How I Use Math to Solve Business Problems

I will be talking about a method that I actively use and that I think is very useful as a result of the guidance I have given to my own team. You can apply this method to your own business somehow. ☺️ When tackling complex business questions, a structured and hypothesis-driven approach to data analysis is crucial. By breaking down the problem into smaller, manageable parts, we can identify the root cause of an issue and develop actionable insights. 🚀 In this article, we will explore how to systematically address a business problem (which has happened before 👀) using data, focusing on a step by step approach that starts with the big picture and gradually narrows down to specific details. Example Business Problem: The problem is brought to the data team as follows: Observation: "Fashion category sales are down 15% compared to the previous year." Business Question: "Why have sales declined, and what actions can we take to reverse this trend?" Understanding the User Funnel Before diving into the analysis, it is essential to visualize the user's journey through the funnel. By understanding the key stages of the funnel, we can systematically ask the right questions to identify where drop-offs or issues occur. The funnel typically includes: Awareness: How are users discovering our platform/ marketng channel? Consideration: How are users engaging with our products or services? Conversion: How are users completing purchases or desired actions? Retention: How are we retaining users and encouraging repeat actions? By breaking down the funnel into these stages, we can focus our analysis on specific areas and uncover potential issues impacting performance. Decision Framework: External vs. Internal Factors When a business problem arises, such as a decline in sales, it is essential to determine whether the root cause is external or internal. This decision framework helps guide the analysis: External Factors: 1.1 → Supply Issues: Has there been a change in the availability of products? 1.2 → Demand Changes: Has customer demand for the products decreased due to market trends, economic conditions, or seasonality? Internal Factors: 2.1 → System Changes: Have there been any changes to the platform, pricing, promotions, or algorithms? 2.2 → User Behavior Changes: Has user behavior changed despite no internal changes? For example, users may have shifted their preferences or purchasing patterns. If no external factors are identified, we then proceed with a deeper analysis of internal factors. This involves examining the user experience, platform performance, and the impact of any recent changes to identify potential internal issues that might be causing the observed problem. We can follow the steps below to address whether the internal flows are system related or the user experience has changed. 🚀 Step 1: Analyze Channel-Based Traffic Hypothesis: The drop in sales may be due to reduced traffic from one or more key channels. Questions Answered: Which marketing channels are driving the most traffic? Has there been a decline in traffic from specific marketing channels? Are there any SEO or marketing issues impacting traffic? We break down traffic by channels: Organic Search: Down 20% YoY Paid Search: Flat YoY Social Media: Down 10% YoY Email Marketing: Up 5% YoY Insight: The most significant decline is in organic search traffic, which may indicate SEO issues or decreased search visibility. Next Step: Since SEO is identified as an issue, we should now investigate which platform (e.g., mobile app, desktop) is experiencing the problem. Step 2: Platform-Based Breakdown Hypothesis: One platform (e.g., mobile app, desktop) may be underperforming compared to others. Questions Answered: Are there differences in performance between platforms? Which platform is experiencing the largest decline in traffic or conversions? Are there any technical or user experience issues affecting specific platforms? Platform breakdown: Mobile App: -5% YoY Desktop: -25% YoY Mobile Web: Flat YoY Insight: Desktop traffic has experienced the largest decline, suggesting potential issues with the desktop experience or conversion. Next Step: With desktop identified as the problem area, we should analyze which key pages on the desktop platform are underperforming. Step 3: Page-Level Performance Hypothesis: Key pages (e.g., homepage, product detail pages) may be experiencing performance issues. Questions Answered: Which key pages are experiencing a decline in engagement or conversions? Are specific pages causing drop-offs in the user journey? Are there technical issues affecting page performance? Page performance: Homepage: Flat YoY Product Listing Page: -15% YoY Product Detail Page: -20% YoY Cart Page: -5% YoY Insight: The decline in product detail page traffic suggests a potential issue with user engagement or product visibility. Next Step: Given the issue on the product detail page, we should examine user engagement metrics to understand why users are dropping off. Step 4: KPI Metrics for Visitors Hypothesis: Users may be dropping off due to poor engagement on key pages. Questions Answered: Are users spending less time on key pages? Is there an increase in bounce rates or drop-offs? Are there specific user behaviors indicating a lack of engagement? Visitor metrics: Bounce Rate (Product Detail Page): Up from 40% to 