. User Satisfaction and Adoption: If users find the new features intuitive, customizable, and beneficial, it contributes to higher user satisfaction and increased adoption of the completeness score functionality.	Feature Functionality: Ensure that the completeness score is calculated accurately in the new PHP version and produces the expected results. Partner-Specific Completeness Score: Verify that partners can easily set their own rules, and the completeness score reflects partner-specific requirements accurately. User Adoption: Collect feedback from users to gauge their satisfaction with the new features, rule-setting process, and partner-specific scoring. Monitor usage statistics to ensure that users are actively utilizing the new capabilities. Improved Maintainability: Availability of skilled developers for PHP compared to Perl. Reduction in the number of maintenance-related issues. Enhanced Flexibility: Positive feedback from users regarding the ease of rule-setting. Customization for Partners: Number of partners utilizing the customization feature.	Q1-Q2
Sub-user: – Brand users (brand assignment) – Icecat editors (managing accesses)	Efficient Brand Management: Streamlining the assignment of the main user to a brand and automatically subscribing all sub-users ensures efficient and centralized brand management. Comprehensive Performance Insights: The generation of statistics reports combining main user and sub-user data provides a holistic view of brand performance, enabling more informed decision-making. Enhanced User Collaboration: Sharing subscriptions seamlessly among main users and sub-users fosters collaborative work environments, promoting synergy and cooperation within the Icecat platform. Enhanced Access Control: The ability to manage user access to different parts of Icecat ensures that users have appropriate permissions, enhancing security and data integrity. Time-Saving Operations: One-click assignment of main users and sub-users reduces the time and effort required for user assignment, enhancing overall operational efficiency.	User On-boarding Time: Measure the time taken to onboard new users or assign existing users to brands before and after the implementation of the Sub-user logic. Access Requests and Approvals: Track the number of access requests and approvals to gauge the effectiveness of the enhanced access control features. Subscription Sharing Utilization: Monitor the usage of subscription sharing functionality to understand how often users take advantage of this feature. User Activity and Engagement: Assess user activity and engagement levels within the Icecat platform to ensure that the implemented features contribute to increased interaction. Operational Efficiency Metrics: Assess overall operational efficiency, including the time saved in user management tasks and the impact on other operational workflows.	Q2-Q3
– UK Energy Label & EU Energy Label – Create a system that will allow Icecat to add MMOs immediately without developers’ efforts	Compliance and Information Transparency: Ensuring adherence to regulatory standards with the inclusion of both UK and EU Energy Labels promotes transparency in energy information for products. Market Accessibility: The integration of Energy Labels expands market access by catering to both UK and EU markets. User Satisfaction: Providing all type of Energy Label formats: PDF and images. Agility and Scalability: The system allowing the addition of MMOs without developer efforts enhances agility and scalability. Editors and taxonomy teams can promptly incorporate new MMOs, responding quickly to market trends. Reduced Development Bottlenecks: Eliminating the need for developers to add MMOs streamlines the content management process, reducing bottlenecks and enabling editors and taxonomy teams to operate more independently	User Engagement with Energy Labels: Analyzing user downloads of Energy Labels and their formats. Time-to-Market for MMOs: Measure the time it takes to add new MMOs to the system. A shorter time-to-market indicates the efficiency of the system and the platform’s responsiveness. Error Rates in MMO Additions: Tracking the occurrence of errors or issues when adding new MMOs without developer involvement. A low error rate signifies the robustness of the system. Impact on Conversion Rates: Evaluate whether the addition of Energy Labels and MMOs correlates with changes in conversion rates, indicating the effectiveness of these elements in influencing partner decisions.	Q2-Q4
Automatic image cropping and standardization (COMPLETED)	Consistent Image Presentation: Ensures a uniform and consistent presentation of images, enhancing the overall visual appeal. Time Efficiency: Saves time for users who would otherwise manually crop images, allowing them to focus on other essential tasks. Enhanced User Experience: Improves the user experience by providing automatically cropped images, reducing the need for manual adjustments. Brand Consistency: Ensures consistency by automatically applying standard cropping, maintaining a cohesive visual identity. Optimized Storage and Bandwidth: Reduces image file sizes by eliminating unnecessary portions, leading to optimized storage and faster loading times. Increased Productivity: Contributes to increased productivity as users can efficiently manage and display images without the need for extensive manual cropping.	Auto-Crop Accuracy: Measure the accuracy of the automatic cropping algorithm in producing images that adhere to the standard. Evaluate false positives and negatives. User Adoption Rate: Track the adoption rate of the automatic cropping service among users. A higher adoption rate indicates the perceived value of the feature. User Satisfaction Surveys: Collect feedback through surveys to understand user satisfaction with the automatic cropping service and identify areas for improvement. Time Saved for Users: Estimate the time saved for users by comparing the time required for manual cropping to the time taken by the automatic cropping service.	Q1
ML: – Object detection – Attributes extraction – Translations – Categories definition	Enhanced Visual Understanding: Automatically detecting and identifying objects within images improves visual comprehension, allowing for more accurate categorization and analysis. Rich Product Information: Extracting attributes from images provides detailed product information, improving the completeness of product descriptions. Multilingual Support: Automatically translating text within images supports a global audience, ensuring that content is accessible and comprehensible across different languages. Improved Content Organization: Automatically defining categories of products enhances content organization, making it easier for users to navigate and find relevant information.	Object Detection Accuracy: Measure the accuracy of object detection algorithms by comparing detected objects against ground truth labels. Attributes Extraction Precision: Assess the precision of attributes extraction by comparing the extracted attributes against manually added data. Translation Accuracy: Evaluate the accuracy of translations by comparing automatically translated text with human-translated versions. Multilingual Coverage: Measure the coverage of languages supported by the translation service. Ensure that a diverse range of languages is effectively translated. Categories Definition Consistency: Evaluate the consistency of automatically defined categories by comparing them against predefined categories. Assess the percentage of correctly categorized products. Completeness of Product Information: Measure the completeness of product information extracted from images. Track the percentage of product listings with enriched attributes. User Feedback and Satisfaction: Collect user feedback on the quality and usefulness of ML-enhanced features. Error Rates and False Positives/Negatives: Track error rates, false positives, and false negatives for each ML application. Work to minimize errors and optimize algorithms based on feedback.	Q1-Q4
