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©Data collection_ Are you doing it right_ yes or no/Shutterstock.com Introduction Initiating a root cause analysis can be derailed by the unexpected discovery of insufficient data. This is a frequent challenge encountered by nascent analysts, aligning with the adage that a significant portion of an analyst's time is devoted to data preparation rather than analytical interpretation. This phenomenon is particularly pronounced when a critical metric, such as conversion rate, experiences an abrupt decline, creating a heightened sense of urgency. This article presents a pragmatic framework for establishing an effective data collection methodology specifically tailored to root cause analysis within the domains of product, web, and marketing analytics. The primary objective is to empower analysts to identify and acquire the necessary data from the outset, thereby mitigating the risk of unproductive time expenditure. Method of Data Collection The majority of analytics teams maintain dashboards for the ongoing monitoring of key performance indicators (KPIs). These dashboards typically leverage pre-existing data pipelines. While it may be tempting to utilize this readily available data for root cause analysis (i.e., determining the factors contributing to a metric's deviation), it is crucial to recognize that such data is primarily designed for tracking purposes and may not provide the granular detail necessary for in-depth investigations. Consequently, the collection of supplementary data often becomes imperative. When embarking on a root cause analysis, the need to gather novel data frequently arises. A straightforward approach to this process entails the following steps: Define Data Requirements:  In the initial stage, it is essential to articulate the specific information required to comprehend the underlying issue. Formulate precise questions that demand answers. For instance, if a decline in conversion rates is observed, it may be necessary to acquire data pertaining to user behavior both preceding and subsequent to the observed change. Develop a Collection Plan:  Subsequently, determine the most effective methodology for data acquisition. This may involve the utilization of existing tools, the implementation of new tracking mechanisms, the administration of surveys, or the exploration of alternative data sources. It is crucial to approach this step with specificity. Identify Data Sources:  Pinpoint the precise origin of the required data. Potential sources may encompass website databases, application logs, marketing platforms, or other relevant repositories. Assign Responsibilities:  Lastly, designate individuals responsible for the data collection process. This proactive measure serves to minimize confusion and ensure the timely completion of data acquisition activities. To facilitate the planning of data requirements for your root cause analysis, please refer to the data collection worksheet provided below. Data Collection Goal : Data Collection Guide Questions Data for Questions List data for questions Who did what, and when? You can find this data in the task and activity records.   How is this different from before? Collect data on how the process is supposed to work and how it actually works   What could have been done to prevent this? Data on barriers to success can reveal prevention opportunities.   What events preceded this outcome? Collect data on what happened and what the effects were.   Who knows what happen? Witness/Participant Data   Why did they do that? Contextual Factors   How to Collect Data For each data point you need, describe how  you will get it. Be specific about the tools or methods you will use (e.g., "Use Google Analytics to check pageviews," "Review server logs for error messages," "Send out a user survey"). Data Sources For each data point you need, list where  you will find it. Be specific about the tools, databases, or systems you will use (e.g., "Google Analytics," "website server logs," "marketing automation platform," "user survey database"). A Real-World Example Consider a scenario where a newly appointed growth analyst is tasked with augmenting sign-up rates for a monthly data analytics coaching platform. Upon observing a decline in website sign-ups, the analyst conducts preliminary investigations to confirm the veracity of this observation. Subsequently, the analyst proceeds to employ a data collection plan to ascertain the underlying causes of this decline. Data Collection Goal : Gather data to explain the sign-up rate drop. Data Collection Guide Questions Data for Questions List data for questions Who did what, and when? You can find this data in the task and activity records. Data on User Behavior (What was done): Pageviews & Navigation:  Navigation paths, entry/exit