Tag Archives: strategy


One of the major challenges faced during the implementation of food waste reduction strategies was changing public behavior and mindsets around food. For many years, most people have viewed excess food as unimportant and not given much thought to wasting it. Things like clearing one’s plate, over-ordering at restaurants, or throwing out old leftovers and expired foods were ingrained habits. Shifting such habitual behaviors requires a significant mindset change, which can be difficult to achieve. It requires sustained education campaigns to raise awareness of the issue and its impacts, as well as motivation for people to adjust their daily food-related routines and habits.

Another behavioral challenge is that reducing food waste often requires more planning and coordination within households. Things like meticulously planning out meals, sticking to grocery lists, adjusting portion sizes, and making better use of leftovers necessitates more effort and time compared to past habits. While improving skills like meal planning, it is an adjustment that not everyone finds easy to make. For families with both parents working long hours, seeking convenience is also an understandable priority, leaving little time or energy for meticulous waste-reduction efforts.

From a business and operations perspective, one challenge is the lack of reliable data on food waste amounts. Most organizations, whether food manufacturers, grocery retailers or food service companies, have historically not tracked the scale of food that gets wasted within their facilities and supply chains. Without robust baseline data, it is difficult to analyze root causes, identify priorities and set meaningful targets for improvement. Some have also been hesitant to publicly share waste data for risk of reputational damage. The lack of common measurement standards has made industry-wide benchmarking and goal setting a challenge.

On the policy front, the mixed competencies shared between different levels and departments of government have made coordinated action difficult. Food waste touches on the responsibilities of agriculture, environment, waste collection, business regulations, public awareness campaigns and more. There is sometimes lack of clarity on who should take the lead, and duplication or gaps can occur between different actors. The complexity with multiple stakeholders across many domains further impedes swift, aligned policy progress to drive systemic changes.

Even when strategies are set, enforcement is a big challenge especially related to food date labeling policies. Standardizing and simplifying date labels to distinguish between ‘Best Before’ – indicating quality rather than safety – and ‘Use By’ date is an important intervention. Inconsistent application of new labeling rules by some in the vast food industry has undermined the effectiveness of this policy change to reduce consumer confusion and subsequent waste. Stronger compliance mechanisms are needed.

From a technological standpoint, while innovative solutions are emerging, scaling these up to have meaningful impact requires extensive investments of time and capital. Food redistribution through apps needs to overcome challenges like adequate coverage, logistical issues in arranging pick ups, necessity of refrigerated transportation, and standardizing quality parameters of donor and recipient organizations. Similarly, food waste valorization is still at a nascent, experimental phase with challenges of developing financially viable business models at commercial scale. These solutions are also capital intensive to set up advanced processing facilities.

Even simple measures like home composting have faced adoption challenges due to requirements like space, installation efforts, maintenance skills and concerns over pests and smells. Compostable packaging is not universally available and green bins for food scrap collection are not scaled up widely in all geographies to make participation easy. Expanded waste collection infrastructure requires substantial capital allocations by local governments already facing budget constraints.

From a supply chain coordination perspective, a key challenge is data and technology integration across the long and complex path food takes from farms to processing units to transport networks to retailers to finally consumers. Lack of end-to-end visibility impedes root cause analysis of where and why waste is originating. It also restricts opportunities for collaborative optimization of inventory, ordering and demand planning practices to minimize food left unconsumed at any stage. Silos between different entities and lack of incentives for open data sharing have hampered integrated solutions.

Reducing food waste faces behavioral, operational, policy-related, technological, financial as well as supply chain coordination challenges. It requires multifaceted, long-term efforts spanning awareness drives, standardized measurement, supportive regulations, scaled infrastructure, collaborative innovation and adaptability to local conditions. The complexity of root causes necessitates system-wide cooperation between industry, governments, researchers and communities to achieve meaningful impact over time. While progress has been made, continued dedication of resources and coordination between different stakeholders remains important to sustain momentum in tackling this massive global issue.


Tommy Hilfiger has emerged as one of the leading fashion brands in the world by effectively leveraging data analytics across various aspects of its marketing approach. Some of the key ways in which the company uses data analytics include:

Customer profiling and segmentation: Tommy Hilfiger gathers extensive customer data from various online and offline touchpoints. This includes transaction data, website behavior data, social media engagement data, loyalty program data, and more. The company analyzes this wealth of customer data to develop rich customer profiles and segment customers based on attributes like demographics, purchase history, lifestyle patterns, engagement preferences, and more. This helps the brand develop highly targeted and personalized marketing campaigns for different customer segments.