Possibility to assign more than one country to a user	Increased Operational Efficiency: Users can streamline their product management efforts by handling products for multiple countries within a single interface. This leads to increased operational efficiency and reduced time spent on managing separate accounts for each country or using International which provides extra noise for those countries which are not in the scope of this user. Improved User Flexibility: Users gain flexibility and convenience in managing products across various countries, contributing to an improved user experience and satisfaction.	Number of Countries per User: Track the distribution of users managing products for different numbers of countries to assess the adoption of multi-country management. User Satisfaction Surveys: User satisfaction scores obtained through surveys. User Adoption Rate: Percentage of users actively utilizing the multi-country management feature.	Q2-Q3
Validation on the Registration Page: – Brand validation; – Website validation.	Enhanced Data Quality: Ensure that registered brands and associated websites are accurate and valid. Improved User Experience: Streamline the registration process for users by guiding them to enter correct and valid brand and website information. Data Consistency and Standardization: Maintain a standardized format for brand and website information in the system, improving data consistency. Reduced Manual Intervention: Minimize the need for manual validation and correction of registration data by sales managers.	Registration Success Rate: Track the percentage of successful registrations after the implementation of brand and website validation. Error Rate: Measure the reduction in registration errors related to brand and website information. User Feedback and Satisfaction: Collect feedback from users regarding their experience with the registration process and the effectiveness of the validation. Support Ticket Reduction: Keep track of the decrease in support tickets or user inquiries related to registration issues. Data Accuracy and Consistency: Assess the improvement in the accuracy and consistency of brand and website information in the system.	Q2-Q3
Partner activity monitoring and insights (+moving user profile from Perl to PHP): – new users, current users login, users who do not login more than 3 month, etc; – feed upload; – PIF/PCF/XML/JSON/LIVE etc. downloads on graph for a last 12 months; – account manager dashboard with their partners; – using of other Icecat functionality. — Informing partners about state of their contract. — Dashboard for brands about their authorized resellers activity.	Enhanced Partner Engagement: Partner activity monitoring and insights provide a deeper understanding of partner engagement, allowing for tailored interactions and improved collaboration. Improved User Profile Management: Moving the user profile from Perl to PHP can enhance manageability, potentially leading to better performance and increased flexibility for user-related functionalities. Data-Driven Decision Making: Insights derived from partner activity data enable data-driven decision-making, allowing for strategic adjustments, feature improvements, and optimized partner experiences. Time Saving Tool: Saves account managers time comparing to creating and analyzing of DD reports.	Login Frequency and Patterns: Number of logins per partner and login patterns. Download Metrics: Monitor the usage patterns of different download types to identify popular data formats and optimize resources accordingly. Utilization of Icecat Functionality: Track how often partners utilize Icecat functionality to assess the relevance and effectiveness of features. Partner Activity Insights Adoption: Track the adoption rate of the insights feature to gauge its relevance and usefulness. Partner Satisfaction: User satisfaction scores obtained through surveys.	Q3-Q4
Upgrades of the Editor Assignment Tracking Tool to increase workflow efficiency + Allow users to upload SLA product lists	Increased Workflow Efficiency: Streamlining the Editor Assignment Tracking Tool can lead to quicker task assignments, improved collaboration, and reduced delays in product content updates. Enhanced Editors Experience: Providing editors with tools that make their tasks more efficient and user-friendly contributes to overall satisfaction and productivity. Efficient Collaboration with Partners: Enabling partners to upload SLA product lists promotes efficient collaboration by providing a streamlined process for editors to describe and manage the listed products. Improved Data Accuracy and Consistency: By allowing partners to upload product lists, there is a potential for increased accuracy and consistency in product descriptions, ensuring that the information provided aligns with partner expectations and requests. Enhanced Partner Experience: The feature enhances the experience for partners by providing them with a convenient method to submit product lists, contributing to smoother interactions with the editing team eliminating the need in intermediary (sales person, project manager).	Data Accuracy: Accuracy of data input and tracking within the tool. Collaboration Metrics: Evaluate collaboration metrics, such as the number of simultaneous users, concurrent task assignments, successful partner submissions, reduced communication barriers, and streamlined interactions. Partner Upload Frequency: Frequency of SLA product lists uploaded by partners. Editorial Efficiency: Assess whether the tool contributes to quicker and more efficient product description tasks for editors. Partner Satisfaction Surveys: Collect feedback from partners about their experience with the new feature and its impact on their collaboration with the editing team. Consistency in Partner Submissions: Ensure that partners consistently follow the expected format and provide accurate information in their uploaded product lists. Quality of Partner-Provided Data: Assess the quality and completeness of data provided by partners in their uploaded lists.	Q3-Q4
Injection of Icecat Studio in VC	Centralized Access: Provides users with a centralized access point to all Icecat services, simplifying navigation and improving user experience. Efficiency and Time Savings: Enhances efficiency by consolidating access to various services into a single tool, saving users time and reducing complexity. User Productivity: Boosts user productivity by eliminating the need to navigate multiple interfaces for different services, leading to a more streamlined workflow. Enhanced User Satisfaction: Improves user satisfaction by offering a more user-friendly and integrated experience, contributing to a positive perception of Icecat services.	User Adoption Rate: Track the adoption rate of the Injection of Icecat Studio among users. Higher adoption rates indicate that users find value in the centralized access. Number of Login Attempts: Monitor the number of login attempts through the Injection of Icecat Studio. A higher number suggests increased user engagement with the centralized access point. System Performance: Monitor the performance of the Injection of Icecat Studio to ensure it can handle the expected user load without significant degradation in response times. Security Audits: Conduct security audits to ensure that the Injection of Icecat Studio complies with security standards and does not introduce vulnerabilities.