pages, Pageviews per Step, Unique Pageviews. Time on Page:  time spent, time spent on specific fields, time to submit. Form Interactions:  Form starts, submissions, completion/abandonment field-specific data, autofill use, number of attempts, field value changes. User Engagement:  Scroll depth, mouse movement Data on User (Whom) : How users arrived at the sign-up page (e.g., search engine, social media) Device used to access the sign-up page (e.g., desktop, mobile). Browser used for sign-up (e.g., Chrome, Safari). User's location when accessing the sign-up page (e.g., US, UK). First-time or repeat visit to sign-up page. Data on Timestamp (When) on sign-up. How is this different from before? Collect data on how the process is supposed to work and how it actually works Collect Data on Ideal Process(How is Supposed to Work):   Document the ideal user flow (steps, pages). Note expected time and conversion rates for each step. Record system requirements. Collect Data on Actual Performance (What's Happening): Watch user sessions or analyze navigation data to see their actual path. Measure the time and completion rate at each stage of the process. Check system performance (load times, errors). What could have been done to prevent this? Data on barriers to success can reveal prevention opportunities. Potential data on potential barriers users faced during the sign-up process:   Form Complexity, Mobile Experience, Instructions and Clarity, Password Requirements, Progress Indicators, Website Errors/Bugs, Loading Times, Browser/Device Incompatibility, Privacy/Security Concerns, Trust/Credibility, Perceived Value, Competitor Actions, Market Trends/Seasonality What events preceded this outcome? Collect data on what happened and what the effects were.   Website Changes: Event: Redesign/updates. Result:  Increased bounce rate, longer form times. Event:  New form. Result:  Increased abandonment, specific errors. Event: Maintenance/downtime. Result:  Lost sign-ups, negative mentions. Marketing Changes: Event:  New campaign. Result:  Initial traffic spike, low conversions. Event: Campaign changes. Result:  Decreased click-throughs changed traffic sources. Event:  Promo ended. Result:  Expected, but potentially steeper, drop. External Factors: Event: Competitor launch. Result:  Decreased traffic. Event: Negative news. Result:  Decreased interest/sign-ups. Event: Search algorithm change. Result:  Drop in organic traffic. Technical Issues: Event: Website errors/bugs. Result:  Increased support tickets, negative reviews. Event:  Slow load times. Result: Increased bounce rate, shorter sessions. Who knows what happen? Witness/Participant Data Key Stakeholders:  Marketing, Web Dev, Product, Customer Support, Sales (if applicable), Users. Why did they do that? Contextual Factors Survey Data:  Open-ended responses regarding motivations, frustrations, and reasons for abandonment. User Testing Data:  Observations of user interactions, noting usability issues and areas of confusion. Support Logs:  Records of user problems and questions related to sign-up. Social Media Data:  Mentions and discussions related to the sign-up process and brand sentiment. How to Collect Data Analytics Platforms:  Use tools like Google Analytics, marketing/ad platforms, and social media analytics to track user behavior, campaign performance, and website traffic. User Research:  Conduct surveys, user testing, and monitor user feedback (support tickets, feedback forms, social media). System Monitoring:  Review system logs, error reports, and performance monitoring tools to assess technical performance. Business Records:  Analyze sales/financial data, project management tools, and meeting minutes for internal context. External Research: Consult market research reports, industry benchmarks, and economic data for external context. Data Sources Website:  Analytics (Google Analytics, heatmaps, session recordings, server logs). Marketing:  Automation/ad platforms, social media, CRM. User Feedback:  Surveys, user testing, support tickets, feedback forms, social media. Technical:  System logs, error/performance monitoring, version control. Business:  Sales/financial data, project management tools, meeting minutes. External:  Market research, benchmarks, economic data. Primarily interviews/discussions, supplemented by:  Meeting minutes, communication records. This example demonstrates a data collection planning model for root cause analysis in digital analytics. It can also be adapted for marketing, product analytics, and other digital metrics. Application Across Disciplines A diverse array of professions across various sectors employ data collection methodologies to effectively identify the root causes of encountered problems. Notable examples include: Product Development:  Product Managers, User Experience (UX) Researchers, Quality