55% Average Time on Page (Product Detail Page): Down from 3 minutes to 2 minutes Conversion Rate: Down from 3% to 2% Insight: A rising bounce rate and reduced average time on page indicate that users may not find the content engaging or relevant. Next Step: We should now investigate if any specific components on the product detail page are contributing to this issue. Step 5: Component-Level Performance Hypothesis: Specific components on key pages (e.g., recommendations, ads, banners) may be underperforming. Questions Answered: Are specific page components causing a decline in engagement or conversions? Are there technical issues with components like recommendations, ads, or banners? Is the recommendation engine displaying relevant products? Component analysis: Recommendations Click-Through Rate (CTR): Down from 8% to 4% Banner Engagement: Flat YoY Ads CTR: Down from 5% to 3% Insight: The significant drop in recommendations CTR suggests that the recommendation engine may not be displaying relevant products. Next Step: If the issue lies in recommendations, we should evaluate recent changes to the recommendation algorithm or logic. After evaluating the changes, we should conduct a monthly breakdown analysis to determine if there were specific months where performance dropped. This will help us pinpoint the exact timing of the issue and identify any other contributing factors or changes made during that period. Step 6: Payment Method and Checkout Flows Hypothesis: Issues in payment methods or checkout flows may be causing drop-offs. Questions Answered: Are there specific issues with payment methods affecting conversions? Is there an increase in drop-offs during the checkout process? Are there user complaints or feedback about payment-related issues? Payment performance: Credit Card Payments: Flat YoY Digital Wallet Payments: Down from 15% to 10% Conversion Rate at Checkout: Flat YoY Insight: The decline in digital wallet payments and overall checkout conversion rate suggests a potential issue with the digital wallet experience. Next Step: If a specific payment method is not boosted for recommendations, detailed analysis can be done based on payment flows (such as users' payment intervals), but the first priority may be monthly reco analysis. Step 6: Monthly Analysis and Change Impact Hypothesis: Identifying the specific month when the decline started can help pinpoint potential causes. Questions Answered: When did the decline in sales or performance metrics start? Were there any specific changes made during that period? Are there seasonal trends or external factors influencing the decline? Monthly breakdown of key metrics: March: Flat April: -5% decline May: -10% decline June: -15% decline Insight: The decline appears to have started in April and accelerated in the following months. Action: Review changes made during the critical months (April to June), such as: Changes in pricing or promotions. Modifications to page design or layout. Updates to recommendations or ads algorithms on recommendation components. By addressing these specific changes, we can determine which factors contributed to the decline and identify corrective actions. Conclusion: A Systematic Approach to Addressing Business Problems By following a structured, hypothesis-driven approach that starts with the big picture and gradually narrows down to more granular details, data analysts can effectively identify the root cause of business problems. This method not only helps in uncovering insights but also provides a roadmap for developing actionable solutions. Ultimately, the choice of specific tools and methods should align with the unique needs and complexity of each project, ensuring the most effective outcomes. Thank you for your time; sharing is caring! 🌍 I will be talking about a method that I actively use and that I think is very useful as a result of the guidance I have given to my own team. You can apply this method to your own business somehow. ☺️ When tackling complex business questions, a structured and hypothesis-driven approach to data analysis is crucial. By breaking down the problem into smaller , manageable parts, we can identify the root cause of an issue and develop actionable insights. 🚀 breaking down the problem into smaller In this article, we will explore how to systematically address a business problem (which has happened before 👀) using data, focusing on a step by step approach that starts with the big picture and gradually narrows down to specific details. Example Business Problem: Example Business Problem: The problem is brought to the data team as follows: Observation: "Fashion category sales are down 15% compared to the previous year." Business Question: "Why have sales declined, and what actions can we take to reverse this trend?" Observation: "Fashion category sales are down 15% compared to the previous year." Observation: Business Question: "Why have sales declined, and what actions can we take to reverse this trend?" Business Question: Understanding the User Funnel Before diving into the analysis, it is essential to visualize the user's journey through the funnel. By understanding the key stages of the funnel, we can systematically ask the right questions to identify where drop-offs or issues occur. The funnel typically includes: Awareness: How are users discovering our platform/ marketng channel? Consideration: How are users engaging with our products or services? Conversion: How are users completing purchases or desired actions? Retention: How are we retaining users and encouraging repeat actions? Awareness: How are users discovering our platform/ marketng channel? Awareness: Consideration: How are users engaging with our products or services? Consideration: Conversion: How are users completing purchases or desired actions? Conversion: Retention: How are we retaining users and encouraging repeat actions? Retention: By breaking down the funnel into these stages, we can focus our analysis on specific areas and uncover potential issues impacting performance. Decision Framework: External vs. Internal Factors When a business problem arises, such as a decline in sales, it is essential to determine whether the root cause is external or internal. This decision framework helps guide the analysis: External Factors: 1.1 → Supply Issues: Has there been a change in the availability of products? 1.2 → Demand Changes: Has customer demand for the products decreased due to market trends, economic conditions, or seasonality? Internal Factors: 2.1 → System Changes: Have there been any changes to the platform, pricing, promotions, or algorithms? 2.2 → User Behavior Changes: Has user behavior changed despite no internal changes? For example, users may have shifted their preferences or purchasing patterns. External Factors: 1.1 → Supply Issues: Has there been a change in the availability of products? 1.2 → Demand Changes: Has customer demand for the products decreased due to market trends, economic conditions, or seasonality? External Factors: External Factors: 1.1 → Supply Issues: Has there been a change in the availability of products? 1.1 → Supply Issues: 1.2 → Demand Changes: Has customer demand for the products decreased due to market trends, economic conditions, or seasonality? 1.2 → Demand Changes: Internal Factors: 2.1 → System Changes: Have there been any changes to the platform, pricing, promotions, or algorithms? 2.2 → User Behavior Changes: Has user behavior changed despite no internal changes? For example, users may have shifted their preferences or purchasing patterns. Internal Factors: Internal Factors: 2.1 → System Changes: Have there been any changes to the platform, pricing, promotions, or algorithms? 2.1 → System Changes: 2.2 → User Behavior Changes: Has user behavior changed despite no internal changes? For example, users may have shifted their preferences or purchasing patterns. 2.2 → User Behavior Changes: If no external factors are identified, we then proceed with a deeper analysis of internal factors. This involves examining the user experience, platform performance, and the impact of any recent changes to identify potential internal issues that might be causing the observed problem. We can follow the steps below to address whether the internal flows are system related or the user experience has changed. 🚀 Step 1: Analyze Channel-Based Traffic Hypothesis: The drop in sales may be due to reduced traffic from one or more key channels. Hypothesis: The drop in sales may be due to reduced traffic from one or more key channels. Hypothesis: Questions Answered: Questions Answered: Which marketing channels are driving the most traffic? Has there been a decline in traffic from specific marketing channels? Are there any SEO or marketing issues impacting traffic? We break down traffic by channels: Organic Search: Down 20% YoY Paid Search: Flat YoY Social Media: Down 10% YoY Email Marketing: Up 5% YoY Which marketing channels are driving the most traffic? Has there been a decline in traffic from specific marketing channels? Are there any SEO or marketing issues impacting traffic? We break down traffic by channels: Organic Search: Down 20% YoY Paid Search: Flat YoY Social Media: Down 10% YoY Email Marketing: Up 5% YoY Organic Search: Down 20% YoY Paid Search: Flat YoY Social Media: Down 10% YoY Email Marketing: Up 5% YoY Organic Search: Down 20% YoY Paid Search: Flat YoY Social Media: Down 10% YoY Email Marketing: Up 5% YoY Insight: The most significant decline is in organic search traffic, which may indicate SEO issues or decreased search visibility. Next Step: Since SEO is identified as an issue, we should now investigate which platform (e.g., mobile app, desktop) is experiencing the problem. Insight: The most significant decline is in organic search traffic, which may indicate SEO issues or decreased search visibility. Insight: Next Step: Since SEO is identified as an issue, we should now investigate which platform (e.g., mobile app, desktop) is experiencing the problem. Next Step: Step 2: Platform-Based Breakdown Hypothesis: One platform (e.g., mobile app, desktop) may be underperforming compared to others. Hypothesis: One platform (e.g., mobile app, desktop) may be underperforming compared to others. Hypothesis: Questions Answered: Questions Answered: Are there differences in performance between platforms? Which platform is experiencing the largest decline in traffic or conversions? Are there any technical or user experience issues affecting specific platforms? Platform breakdown: Mobile App: -5% YoY Desktop: -25% YoY Mobile Web: Flat YoY Are there differences in performance between platforms? Which platform is experiencing the largest decline in traffic or conversions? Are there any technical or user experience