New generation of Variants: user-specific content	Personalized User Experience: Enables the generation of user-specific content, providing a more personalized experience for individual users interacting with Icecat PIM. Targeted Marketing and Communication: Facilitates the creation of tailored marketing materials and communication strategies, allowing for more effective targeting of specific user segments. Improved Engagement and Interactivity: Enhances user engagement by delivering content that is specifically relevant to each user, encouraging increased interaction with the platform. Efficient Content Customization: Streamlines the process of creating user-specific assets, saving time and resources in content customization for diverse user needs. Competitive Advantage: Provides a competitive edge by offering a feature that responds directly to user preferences and requirements, potentially attracting and retaining more users.	User Adoption Rate: Track the adoption rate of the New Generation of Variants feature among users. User Satisfaction Surveys: Conduct user satisfaction surveys to gather feedback on the user-specific content feature. Use the feedback to understand user sentiments and identify areas for improvement.

Icecat Brand dashboard and reporting

Development	Value	Metrics for Evaluation	ETA
PCF type DD request (COMPLETED)	Enhanced Data Customization: Allow users to tailor reports more precisely by adding a PCF filter for report creation. Improved Decision-Making: Empower decision-makers to generate reports specifically based on PCF-type requests, leading to more informed and targeted decisions.	Report Customization Rate: Measure the percentage of users who utilize the PCF filter when creating reports. User Engagement: Monitor user engagement with the newly added PCF filter, tracking how frequently it is used. Request-Type-Specific Insights: Evaluate the depth and specificity of insights gained from reports generated with the PCF-type request filter. User Feedback and Satisfaction: Gather feedback from users regarding their satisfaction with the new PCF filter for report creation.	Q1
3rd “Questionnaire” email after registration (report visible in user profile)	Enhanced User Engagement: By sending a follow-up questionnaire, users are encouraged to actively participate and share additional information. This engagement fosters a sense of involvement and connection with the platform. Feedback and Insights: The questionnaire serves as a direct channel for obtaining user feedback. This valuable input can be used to identify areas of improvement, understand user preferences, and tailor future developments to better meet user expectations.	Survey Completion Rate: Measure the percentage of users who complete the questionnaire. A higher completion rate indicates greater user willingness to provide information. User Response Time: Assess the average time taken by users to respond to the questionnaire. A prompt response time suggests user interest and engagement. Qualitative Feedback Analysis: Evaluate the qualitative feedback received through the questionnaire to identify recurring themes, positive sentiments, or areas for improvement	Q2-Q3

Icecat Security

Development	Value	Metrics for Evaluation	ETA
Introducing encryption at rest for customer-sensitive data	Enhanced Data Security: Implementing encryption at rest enhances the security of customer-sensitive data, safeguarding it from unauthorized access and potential breaches. Compliance with Data Protection Regulations: Align with data protection regulations and industry standards by implementing encryption for sensitive customer information. Protection against Insider Threats: Mitigate the risk of insider threats by securing sensitive data even when it is stored within the organization. Customer Trust and Reputation: Build and maintain customer trust by demonstrating a commitment to the security and privacy of their sensitive data. Risk Mitigation: Reduce the risk of data breaches and the associated financial and reputational costs.	Encryption Coverage: Measure the percentage of customer sensitive data that is encrypted. Aim for comprehensive coverage to ensure that a significant portion of the data is protected. Compliance with Standards: Evaluate the compliance of the encryption implementation with relevant security standards and regulations (e.g., GDPR, HIPAA). Ensure that the encryption solution aligns with industry best practices. Performance Impact: Measure the impact of encryption on system performance. Assess factors such as latency, response times, and throughput to ensure that the encryption solution does not significantly degrade performance. User Authentication and Authorization: Assess the effectiveness of user authentication and authorization mechanisms related to accessing encrypted data. Ensure that only authorized users have the necessary credentials to decrypt and access sensitive information. Integration with Existing Systems: Evaluate the seamless integration of encryption at rest with existing systems and databases. Ensure that the implementation does not disrupt normal operations and data workflows.	Q2-Q4
2FA for users (Google Auth + email)	Diverse Authentication Options: Introducing 2FA with both Google Authenticator and email provides users with diverse options for enhancing their account security. Increased Security Resilience: Adding email-based 2FA alongside Google Authenticator enhances the overall resilience of the authentication process. User-Friendly Experience: Enhance the user experience by offering a seamless and user-friendly 2FA process that includes both Google Authenticator and email. Mitigation of Account Compromises: Strengthen account security and reduce the risk of unauthorized access or account compromises.	2FA Adoption Rate: Measure the percentage of users who opt to enable 2FA with both Google Authenticator and email. User Preferences: Collect data on user preferences for 2FA methods, understanding which method (Google Authenticator or email) is more widely preferred. User Feedback: Collect feedback from users regarding their experience with the new 2FA options.	Q3-Q4

Icecat Open Catalog Interface (APIs)

Development	Value	Metrics for Evaluation	ETA
Taxonomy data API (Category, Time period, Daily changes, etc.)	Improved Accessibility: The Taxonomy Data API enhances accessibility for clients by providing a streamlined and efficient way to access necessary taxonomy information. This reduces the need for clients to handle large files and simplifies the retrieval process. Operational Efficiency: Clients benefit from improved operational efficiency as they can easily integrate the Taxonomy Data API into their systems. This results in faster access to taxonomy information, reducing the time and resources required for manual file processing. Reduced Complexity: The development simplifies the integration process for clients, eliminating the need to work with large files and perform complex data processing. This reduction in complexity enhances the overall user experience. Enhanced Partner Relationships: Offering an efficient Taxonomy Data API strengthens relationships with clients by providing them with a convenient and effective solution for obtaining taxonomy information. This contributes to overall client satisfaction.	API Adoption Rate: Track the adoption rate to understand how many clients are utilizing the API as part of their operations. Number of API Calls: Volume of requests made to the Taxonomy Data API. Error Rates: Track the number of errors or issues encountered by clients to ensure the reliability and effectiveness of the API. Client Satisfaction Surveys: Satisfaction scores obtained through client feedback surveys. Integration Speed: Time taken by clients to integrate the Taxonomy Data API into their systems. Cost Savings: Assess whether clients experience cost savings in terms of time and resources by using the API instead of processing large files. Usage Patterns: Analyze trends in API usage to understand client preferences, peak usage times, and potential areas for optimization.	Q3-Q4