Assurance (QA) Engineers, and Operations Managers. Marketing:  Marketing Analysts, Marketing Managers, and Digital Marketing Specialists. Web Development & Operations:  Web Analysts, Developers, Engineers, and Site Reliability Engineers (SREs). Data Science:  Data Scientists and Machine Learning Engineers. Cross-disciplinary Applications:  Business Analysts, Data Analysts, Operations Research Analysts, Project Managers, Process Improvement Specialists, and Quality Control Specialists. Critical Considerations for Data Collection Several key considerations must be borne in mind during the data collection process: Temporal Dimension:  Close attention should be paid to the temporal sequence of events. Analyzing the chronological occurrence of events facilitates the identification of temporal patterns and the assessment of changes over time. Contextual Analysis:  A comprehensive understanding of the broader context surrounding the event is crucial. Analyzing the situational factors surrounding the event can unveil hidden causal relationships that may not be readily apparent from the data alone. Exploration Beyond Conventional Sources:  Data collection should not be limited to traditional databases. Investigating alternative sources such as spreadsheets, charts, logs, and even video recordings may yield valuable insights and uncover crucial clues. Human Interaction:  Conducting interviews with individuals directly impacted by the event can provide invaluable qualitative insights that may not be readily discernible from quantitative data. It is advisable to conduct these interviews promptly following the occurrence of the event to ensure the accuracy and freshness of the information. Historical Data Integrity:  When utilizing historical data, it is imperative to thoroughly investigate its storage methods and consult with individuals responsible for its management. This diligent approach will help to mitigate the risk of data misinterpretation and ensure the reliability of subsequent analyses. Conclusion In summary, a focused data collection strategy, encompassing defined requirements, source identification, and clear responsibilities, is crucial for accurate and efficient root cause analysis across diverse applications. Data Analytics Training Resources Analysts Builder Master key analytics tools. 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©Problem solving analysis/Shutterstock.com Introduction In a previous article , I explored the 6W Methodology, a structured framework for data analysis that encourages deeper inquiry beyond superficial observations. Building upon this, I will now introduce another powerful tool in the data analyst's arsenal: the Ishikawa Diagram, often referred to as the Fishbone Diagram. This visual technique is invaluable for identifying the root causes of metric shifts, whether positive or negative. This article will delve into: The Rationale Behind Ishikawa Diagrams:  Why this method is crucial for effective root cause analysis. A Step-by-Step Guide to Creating an Ishikawa Diagram:  A practical framework for its implementation. A Real-World Example:  Illustrating the application of the Ishikawa Diagram in a specific scenario. Key Considerations:  Important factors to keep in mind while utilizing this technique. Cross-Industry Applications:  Demonstrating the versatility of Ishikawa Diagrams across various sectors. By the end of this article, you will have a solid understanding of how to leverage Ishikawa Diagrams to effectively pinpoint root causes and drive data-driven decision-making within your organization. The Rationale Behind Ishikawa Diagrams The Ishikawa Diagram, also commonly known as a cause-and-effect diagram or fishbone diagram, was developed by Dr. Kaoru Ishikawa, a renowned Japanese quality control expert. Dr. Ishikawa recognized the need for a visual tool that could effectively capture and organize all potential causes contributing to a specific effect or problem. Why the Ishikawa Diagram is Essential for Effective Root Cause Analysis The Ishikawa diagram is a valuable tool for systematically identifying potential root causes of a problem. By visually mapping out various categories of potential inputs (such as people, methods, machinery, materials, and environment) and their potential impacts on the process or product, the Ishikawa diagram helps to: Brainstorm comprehensively:  Encourage a thorough exploration of potential causes that might otherwise be overlooked. Organize information:  Structure the brainstorming process and facilitate clear visualization of cause-and-effect relationships. Prioritize investigation:  Focus investigative efforts on the most likely root causes. Facilitate communication:  Clearly communicate the identified root causes to stakeholders. By utilizing the Ishikawa diagram, teams can move beyond