Predictive analysis of customer behavior: Tommy Hilfiger combines its customer profiles and segmentation with predictive modeling techniques to analyze historical customer data and identify patterns in customer behaviors. This helps the company predict future customer behaviors like likelihood of purchase, priority product categories, engagement preferences, loyalty patterns, churn risk, and so on for individual customers or segments. Such predictive insights enable Tommy Hilfiger to implement highly customized and predictive marketing campaigns.

Personalized communication and offers: Leveraging its customer profiling, segmentation, and predictive analysis capabilities, Tommy Hilfiger sends hyper-personalized communications including catalogs, emails, push notifications, and offers to its customers. For example, it may promote new arrivals specifically catering to the past purchase history of a high value customer and offer them additional discounts. Such personalization has significantly boosted customer engagement and spending for the brand.

Cross-selling and upselling: Data analytics helps Tommy Hilfiger identify related and complementary product categories that an individual customer may be interested based on their past purchases. It employs this to dynamically send targeted cross-selling and upselling recommendations. For instance, it can detect customers who frequently purchase jeans and actively promote shirts and accessories that will complement the jeans. This has noticeably increased its average order value over time.

Omnichannel attribution modeling: With customers engaging via multiple channels today, it is important to analyze the impact of each touchpoint. Tommy Hilfiger uses advanced attribution modeling to recognize the actual impact and value of each marketing channel toward final online and offline conversions. This provides valuable insights into optimizing spending across online and offline channels for maximum ROI.

Real-time personalized webpage experiences: Tommy Hilfiger leverages customer data to deliver hyper-personalized webpage experiences to its customers. For example, when a customer visits the website, they are prominently displayed products from their past viewed/wishlisted categories to optimize engagement. Product recommendations are also dynamically updated based on their real-time behavior like adding products to cart. This has increased conversion rates on the website significantly.

Location-based and contextual marketing: It analyzes location check-ins of customers on its app to identify high engagement areas. It then promotes relevant offers and campaigns to customers visiting such preferred locations. For example, discounts on footwear if customers are detected at a hobby store. Contextual triggers like weather, events, and seasonality are also integrated to further boost messaging relevance.

Inventory and demand forecasting: Tommy Hilfiger uses its rich historical sales data combined with external demand drivers to forecast demand and sales volumes for individual SKUs with a high degree of accuracy. Using these fine-grained demand forecasts, it optimally plans production runs and inventory levels to reduce markdown risk and ensure adequate stock levels. This has enhanced operational efficiency.

Promotions and pricing optimization: Data analytics enables Tommy Hilfiger to test and learn which combination of products, offers, campaigns, and prices are most effective at stimulating demand and maximizing revenues/profits for the company as well as value for customers. For example, A/B testing of home page designs or discount levels. It then routes the top performing strategies to full rollout.

Performance measurement and optimization: At every step, Tommy Hilfiger measures key metrics like viewership, engagement, conversion, repeat rates, NPS etc. to evaluate strategy effectiveness. It uses these data-driven insights to continually enhance its algorithms, models and approach over time – establishing a virtuous cycle of continuous performance improvement.

Tommy Hilfiger has transformed into a fully digital-driven business by taking extensive advantage of data analytics across the customer lifecycle right from engagement and personalization to predictive strategy optimization. This has enabled memorable customer experiences driving brand love and loyalty, fueling the company’s consistent growth. Data-led decision making is now at the core of Tommy Hilfiger’s entire operations globally.


Telegram has taken a unique approach to monetization compared to other popular messaging platforms such as WhatsApp, Facebook Messenger, WeChat, and LINE. While many messaging apps have adopted paid subscription models or in-app advertising and promotions, Telegram has so far avoided these monetization tactics in favor of other innovative strategies.

Telegram is considered a “freemium” service as users can enjoy the basic features for free, but paid subscriptions are available to unlock additional premium features. Unlike other messaging platforms, Telegram does not place ads or in-app promotions and has stated they never will due to concerns over how ads could impact user privacy and experience. Instead, Telegram relies mainly on optional donations from its large existing user base to fund ongoing development and server costs. Telegram is able to offer these services without ads currently because founder Pavel Durov has pledged around $200 million from his personal fortune to support the app.