issues affecting specific platforms? Platform breakdown: Mobile App: -5% YoY Desktop: -25% YoY Mobile Web: Flat YoY Mobile App: -5% YoY Desktop: -25% YoY Mobile Web: Flat YoY Mobile App: -5% YoY Desktop: -25% YoY Mobile Web: Flat YoY Insight: Desktop traffic has experienced the largest decline, suggesting potential issues with the desktop experience or conversion. Next Step: With desktop identified as the problem area, we should analyze which key pages on the desktop platform are underperforming. Insight: Desktop traffic has experienced the largest decline, suggesting potential issues with the desktop experience or conversion. Insight: Next Step: With desktop identified as the problem area, we should analyze which key pages on the desktop platform are underperforming. Next Step: Step 3: Page-Level Performance Hypothesis: Key pages (e.g., homepage, product detail pages) may be experiencing performance issues. Hypothesis: Key pages (e.g., homepage, product detail pages) may be experiencing performance issues. Hypothesis: Questions Answered: Questions Answered: Which key pages are experiencing a decline in engagement or conversions? Are specific pages causing drop-offs in the user journey? Are there technical issues affecting page performance? Page performance: Homepage: Flat YoY Product Listing Page: -15% YoY Product Detail Page: -20% YoY Cart Page: -5% YoY Which key pages are experiencing a decline in engagement or conversions? Are specific pages causing drop-offs in the user journey? Are there technical issues affecting page performance? Page performance: Homepage: Flat YoY Product Listing Page: -15% YoY Product Detail Page: -20% YoY Cart Page: -5% YoY Homepage: Flat YoY Product Listing Page: -15% YoY Product Detail Page: -20% YoY Cart Page: -5% YoY Homepage: Flat YoY Product Listing Page: -15% YoY Product Detail Page: -20% YoY Cart Page: -5% YoY Insight: The decline in product detail page traffic suggests a potential issue with user engagement or product visibility. Next Step: Given the issue on the product detail page, we should examine user engagement metrics to understand why users are dropping off. Insight: The decline in product detail page traffic suggests a potential issue with user engagement or product visibility. Insight: Next Step: Given the issue on the product detail page, we should examine user engagement metrics to understand why users are dropping off. Next Step: Step 4: KPI Metrics for Visitors Hypothesis: Users may be dropping off due to poor engagement on key pages. Hypothesis: Users may be dropping off due to poor engagement on key pages. Hypothesis: Questions Answered: Questions Answered: Are users spending less time on key pages? Is there an increase in bounce rates or drop-offs? Are there specific user behaviors indicating a lack of engagement? Visitor metrics: Bounce Rate (Product Detail Page): Up from 40% to 55% Average Time on Page (Product Detail Page): Down from 3 minutes to 2 minutes Conversion Rate: Down from 3% to 2% Are users spending less time on key pages? Is there an increase in bounce rates or drop-offs? Are there specific user behaviors indicating a lack of engagement? Visitor metrics: Bounce Rate (Product Detail Page): Up from 40% to 55% Average Time on Page (Product Detail Page): Down from 3 minutes to 2 minutes Conversion Rate: Down from 3% to 2% Bounce Rate (Product Detail Page): Up from 40% to 55% Average Time on Page (Product Detail Page): Down from 3 minutes to 2 minutes Conversion Rate: Down from 3% to 2% Bounce Rate (Product Detail Page): Up from 40% to 55% Average Time on Page (Product Detail Page): Down from 3 minutes to 2 minutes Conversion Rate: Down from 3% to 2% Insight: A rising bounce rate and reduced average time on page indicate that users may not find the content engaging or relevant. Next Step: We should now investigate if any specific components on the product detail page are contributing to this issue. Insight: A rising bounce rate and reduced average time on page indicate that users may not find the content engaging or relevant. Insight: Next Step: We should now investigate if any specific components on the product detail page are contributing to this issue. Next Step: Step 5: Component-Level Performance Hypothesis: Specific components on key pages (e.g., recommendations, ads, banners) may be underperforming. Hypothesis: Specific components on key pages (e.g., recommendations, ads, banners) may be underperforming. Hypothesis: Questions Answered: Questions Answered: Are specific page components causing a decline in engagement or conversions? Are there technical issues with components like recommendations, ads, or banners? Is the recommendation engine displaying relevant products? Component analysis: Recommendations Click-Through Rate (CTR): Down from 8% to 4% Banner Engagement: Flat YoY Ads CTR: Down from 5% to 3% Are specific page components causing a decline in engagement or conversions? Are there technical issues with components like recommendations, ads, or banners? Is the recommendation engine displaying relevant products? Component analysis: Recommendations Click-Through Rate (CTR): Down from 8% to 4% Banner Engagement: Flat YoY Ads CTR: Down from 5% to 3% Recommendations Click-Through Rate (CTR): Down from 8% to 4% Banner Engagement: Flat YoY Ads CTR: Down from 5% to 3% Recommendations Click-Through Rate (CTR): Down from 8% to 4% Banner