superficial observations and delve deeper into the underlying factors contributing to a particular issue. A Step-by-Step Guide to Creating an Ishikawa Diagram  This section will outline the key steps involved in constructing an effective Ishikawa Diagram, beginning with a visual representation of its typical format. To effectively construct an Ishikawa Diagram: Establish the Structure: Create a fishbone diagram using a suitable tool like Excel or Lucidchart. Define the Effect: Clearly articulate the specific problem in the head of the fishbone. Determine Categories: Label the major branches with relevant categories. Common categories include personnel, materials, methods, machinery, measurements, and environment. However, select categories that are most relevant to the specific problem. Alternatively, consider frameworks like 6W or 5W1H, especially for data-related issues. Identify Potential Causes: List suspected causes under each category. A single cause may belong to multiple categories. Delve Deeper: Use the "5 Whys" technique to identify underlying causes by iteratively asking "why" five times for each identified cause. A Real-World Example Sierra Design, a fictitious e-commerce store specializing in graphic design products, is grappling with a critical challenge: an alarmingly high cart abandonment rate of 65%. This indicates that two-thirds of customers who add items to their cart ultimately fail to complete their purchase. To enhance revenue and bolster customer satisfaction, Sierra Design aims to achieve a substantial reduction in this rate, targeting a 45% decrease. This scenario provides an ideal context for applying the cause-and-effect fishbone diagram. By visually mapping out the various categories of potential inputs and their potential impacts on the cart abandonment rate, Sierra Design can gain valuable insights into the root causes of this critical business challenge. Let us explore an example of how this framework can be effectively implemented. Solutions The analysts, in collaboration with their team and colleagues, brainstormed potential input categories to investigate. These categories were carefully considered and subsequently highlighted before being integrated into the fishbone diagram template. Problem Statement:  High Cart Abandonment Rate (65%) Main Branches (Categories): People:    Customer Frustration (Poor website navigation, confusing checkout process, lack of trust) Customer Service Issues (Slow response times, unhelpful staff) Marketing Team (Inadequate targeting, ineffective messaging) Process: Lengthy Checkout Process (Too many steps, required account creation) Website Bugs and Glitches (Slow loading times, broken links, payment errors) Inadequate Product Information (Missing details, unclear descriptions, poor quality images) Technology: Website Platform Issues (Incompatibility with devices, slow server response) Payment Gateway Problems (Declined transactions, security concerns) Website Security Concerns (Lack of SSL encryption, insufficient data protection) Environment Competition (Aggressive pricing, superior customer experience from competitors) Economic Factors (High unemployment, inflation) Seasonal Fluctuations (Reduced demand during certain periods) Fishbone Diagram Solution Key Considerations Several critical factors must be considered when effectively utilizing Ishikawa Diagrams: Precise Problem Definition:  A clear and concise problem statement is crucial. Techniques like Pareto analysis can help refine it. Mitigating Analysis Paralysis:  The "5 Whys" method helps prevent getting overwhelmed by identifying root causes. Data-Driven Insights:  Utilize data analysis, customer feedback, and A/B testing to inform the investigation. Applications Across Industries The Ishikawa Diagram proves valuable across various fields. For Web Analysts:  It helps pinpoint the root causes of declining traffic, high bounce rates, and low conversion rates. Product Analysts:  Utilize it to analyze product defects, improve user experience, and overcome market entry challenges. Data Scientists:  Employ it to address data quality issues, enhance machine learning model performance, and analyze predictive model failures. Security Analysts:  Leverage it to investigate cybersecurity incidents, analyze data breaches, and understand the sources of cyber threats. Conclusion In conclusion, the Ishikawa Diagram offers a structured approach to root cause analysis, empowering professionals to drive data-driven decisions and achieve strategic objectives. Data Analytics Training Resources Analysts Builder: Master key analytics tools. Analysts Build provides in-depth training in SQL, Python, and Tableau, along with resources for career advancement. Use code ABNEW20OFF  for 20%  off. Details:   https://www.analystbuilder.com/?via=amara