New Generation JSON (including INT JSON)	Enhanced User Experience: Multilingual JSON files provide a personalized and enhanced user experience by delivering content in the language specified in the user’s profile. This contributes to improved satisfaction and engagement. Global Reach and Localization: The development enables global reach by allowing users to receive content in their preferred language. It supports localization efforts, making the platform more accessible and appealing to a diverse audience. Improved Communication and Understanding: Users can better understand and engage with content when it is presented in their preferred language. This fosters clearer communication and a deeper connection between the platform and its users. Market Expansion Opportunities: By supporting multiple languages, the platform becomes more attractive to users from different regions, opening up opportunities for market expansion and attracting a more diverse user base.	User Satisfaction Scores: Satisfaction scores obtained through user feedback surveys. Global Market Reach: Increased user registrations and interactions from new geographic regions. Switching From XML: Number of users switched from multilingual XML	Q3-Q4
Personalized index files	Enhanced Partner Integration: The development allows for personalized index files tailored to each partner’s product lists, facilitating seamless integration with partner systems and improving overall collaboration. Increased Partner Satisfaction: Partners benefit from personalized index files, leading to a more tailored and efficient experience. This can enhance partner satisfaction and strengthen business relationships. Improved Data Accuracy and Relevance: The personalized index files ensure that the information presented to partners is accurate and relevant to their specific product lists, reducing errors and eliminating the need to process big index files for finding necessary products. Streamlined Product Data Distribution: The development streamlines the distribution of product data to partners by providing them with index files that directly align with their product lists, resulting in a more efficient data delivery process.	Partner Satisfaction Ratings: Partner satisfaction scores obtained through surveys. Integration Time Reduction: Average time taken for partners to integrate product data using personalized index files. Partner Adoption Rate: Number of partners actively using personalized index files. Reduction in Integration Issues: Number of reported integration issues or errors. Efficiency of Data Distribution: Time taken to distribute product data to partners using personalized index files. Enhanced Collaboration Metrics: Evaluate collaboration metrics, such as successful integrations, reduced support requests, and streamlined interactions.	Q3-Q4

Icecat Main Tech Stack and Security

Development	Value	Metrics for Evalutation	ETA
Reporting engine speed up	Improved Operational Efficiency: Enhances the speed and performance of the reporting engine, leading to quicker generation and retrieval of reports. This improves overall operational efficiency. Timely Decision-Making: Faster reporting enables timely access to critical data, facilitating quicker decision-making processes for users relying on the reports. Enhanced User Experience: Provides users with a more responsive and efficient reporting experience, contributing to overall satisfaction and engagement with the reporting system. Increased Productivity: Accelerating the reporting engine reduces the time users spend waiting for reports, allowing them to focus on more productive tasks.	Report Generation Time: Measure the time it takes to generate reports before and after the speed-up implementation. A significant reduction in report generation time indicates success. User Feedback and Satisfaction: Collect user feedback on the improved reporting engine speed. User satisfaction surveys and direct feedback will provide insights into the impact on user experience. System Performance: Evaluate the overall system performance with the optimized reporting engine. Monitor server loads, response times, and resource utilization to ensure system stability. Concurrency and Scalability: Assess the reporting engine’s ability to handle multiple concurrent requests. Ensure scalability to accommodate increasing numbers of users and reports without degradation in performance. Error Rates: Track any changes in error rates related to report generation. A decrease in error rates indicates improved stability and reliability.	Q1-Q2
Product merging re-engineering (COMPLETED)	Data Accuracy and Consistency: Improves the accuracy and consistency of product information by re-engineering the product merging process, ensuring that merged data is comprehensive and reliable. Enhanced Data Governance: Strengthens data governance by providing a structured and controlled approach to merging products, minimizing the risk of errors and inconsistencies in the merged data. Improved User Experience: Enhances the user experience by offering a more user-friendly and intuitive product merging mechanism, facilitating smoother interactions for users managing product information.	Feedback and User Satisfaction: Collect user feedback on the re-engineered product merging process. Use satisfaction surveys and direct feedback to understand user sentiments and identify areas for improvement. Error Rates: Monitor error rates related to the product merging process. A reduction in error rates indicates improved data integrity and a more reliable merging mechanism. System Performance: Monitor the overall system performance during and after the product merging process. Ensure that the re-engineering does not negatively impact system resources or response times.	Q1
Editor journal speed up	Improved Editorial Efficiency: Enhances the speed and performance of the editor journal and product history, leading to quicker and more efficient editorial processes. This improves overall editorial productivity. Reduced Workload and Waiting Times: Decreases the workload on editors by minimizing waiting times associated with journal tasks, allowing them to focus on content creation and editing without unnecessary delays. Enhanced User Experience: Provides editors with a more responsive and streamlined journal experience, contributing to overall satisfaction and engagement in the editorial process. Increased Editorial Throughput: Improves the overall throughput of the editorial team by enabling faster processing of tasks within the journal, facilitating the handling of a larger volume of editorial activities	Journal Task Processing Time: Measure the time it takes to process individual tasks within the editor journal. Compare processing times before and after the speed-up implementation. Editorial Workflow Efficiency: Assess the overall efficiency of the editorial workflow by tracking the time from task initiation to completion. A decrease in the time taken indicates improved efficiency. User Feedback and Satisfaction: Collect user feedback on the speed and performance improvements in the editor journal. User satisfaction surveys and direct feedback will provide insights into the impact on user experience. System Performance: Monitor the overall system performance during journal activities. Ensure that the speed-up implementation does not negatively impact system resources or response times.	Q1-Q2