Engagement: Flat YoY Ads CTR: Down from 5% to 3% Insight: The significant drop in recommendations CTR suggests that the recommendation engine may not be displaying relevant products. Next Step: If the issue lies in recommendations, we should evaluate recent changes to the recommendation algorithm or logic. After evaluating the changes, we should conduct a monthly breakdown analysis to determine if there were specific months where performance dropped. This will help us pinpoint the exact timing of the issue and identify any other contributing factors or changes made during that period. Insight: The significant drop in recommendations CTR suggests that the recommendation engine may not be displaying relevant products. Insight: Next Step: If the issue lies in recommendations, we should evaluate recent changes to the recommendation algorithm or logic. After evaluating the changes, we should conduct a monthly breakdown analysis to determine if there were specific months where performance dropped. This will help us pinpoint the exact timing of the issue and identify any other contributing factors or changes made during that period. Next Step: Step 6: Payment Method and Checkout Flows Hypothesis: Issues in payment methods or checkout flows may be causing drop-offs. Hypothesis: Issues in payment methods or checkout flows may be causing drop-offs. Hypothesis: Questions Answered: Questions Answered: Are there specific issues with payment methods affecting conversions? Is there an increase in drop-offs during the checkout process? Are there user complaints or feedback about payment-related issues? Payment performance: Credit Card Payments: Flat YoY Digital Wallet Payments: Down from 15% to 10% Conversion Rate at Checkout: Flat YoY Are there specific issues with payment methods affecting conversions? Is there an increase in drop-offs during the checkout process? Are there user complaints or feedback about payment-related issues? Payment performance: Credit Card Payments: Flat YoY Digital Wallet Payments: Down from 15% to 10% Conversion Rate at Checkout: Flat YoY Credit Card Payments: Flat YoY Digital Wallet Payments: Down from 15% to 10% Conversion Rate at Checkout: Flat YoY Credit Card Payments: Flat YoY Digital Wallet Payments: Down from 15% to 10% Conversion Rate at Checkout: Flat YoY Insight: The decline in digital wallet payments and overall checkout conversion rate suggests a potential issue with the digital wallet experience. Next Step: If a specific payment method is not boosted for recommendations, detailed analysis can be done based on payment flows (such as users' payment intervals), but the first priority may be monthly reco analysis. Insight: The decline in digital wallet payments and overall checkout conversion rate suggests a potential issue with the digital wallet experience. Insight: Next Step: If a specific payment method is not boosted for recommendations, detailed analysis can be done based on payment flows (such as users' payment intervals), but the first priority may be monthly reco analysis. Next Step: Step 6: Monthly Analysis and Change Impact Hypothesis: Identifying the specific month when the decline started can help pinpoint potential causes. Hypothesis: Identifying the specific month when the decline started can help pinpoint potential causes. Hypothesis: Questions Answered: Questions Answered: When did the decline in sales or performance metrics start? Were there any specific changes made during that period? Are there seasonal trends or external factors influencing the decline? Monthly breakdown of key metrics: March: Flat April: -5% decline May: -10% decline June: -15% decline When did the decline in sales or performance metrics start? Were there any specific changes made during that period? Are there seasonal trends or external factors influencing the decline? Monthly breakdown of key metrics: March: Flat April: -5% decline May: -10% decline June: -15% decline March: Flat April: -5% decline May: -10% decline June: -15% decline March: Flat April: -5% decline May: -10% decline June: -15% decline Insight: The decline appears to have started in April and accelerated in the following months. Insight: The decline appears to have started in April and accelerated in the following months. Insight: Action: Review changes made during the critical months (April to June), such as: Action: Changes in pricing or promotions. Modifications to page design or layout. Updates to recommendations or ads algorithms on recommendation components. Changes in pricing or promotions. Modifications to page design or layout. Updates to recommendations or ads algorithms on recommendation components. By addressing these specific changes, we can determine which factors contributed to the decline and identify corrective actions. Conclusion: A Systematic Approach to Addressing Business Problems By following a structured, hypothesis-driven approach that starts with the big picture and gradually narrows down to more granular details, data analysts can effectively identify the root cause of business problems. This method not only helps in uncovering insights but also provides a roadmap for developing actionable solutions. Ultimately, the choice of specific tools and methods should align with the unique needs and complexity of each project, ensuring the most effective outcomes. Thank you for your time; sharing is caring! 🌍