Introduction John W. Gardner astutely observed that "The art of problem-solving lies not in finding the right answers, but in asking the right questions." When your digital platform experiences unexpected error spikes, effectively addressing the situation demands more than simply resolving the immediate issue. It requires a deeper dive to understand the underlying "why." This article introduces high-level step analysis – a robust methodology designed to dissect complex processes, pinpoint the root cause of errors, and cultivate more effective, data-driven troubleshooting practices. High-Level Step Analysis High-level step analysis entails the meticulous deconstruction of a complex process into a series of discrete stages. By scrutinizing each stage individually, potential points of failure can be identified and the root cause of the error can be effectively isolated. Troubleshooting Errors: A Systematic Approach 1. Visualize the Trend: Effective error analysis begins with a thorough visualization of error trends. Moving beyond basic line charts, employ specialized techniques to quickly identify and understand error spikes: Control Charts:  Utilize these to effectively distinguish between normal and abnormal variations in error rates over time. Process Behavior Charts:  These charts help to identify unusual patterns and potential anomalies within the error data. Indexing Number-Trends Series:  Visualize error rates using this method to easily spot sudden increases or decreases, indicating potential issues. To further refine your analysis, segment your data by error type or error code. This granularity helps pinpoint the most critical issues and prioritize your investigation efforts. 2. Correlate Errors with Releases: For the specific error types or codes identified during the trend analysis, carefully examine their correlation with recent system updates or deployments. Newly introduced code or configuration changes often serve as the root cause of unexpected error spikes. 3. Analyze User Sessions: To gain a deeper understanding of the error's impact, meticulously analyze user sessions occurring around the time of the spike. Leverage Session Recording Tools:  Tools such as Hotjar, FullStory, or Microsoft Clarity provide valuable visual insights into user interactions, allowing you to observe the precise sequence of events leading to the error. Analyze Clickstream Data:  Alternatively, analyze clickstream data to reconstruct the user journey. SQL lag window functions can be particularly helpful in this regard. By meticulously reconstructing the user journey, you can pinpoint the exact sequence of events that triggered the error. 4. Deep Dive into the Customer Journey: Following the initial error analysis, a comprehensive examination of the entire customer journey is crucial. Assess Impact:  Recognize that the severity of an error varies significantly. While site freezes can severely impact conversions, other errors may have a more minor impact. Stage-by-Stage Analysis:  To pinpoint the root cause, meticulously analyze each stage of the customer journey: Product Browsing:  Investigate issues with search, catalog loading, and navigation. Product Selection:  Analyze problems with variations, adding to cart, and inventory checks. Checkout Process:  Examine errors during payment, order confirmation, and shipping calculations. Order Fulfillment:  Investigate issues with order picking, packing, and shipping. Customer Support:  Analyze delays in responding to inquiries or resolving customer issues. By meticulously analyzing these stages, you gain valuable insights into user interactions and identify the precise factors contributing to the error spike. 5. Assess Impact on Conversions: Quantify the impact of the error on key metrics. Calculate Conversion Rates:  Determine conversion rates and goal completion rates before and after the error spike, focusing on the specific error scenario identified in step 4. Define Severity Levels:  Work closely with your product owner or analytics lead to define severity levels (e.g., P1, P2) based on acceptable revenue or conversion thresholds. This framework helps prioritize remediation efforts. By diligently assessing the impact on conversions and revenue, you can effectively communicate the severity of the issue and prioritize resolution accordingly. Benefits of High-Level Step Analysis High-level step analysis offers several significant advantages in troubleshooting and process improvement: Accelerated Troubleshooting:  By meticulously deconstructing complex processes into discrete stages, the source of the problem can be swiftly identified. This streamlined approach results in substantial time and resource savings. Enhanced Team Collaboration:  A shared understanding of the process, facilitated by this analysis, fosters smoother collaboration among