Backup files to S3 storage	Data Resilience and Disaster Recovery: Enhances data resilience by backing up files to S3 storage, providing a reliable disaster recovery solution in case of data loss or system failures. Improved Data Transfer Speed: Speeds up data transfer by distributing data for US partners, optimizing the backup process and reducing the time required for data replication. Business Continuity: Ensures business continuity by having a robust backup strategy, minimizing the impact of potential data loss or disruptions to operations. Global Collaboration Efficiency: Facilitates efficient collaboration with US partners by distributing files, enabling quicker access to shared resources and enhancing overall partnership effectiveness. Reduced Downtime Costs: Minimizes potential downtime costs associated with data loss or recovery efforts by implementing a streamlined and accelerated backup process.	Backup Speed and Throughput: Measure the speed and throughput of the backup process to S3 storage. Compare backup times before and after the implementation of the new backup strategy. Recovery Time Objective (RTO): Assess the time it takes to recover data in case of a disaster. A lower Recovery Time Objective indicates improved disaster recovery capabilities. Data Transfer Rates: Monitor the data transfer rates for distributed files to US partners. An increase in transfer rates signifies the success of optimizing data distribution. Partner Collaboration Metrics: Assess collaboration efficiency with US partners by tracking the utilization of distributed files. Improved collaboration metrics indicate enhanced partnership effectiveness. Disaster Recovery Testing Success: Conduct disaster recovery tests and evaluate their success. A successful recovery from simulated disasters demonstrates the effectiveness of the backup strategy.	Q1-Q2
TQP 2.0: Conform to Kafka and Redis behavior	Improved System Integration: Enhances the integration of TQP 2.0 with Kafka and Redis, ensuring seamless communication and compatibility with these key components of the system architecture. Enhanced Data Processing Efficiency: Conforming to Kafka and Redis behavior with extended task life contributes to improved data processing efficiency, allowing for longer-duration tasks without disruptions. Reduced Latency in Task Execution: Minimizes latency in task execution by aligning TQP 2.0 behavior with the characteristics of Kafka and Redis, leading to quicker and more responsive task processing. Scalability and Performance Optimization: Supports the scalability of the system by optimizing the performance of TQP 2.0 when interacting with Kafka and Redis, ensuring a smooth and efficient scaling process.	Task Processing Time: Measure the time it takes to process tasks within TQP 2.0. Compare processing times before and after the development to ensure that task execution remains efficient. Latency Reduction: Monitor the reduction in latency in task execution. Compare the latency metrics before and after the implementation to ensure that tasks are processed more quickly. System Throughput: Assess the overall throughput of the system with TQP 2.0 conforming to Kafka and Redis behavior. Ensure that the system can handle a higher volume of tasks efficiently. Scalability Metrics: Evaluate the scalability of the system by measuring how well TQP 2.0 performs with increasing task loads. Ensure that scalability is maintained or improved. Task Life Duration: Measure the duration of tasks within TQP 2.0. Ensure that tasks can have an extended life without negatively impacting system performance or stability. Feedback from the Team: Gather feedback from the team regarding system performance, stability, and any observed improvements or challenges. Direct input from the team provides valuable insights.	Q1-Q2
Health monitor for icecat.biz (COMPLETED)	Improved System Reliability: Enhances the reliability of icecat.biz by implementing a health monitor, reducing the risk of downtime and ensuring continuous availability of the platform. Proactive Issue Identification: Enables proactive identification of potential issues or anomalies within icecat.biz, allowing for timely intervention and resolution before they escalate into critical problems. Enhanced User Experience: Contributes to an improved user experience by minimizing disruptions and ensuring that users can access and interact with icecat.biz seamlessly. Operational Efficiency: Streamlines operations by providing real-time insights into the health and performance of icecat.biz, allowing for more efficient monitoring and management. Reduced Downtime Costs: Minimizes costs associated with downtime by promptly addressing health issues, preventing prolonged service disruptions, and mitigating potential financial impacts.	Issue Detection Time: Evaluate the time it takes for the health monitor to detect and report issues within icecat.biz. Faster detection times contribute to proactive issue resolution. Resolution Time: Monitor the time taken to resolve issues identified by the health monitor. A shorter resolution time indicates efficient problem-solving and reduced impact on users. User Satisfaction: Collect user feedback on the perceived reliability and performance of icecat.biz. Higher user satisfaction scores indicate that the health monitor positively impacts the user experience. Alert Accuracy: Assess the accuracy of alerts generated by the health monitor. Ensure that alerts are relevant and reflective of actual system health, avoiding false positives. Resource Utilization: Monitor the utilization of system resources during regular operation and when issues are detected. Ensure that resource usage remains within acceptable limits. Scalability Metrics: Evaluate the scalability of the health monitor to accommodate the growth of icecat.biz. Verify that the monitor can effectively scale with increased system complexity and user load.	Q1
Moving Perl handlers to PHP	Improved System Performance: Moving Perl handlers to PHP can contribute to improved system performance, resulting in faster response times for requests related to images, PDFs, XML, and the repository. Standardization of Technology Stack: Standardizing on PHP for handling various types of requests aligns with modern development practices, simplifying the technology stack and making it more consistent. Enhanced Developer Productivity: Developers can benefit from working with a single technology (PHP) for handling diverse requests, potentially increasing productivity, and reducing the learning curve associated with multiple languages. Easier Maintenance: Consolidating handlers in PHP can make maintenance tasks more straightforward, as developers can focus on a single language and set of tools, reducing the complexity of code maintenance. Scalability: PHP is known for its scalability, and moving handlers to PHP can contribute to better scalability of the system, allowing it to handle increased loads more efficiently.	Response Time Improvement: Measure the improvement in response times for requests that were previously handled by Perl and are now handled by PHP. A reduction in response times indicates improved performance. Successful Migration Rate: Track the percentage of handlers successfully migrated from Perl to PHP. A high success rate indicates effective migration and adaptation of the codebase. Scalability Testing: Conduct scalability tests to assess the performance of the system with PHP handlers under increasing loads. Verify that the PHP implementation supports the platform’s scalability needs. Bug Reports and Fixes: Track the number of bug reports related to the PHP handlers and the time taken to address and fix reported issues. A low number of critical bugs and swift resolutions indicate success. Integration Testing Success: Assess the success of integration testing involving PHP handlers. Verify that the PHP handlers seamlessly integrate with other components of the system.	Q2-Q4