Telegram launched “Telegram Premium” in June 2022, introducing a paid subscription for the first time. Premium subscribers can receive a larger maximum number of contacts, folders, pins, and more. Premium also increases file upload limits and introduces exclusive animated emoji and reactions. Telegram Premium costs $4.99 per month but the company claims this optional subscription will be enough for Telegram to fully support itself without any future need for alternative monetization methods like ads.

In contrast, WhatsApp employs no monetization at all presently since it is owned by Facebook parent company Meta. WhatsApp did have plans to introduce optional business-focused paid services and in-app purchases, but that was delayed indefinitely after a user backlash over privacy concerns. WhatsApp has over 2 billion users but generates no direct revenue, relying solely on Meta’s other business revenues to fund development.

Meta’s other messaging platforms like Facebook Messenger and Instagram Direct have prominent in-app advertising including product and service recommendations. Businesses can promote their Messenger profiles, chats, stories, and online stores through ads. Messenger also offers subscription plans for businesses’ customer service capabilities through tools like Messenger API bots.

WeChat in China has become a powerful super app with a wide array of services completely integrated within the messaging experience. WeChat monetizes through digital payments services, gaming integrations, and a thriving mini program ecosystem similar to mobile apps where businesses can promote and sell digital goods/services. WeChat takes a cut of revenues from these integrations that has made it immensely profitable for parent company Tencent without any ads within the core chat functions.

Japanese messaging platform LINE also emphasizes services beyond communication including games, commerce, and digital content integrated directly into the app experience. LINE generates major revenues through its games including Puzzle & Dragons, sales of LINE-based stickers and digital goods, advertising, and a payments platform called LINE Pay similar to WeChat Pay. LINE has also explored optional premium LINE TV and phone plan subscriptions.

Korean messaging giant Kakao follows a South Korean model emphasizing built-in mini games accessible via chat profiles which generate abundant in-game purchases. KakaoTalk also earns income from a music streaming service, loyalty points program, commerce platform, and its digital wallet service Kakao Pay.

In summary – while most messaging platforms depend heavily on in-app ads, e-commerce integrations or paid subscriptions – Telegram has resisted this approach so far through Pavel Durov’s initial funding and the recent premium subscription option. WeChat, LINE, Kakao and Messenger align more with the super app model fully integrating overlays services alongside communication. But Telegram seeks to keep a tighter separation of chat functionality from additional monetized overlays and services. Only time will tell if Telegram Premium generates enough ongoing revenue or if alternative strategies may eventually be explored.


There are several key metrics that should be used to measure the success of an email marketing strategy effectively. Tracking the right metrics is important to determine how well the emails are performing and if the strategy needs any adjustments over time. Some of the most important metrics to track include:

Open Rates – One of the most basic but important metrics is the open rate which measures how many recipients actually opened each email. Open rates help determine if the subject lines are enticing enough for people to take a look at the content. It’s a good idea to track open rates over time and benchmark them against industry averages for the sector. Open rates of 20% or higher are generally considered good but the goal should be continuous improvement over time.

Click-Through Rates – After measuring opens, tracking click-through rates from email content to the desired destination URLs is crucial. CTRs help determine which content and call-to-action buttons are most effective at driving people to the website. clicks within the body of emails and footers should be tracked separately. CTRs of 2-3% from content links or 5-10% from CTAs are generally seen as good performance.

Unsubscribe Rates – Also important to measure is the unsubscribe rate which shows the percentage of people who choose to unsubscribe from a particular mailing list. Higher unsubscribe rates could indicate people are receiving emails they don’t find relevant. Unsubscribe rates below 1% are ideal.

Engagement/Interaction Rates – Beyond just open and click metrics, it’s valuable to measure engagement rates that track actions like social sharing, form submissions, content downloads, etc. This helps determine if emails are effective at driving real interactions and conversions beyond just initial clicks.

Conversion/Revenue Metrics – The most important metrics focus on conversions and revenue. These include metrics like e-newsletter signups, webinar/event registrations, website registrations, lead submissions, e-commerce purchases and sales revenue that can be directly attributed to email interactions. Goals and return on investment should connect email metrics back to conversion and revenue results.

Subscriber/List Growth – Over time, the email list size and growth rates are also important to track. Steady growth of the list size shows improved acquisition strategies while flat or declining numbers may indicate issues. Growth of targeted lists is better than overall general growth.

Delivery and Spam Rates – Ensuring high email deliverability is critical to the strategy’s success as well. Tracking metrics around successful email deliveries, spam complaint rates and bounce rates help spot any red flags impacting overall performance.