development, operations, and support teams. Proactive Problem Prevention:  Scrutinizing each stage of the process proactively identifies potential points of failure. This proactive approach enables the implementation of preventative measures, mitigating the risk of future disruptions. Data-Driven Decision Making:  By leveraging data from diverse sources, such as server logs and support tickets, informed decisions can be made regarding problem resolution and future preventative measures. This data-driven approach ensures that solutions are effective and sustainable. A Real World Example Scenario: As a senior data analyst at an e-commerce company, I observed a concerning trend: a significant increase in online shopping cart abandonment rates. Upon further investigation, a surge in "Payment Processing Error" messages was identified. This alarming trend indicated that numerous customers were unable to complete their purchases due to credit card declines. Applying High-Level Step Analysis: To pinpoint the root cause of this issue, the high-level step analysis methodology outlined in this article was meticulously applied: Visualize the Trend: Control Charts were employed to monitor daily conversion rates and "Payment Processing Error" rates, enabling the identification of any unusual spikes or patterns in error rates. An Indexing Number-Trends Series was utilized to visualize the error rate trend, facilitating rapid identification of the onset of the decline. (For guidance on controlling index-number trend series, refer to this resource: https://shorturl.at/h7TJS ) Correlate Errors with Releases: Recent website updates and deployments were thoroughly reviewed, with a particular focus on changes related to the payment gateway integration. Analyze User Sessions: Quantum Metrics was leveraged to record user behavior during the checkout process, with a specific focus on sessions encountering "Payment Processing Errors." Clickstream data was analyzed using SQL lag window functions to meticulously track the sequence of events preceding the error, providing valuable insights into the user journey. Deep Dive into the Customer Journey: Checkout Process: User interactions with payment form fields were meticulously analyzed. A thorough investigation was conducted to determine if specific payment methods or card types exhibited a higher propensity for errors. Error messages returned by the payment gateway were carefully examined to identify specific failure reasons, such as invalid card details or insufficient funds. Customer Support: Support tickets related to payment issues during the decline period were meticulously analyzed. Assess Impact on Conversions: Conversion rates were meticulously calculated both before and after the error spike. The financial impact of the decline in conversions, in terms of lost revenue, was estimated. The severity of the issue was determined based on its impact on both revenue and customer experience. Solution: The analysis revealed that the recent deployment of a new payment gateway integration had inadvertently introduced a critical bug. This bug resulted in the transmission of incorrect data to the gateway, leading to a significant increase in declined transactions. Resolution: The development team promptly identified and rectified the bug within the payment gateway integration. As an immediate solution, a rollback to the previous integration version was implemented. Proactive communication regarding the issue was disseminated to all affected customers. Key Learnings: Proactive monitoring of key performance indicators (KPIs), such as conversion rates and error rates, is paramount for the early detection of anomalies. Session recording tools provide invaluable insights into user behavior, significantly aiding in the identification of root causes. Rigorous testing of all new integrations is essential to prevent unforeseen production issues. Note:  This case study effectively demonstrates the efficacy of high-level step analysis, in conjunction with data-driven techniques, in troubleshooting and resolving complex real-world problems within an e-commerce environment. Conclusion High-level step analysis is a powerful technique for troubleshooting error spikes in e-commerce platforms. By breaking down complex processes into manageable steps, you can quickly identify potential problem areas, prioritize investigations, and implement effective solutions. This approach not only helps resolve immediate issues but also improves the overall resilience and efficiency of your e-commerce operations. Data Analytics Training Resources Analysts Builder Master key analytics tools. Analysts Build provides in-depth training in SQL, Python, and Tableau, along with resources for career advancement. Use code ABNEW20OFF  for 20%  off. Details:   https://tinyurl.com/Analysts-Builder