Refresh auto-tests	Reliability of Automated Testing: Enhances the reliability of auto-tests, ensuring that automated testing accurately reflects the functionality and behavior of the Icecat platform. Efficient Test Execution: Improves the efficiency of test execution, reducing the time required to run auto-tests and providing faster feedback on the code changes. Early Issue Detection: Facilitates early detection of issues by running comprehensive and up-to-date auto-tests, enabling the identification of potential issues before they reach production. Reduced Manual Testing Effort: Reduces the need for extensive manual testing by automating repetitive and routine testing scenarios, freeing up resources for more complex testing tasks. Accelerated Development Cycles: Contributes to faster development cycles by streamlining the testing process, allowing developers to iterate more quickly and release features with confidence.	Test Coverage Improvement: Measure the improvement in test coverage after refreshing auto-tests. An increase in test coverage indicates a more comprehensive testing suite. Test Failure Rates: Monitor the rates of test failures before and after the refresh. Bug Detection Rate: Track the rate at which auto-tests detect bugs or issues. Regression Testing Success: Assess the success of auto-tests in detecting regression issues. Verify that the refreshed tests effectively catch regressions introduced by new code changes.	Q2
Improve data retrieval speed: use level 1 cache	Improved Data Retrieval Speed: The implementation of a level 1 cache will significantly enhance the speed of data retrieval for product data and dictionaries, providing users with faster access to information. Enhanced User Experience: Faster data reading directly translates to an improved user experience, reducing wait times and increasing overall system responsiveness. Scalability Support: Cache provides a scalable solution for storing product data and dictionaries, supporting the platform’s growth without compromising performance.	Average Data Retrieval Time: Measure the average time taken to retrieve product data and dictionary information before and after the implementation of the level 1 cache. System Response Time: Evaluate the overall response time of the system for various data retrieval operations. Ensure that the implementation of the level 1 cache contributes to a reduction in system response time. Cache Warm-up Time: Measure the time required for the cache to “warm up” after a system restart or cache flush. A shorter warm-up time indicates quicker availability of cached data.	Q2-Q3
Update project to PHP 8.3	Enhanced Performance and Efficiency: Upgrading the project to PHP 8.3 can lead to improved performance and efficiency, benefiting the overall responsiveness of the Icecat platform. Security Improvements: PHP 8.3 updates often include security enhancements and fixes, contributing to a more secure environment and protecting against potential vulnerabilities. Compatibility with Modern Libraries and Tools: Ensures compatibility with the latest libraries, frameworks, and development tools that leverage features introduced in PHP 8.3, promoting a more modern and capable development ecosystem. Maintenance Ease: Up-to-date projects are typically easier to maintain and support. Updating to PHP 8.3 can lead to a codebase that is more aligned with current standards, reducing technical debt. Developer Productivity: Developers can benefit from new language features, improvements, and optimizations in PHP 8.3, potentially enhancing productivity and enabling the adoption of more efficient coding practices.	Successful Migration Rate: Track the percentage of the project successfully migrated to PHP 8.3. A high success rate indicates effective migration and adaptation of the codebase. System Stability: Monitor system stability after the update. Assess the frequency of errors, crashes, or unexpected behaviors compared to the pre-update state. Compatibility Testing: Conduct compatibility tests with third-party libraries, frameworks, and tools to ensure seamless integration. Measure the success rate of compatibility testing. Developer Adoption of New Features: Gauge the extent to which developers adopt and leverage new features introduced in PHP 8.3. Increased adoption indicates successful knowledge transfer and utilization of improvements.	Q2
Data base optimization: DB temp, MyISAM table	Improved Database Performance: Optimizing the MyISAM tables in the database leads to improved performance, ensuring faster data retrieval and more efficient query processing. This contributes to a more responsive and scalable system. Enhanced Backup and Dump Processes: The optimization of MyISAM tables aims to make backup and dump processes easier and more streamlined. This improves the efficiency of data backup, ensuring data resilience and quicker recovery in case of failures. Data Security and Integrity: The optimization process enhances data security by reducing the risk of data corruption. It contributes to the overall integrity of the database, providing a more reliable foundation for critical business operations. Resource Utilization Efficiency: Optimizing MyISAM tables contributes to better resource utilization within the database system. This can result in reduced storage requirements and improved utilization of system resources, leading to cost savings. Reduction of Technical Debt: The optimization initiative helps in reducing technical debt within the database architecture. It eliminates inefficiencies and outdated practices, making the system more maintainable and adaptable to future changes.	Backup and Dump Duration: Evaluate the time required to perform backups and dumps of the database both pre and post-optimization. A reduction in backup and dump duration reflects improved efficiency in these processes. Storage Space Utilization: Monitor the storage space utilized by the database before and after optimization. A reduction in storage space indicates more efficient data storage and potential cost savings. Data Recovery Time: Measure the time required to recover data in case of a database failure before and after optimization. A decrease in data recovery time enhances the system’s resilience. Developer Feedback: Gather feedback from developers involved in database-related tasks regarding the impact of optimization on their workflows and system interactions. Positive feedback indicates successful integration.	Q2
MMO REST rewriting	Enhanced Multimedia Content Delivery: The rewriting of MMO REST aims to enhance the delivery of multimedia content, ensuring that users can efficiently access and retrieve PDFs, certificates, videos, product stories, and other multimedia objects. Improved Content Accessibility: By optimizing the MMO REST functionality, the platform can provide improved accessibility to multimedia content, enabling users to seamlessly incorporate diverse media assets into their workflows. Scalability for Media Handling: The rewritten MMO REST functionality should offer scalability for handling increasing volumes of multimedia content, supporting the platform’s growth without compromising performance. Modernized Multimedia Data Architecture: Aligning MMO REST with modern standards ensures a robust and future-proof architecture for handling multimedia data, enhancing compatibility with evolving technologies and industry practices. Developer Productivity with Multimedia Integration: A well-designed and rewritten MMO REST for multimedia content can improve developer productivity by providing clear and consistent interfaces for integrating various types of media assets.	Scalability in Multimedia Handling: Conduct scalability tests to evaluate the performance of MMO REST in handling a growing volume of multimedia content. Ensure scalability for diverse media types. Developer Feedback on Multimedia Integration: Gather feedback from developers regarding the integration of multimedia content using the MMO REST API. Positive feedback indicates improved developer experience with diverse media assets. Compatibility with Multimedia Standards: Verify that the rewritten MMO REST adheres to relevant industry standards and best practices for handling multimedia data. Ensure compatibility with commonly used multimedia formats and protocols.	Q3-Q4