Benchmarking – Along with benchmarking key metrics against past performance, it’s good practice to benchmark email marketing KPIs against relevant industry averages provided in reports from experts like Litmus, Mailchimp, etc. This helps assess if results are above or below expected norms.

Segment-level Analytics – Drilling down metrics to see performance of different email list segments, content categories and device types (mobile vs desktop) provides actionable insights. For example, transactional emails may have different benchmarks than marketing emails.

Attribution Modeling – Advanced attribution techniques can begin linking final conversions back to specific emails, campaigns, links, or media that contributed to a sale or lead. This improves ROI justification and optimization of attributing budget/efforts.

Qualitative Feedback – In addition to quantitative metrics, occasional qualitative surveys can gather customer feedback on email preferences, content relevancy, and improvement ideas. This user sentiment helps supplement the quantitative metrics.

Testing and Optimization – Consistent a/b split testing of subject lines, send times, call to action buttons, and design/formatting helps optimize different email elements. Winners of each test round can be implemented to continuously enhance email performance.

It’s important to track a balanced set of relevant metrics at different stages of the customer journey that measures email strategy success based on multiple dimensions – from initial engagement and interaction levels to conversions and renewals further down the line. Combining quantitative metrics with occasional qualitative surveys provides invaluable insights to evaluate progress, refine approaches, and improve ROI from the email marketing strategy over the long-term. Continuous testing helps make ongoing enhancements to keep email performance improving over time.


Testing Strategy:

The testing strategy for the payroll system involves rigorous testing at four levels – unit testing, integration testing, system testing, and user acceptance testing.

Unit Testing: All individual modules and program units that make up the payroll application will undergo unit testing. This includes functions, classes, databases, APIs etc. Unit tests will cover both normal and edge conditions to test validity, functionality and accuracy. We will use a test-driven development approach and implement unit tests even as the code is being written to ensure code quality. A code coverage target of 80% will be set to ensure that most of the code paths are validated through unit testing.

Integration Testing: Once the individual units have undergone unit testing and bugs fixed, integration testing will involve testing how different system modules interact with each other. Tests will validate the interface behavior between different components like the UI layer, business logic layer, and database layer. Error handling, parameter passing and flow of control between modules will be rigorously tested. A modular integration testing approach will be followed where integration of small subsets is tested iteratively to catch issues early.

System Testing: On obtaining satisfactory results from unit and integration testing, system testing will validate the overall system functionality as a whole. End-to-end scenarios mimicking real user flows will be designed and tested to check requirements implementation. Performance and load testing will also be conducted at this stage to test response times and check system behavior under load conditions. Security tests like penetration testing will be carried out by external auditors to identify vulnerabilities.

User Acceptance Testing: The final stage of testing prior to deployment will involve exhaustive user acceptance testing (UAT) by the client users themselves. A dedicated UAT environment exactly mirroring production will be set up for testing. Users will validate pay runs, generate payslips and reports, configure rules and thresholds through testing. They will also provide sign off on acceptance criteria and report any bugs found for fixing. Only after clearing UAT, the system will be considered ready for deployment to production.

Deployment Strategy:

A multi-phase phased deployment strategy will be followed to minimize risks during implementation. The key steps are:

Development and Staging Environments: Development of new features and testing will happen in initial environments isolated from production. Rigorous regression testing will happen across environments after each deployment.

Pilot deployment: After UAT sign off, the system will first be deployed to a select pilot user group and select location/department. Their usage and feedback will be monitored closely before proceeding to next phase.

Phase-wise rollout: Subsequent deployments will happen in phases with rollout to different company locations/departments. Each phase will involve monitoring and stabilization before moving to next phase. This reduces load and ensures steady-state operation.

Fallback strategy: A fallback strategy involving capability to roll back to previous version will be in place. Database scripts will allow reverting schema and data changes. Standby previous version will also be available in case required.

Monitoring and Support: Dedicated support and monitoring will be provided post deployment. An incident and problem management process will be followed. Product support will collect logs, diagnose and resolve issues. Periodic reviews will analyze system health and user experience.

Continuous Improvement: Feedback and incident resolutions will be used for further improvements to software, deployment process and support approach on an ongoing basis. Additional features and capabilities can also be launched periodically following the same phased approach.

Regular audits will also be performed to assess compliance with processes, security controls and regulatory guidelines after deployment into production. This detailed testing and phased deployment strategy aims to deliver a robust and reliable payroll system satisfying business and user requirements.