Re-engineering coverage from file to PHP	Improved Performance and Efficiency: Re-engineering coverage from file to PHP aims to enhance the performance and efficiency of generating coverage reports, providing faster and more responsive access to information. Scalability and Flexibility: Re-engineering the coverage process in PHP should offer scalability and flexibility, allowing the system to handle increased coverage requests and adapt to evolving requirements without compromising performance. Ease of Integration: The new PHP-based coverage system aims to be easily integrable into existing workflows and systems, providing a seamless experience for users who rely on coverage reports for decision-making. Maintenance and Supportability: PHP-based re-engineering contributes to improved maintenance and supportability, making it easier to troubleshoot issues, apply updates, and ensure the ongoing reliability of the coverage generation process.	Report Generation Time: Measure the time taken to generate coverage reports in PHP compared to the previous method. A reduction in report generation time indicates improved efficiency. Scalability Testing: Conduct scalability tests to evaluate how well the PHP-based coverage system handles increased coverage requests. Verify that the system remains performant under various load conditions. Error Rates and Issue Resolution Time: Monitor error rates associated with the PHP-based coverage generation process. Measure the time taken to identify and resolve issues, aiming for a decrease in error rates and efficient issue resolution. User Satisfaction and Adoption: Collect user feedback on the new PHP-based coverage reports. Measure user satisfaction and adoption rates to gauge the success of the re-engineering effort in meeting user needs. Maintenance Effort and Update Frequency: Measure the effort required for ongoing maintenance of the PHP-based coverage system. Track how frequently updates are applied to address new requirements or improvements	Q3
PCF service speed up and re-engineering up to the standards	Faster Service Response Time: Speeding up the PCF service contributes to faster response times, enhancing user experience and satisfaction. This is particularly crucial for services that directly impact user interactions. Improved Scalability: Re-engineering the PCF service up to the standards ensures improved scalability. The service should efficiently handle increased workloads and user demand without compromising performance. Enhanced Reliability: The re-engineering efforts contribute to enhanced reliability by addressing any existing performance bottlenecks or issues. A more reliable service leads to increased trust among users. Efficient Resource Utilization: Optimization of the PCF service results in more efficient resource utilization. This can lead to cost savings and a more sustainable infrastructure for delivering the service. Compliance with Industry Standards: Bringing the PCF service up to industry standards ensures that it aligns with best practices and compliance requirements. This can be crucial for maintaining trust and meeting regulatory obligations.	Service Response Time Improvement: Measure the improvement in service response time after the speed-up and re-engineering efforts. Scalability Testing Results: Conduct scalability testing to assess how well the re-engineered PCF service handles increased loads. Ensure that the service scales effectively without degradation in performance. System Throughput: Monitor the overall throughput of the PCF service before and after the re-engineering. Ensure that the service can handle a higher volume of requests without experiencing performance bottlenecks. Error Rates: Track the error rates or service disruptions before and after the re-engineering. A decrease in error rates indicates improved service reliability. System Availability: Measure the overall availability of the PCF service. Ensure that the re-engineering efforts contribute to increased uptime and availability for users. User Satisfaction Surveys: Conduct user satisfaction surveys to gather feedback on the perceived improvement in service performance. Positive feedback indicates success in enhancing user experience. Incident Response Time: Track the time taken to respond to and resolve incidents related to the PCF service. Faster incident response times indicate improved service management.	Q3
Feature REST new version	Enhanced API Performance: The development of the new version of Feature REST aims to enhance the performance of the API, providing faster and more efficient access to product features and related information. Scalability and Flexibility: The new version should offer scalability, allowing the system to handle increased requests for product features, and flexibility to adapt to evolving requirements without compromising performance. Improved Data Retrieval: Users should experience improved data retrieval capabilities through the Feature REST API, ensuring that they can efficiently access and utilize product feature information. Modernized Architecture: Aligning Feature REST with modern standards contributes to a more robust and future-proof architecture, enhancing compatibility with evolving technologies and industry best practices. Developer Productivity: A well-designed new version of Feature REST can improve developer productivity by providing clear and consistent interfaces for accessing and managing product feature data	API Response Time: Measure the response time of the new version of Feature REST API compared to the previous version. A decrease in response time indicates improved efficiency. Throughput: Assess the throughput of the Feature REST API, measuring the volume of requests it can handle per unit of time. Ensure that the new version supports increased throughput. Scalability Testing: Conduct scalability tests to evaluate the performance of the new version under increasing loads. Ensure that it supports the platform’s scalability needs. Developer Feedback: Gather feedback from developers regarding the usability and clarity of the new version of Feature REST API. Positive feedback indicates improved developer experience.	Q4
Improving data base structure: IntModelName table migration from Product Table to Model Name table	Improved Database Structure: Migrating the `IntModelName` table from the `Product` table to the `Model Name` table aims to optimize the overall database structure. This leads to a more efficient and organized data schema. Enhanced Query Performance: The migration contributes to enhanced query performance by separating model name-related data into its dedicated table. This optimization reduces the complexity of queries and improves overall database responsiveness. Simplified Data Retrieval: The migration simplifies data retrieval processes by consolidating model name information in a dedicated table. This streamlines data access and retrieval tasks for applications and services interacting with the database. Scalability Improvement: Optimizing the architecture through the migration supports scalability. A well-structured database is more scalable, allowing for efficient handling of increasing data volumes and evolving system requirements.	Query Execution Time Improvement: Measure the improvement in query execution time for operations related to model name data. A decrease in query execution time indicates successful optimization. Database Response Time: Monitor the overall response time of the database for various operations before and after the migration. Ensure that the migration contributes to a more responsive database. Data Retrieval Efficiency: Evaluate the efficiency of data retrieval processes for applications relying on model name information. Confirm that the migration simplifies and accelerates data retrieval. Application Performance Metrics: Gather performance metrics from applications interacting with the database. Evaluate any improvements in application response times and overall system performance. Developer Feedback: Collect feedback from developers regarding their experience with the optimized architecture. Positive feedback indicates that the migration simplifies development tasks and improves workflow.	Q4
Re-engineering brand statistics up to the standards	Enhanced Data Accuracy: Re-engineering brand statistics up to standards aims to improve the accuracy of brand-related data, ensuring that statistical information reflects the most current and reliable insights into brand performance. Standardization of Metrics: The re-engineering effort intends to standardize the metrics used in brand statistics, providing consistency and comparability across different brands. This ensures a unified approach to measuring and analyzing brand performance. Improved Performance and Efficiency: The re-engineering process should lead to enhanced performance and efficiency in generating brand statistics, providing faster access to critical insights and reducing processing times.	Standardization Rate: Evaluate the degree of standardization achieved in brand statistics metrics. Measure how well the re-engineering effort aligns different metrics to ensure consistency. Performance Enhancement: Measure the performance gains achieved through re-engineering. Assess the reduction in processing times and improvements in generating brand statistics reports. User Satisfaction: Collect feedback from users, analysts, and stakeholders regarding the usability and effectiveness of the re-engineered brand statistics. Positive feedback indicates improved user satisfaction.	Q4
Improving PIM session handling (from Perl to PHP)	Enhanced Performance and Responsiveness: The improvement in PIM session handling, transitioning from Perl to PHP, aims to enhance overall system performance and responsiveness, providing users with faster and more efficient session management. Seamless Integration with Modern Technologies: The transition to PHP ensures seamless integration with modern technologies and aligns the back-office session handling with contemporary web development practices, contributing to long-term system compatibility. Scalability and Flexibility: Improving session handling in PHP is designed to offer scalability, allowing the system to efficiently handle increased user loads and adapt to evolving requirements without compromising performance. Developer Productivity: The shift to PHP improves developer productivity by providing a familiar and widely adopted programming language for session handling, streamlining development processes and reducing learning curves. Maintenance and Supportability: The move to PHP contributes to improved maintenance and supportability, making it easier to troubleshoot issues, apply updates, and ensure the ongoing reliability of PIM session management.	Scalability Testing: Conduct scalability tests to evaluate how well the PHP-based session handling system performs under increasing loads. Ensure that it remains responsive and efficient. Developer Feedback: Gather feedback from developers regarding the usability and clarity of the PHP-based session handling. Positive feedback indicates improved developer experience and ease of integration. Integration Success: Measure the success of integrating the PHP-based session handling into the existing PIM system. Ensure that the integration process is smooth and aligns with user expectations. Maintenance Effort and Update Frequency: Measure the effort required for ongoing maintenance of the PHP-based session handling. Track how frequently updates are applied to address new requirements or improvements. Security in Session Management: Conduct security assessments to ensure that the PHP-based session handling maintains security standards. Monitor and address any potential vulnerabilities or risks introduced during the transition.	Q4
Clean up unused Perl code	Enhanced Code Maintainability: Cleaning up unused Perl code contributes to enhanced code maintainability by removing unnecessary and redundant components. This makes the codebase more manageable and easier to understand for developers. Reduced Technical Debt: The removal of unused Perl code reduces technical debt within the system. It eliminates unnecessary complexities, making the codebase cleaner and more efficient for future development and maintenance. Improved System Performance: Eliminating unused code can result in improved system performance by reducing the overhead associated with unnecessary functionalities. This contributes to faster execution and resource optimization. Easier Debugging and Troubleshooting: A cleaner codebase makes debugging and troubleshooting more straightforward. Developers can focus on relevant code, leading to faster issue resolution and a more efficient development process. Enhanced Security: Removing unused Perl code reduces the attack surface and potential vulnerabilities in the system. This contributes to enhanced security by minimizing the potential points of exploitation.	Codebase Size Reduction: Evaluate the reduction in the overall size of the codebase after removing unused Perl code. A smaller codebase is often easier to manage and results in improved system performance. Build and Compilation Time: Measure the time it takes to build and compile the system before and after the cleanup. A reduction in build and compilation time indicates improved efficiency. Bug Fixing Time: Track the time taken to fix bugs or implement new features before and after cleaning up unused code. A decrease in bug fixing time suggests that the cleanup has positively impacted development speed. System Performance Metrics: Monitor system performance metrics, such as response times and resource utilization, before and after the cleanup. Ensure that system performance is positively impacted by the removal of unused code. Security Scans and Vulnerability Assessments: Conduct security scans and vulnerability assessments before and after the cleanup to identify potential security improvements. Ensure that the cleanup has contributed to a more secure codebase. Developer Productivity: Gather feedback from developers regarding productivity improvements after the cleanup. Positive feedback indicates that developers find the codebase more manageable and easier to work with. Regression Testing Results: Perform regression testing to ensure that the cleanup has not introduced new issues or regressions. Ensure that the system remains stable and functional after removing unused Perl code.	Q4

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Read further: Icecat, Brand Cloud, Icecat, Open Icecat

Vazha Abramishvili

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