The document provides an overview of test automation and discusses why organizations automate testing, the benefits of test automation including increased coverage, repeatability, and leverage of resources, and when automation may not be appropriate such as for unstable designs or applications with inexperienced testers. It emphasizes that test automation requires an initial investment and ongoing maintenance. Automation should not be seen as a way to reduce testing resources or compensate for lack of expertise. The document also outlines best practices for test automation, including developing a test framework to manage the process and avoid duplication of effort, and setting realistic expectations about the time required to realize benefits.
Driving Agile Product Development with ExperimentationSplit Software
The document discusses using experimentation to drive agile product development. It describes how experiments work by randomly assigning users to a control or treatment group to test hypotheses about new features. Key aspects covered include feature flags to target users, product instrumentation to capture metrics, and a statistical engine to analyze results. Well-designed experiments can rapidly deliver valuable software by ensuring outcomes are measured before wide releases. They provide benefits like reducing risk and improving the speed of iterating on ideas.
Drive Growth with Agile Product Development: How data-driven teams leverage e...Split Software
Historically, product managers spent a lot of their time in reactive mode, which made staying strategic increasingly difficult. That is no longer the case. Product teams today must measure and iterate throughout the product creation process to build products that are sticky, delightful and deliver obvious and immediate value to users.
We will discuss best practices for agile product management, where user insight, guidance and experimentation come together as continuous process.
We will also explore how product teams can pair deeper user insights with product experimentation to onboard new users, release new products and features, understand user feedback and continuously improve their products and user experience.
Making Your Hypothesis Work Harder to Inform Future Product StrategyOptimizely
At Treatwell, each experiment goes beyond improving a single business metric. Experimentation works to evolve their product while enriching customer insights in order to deliver the best digital experience to their users. Join Laura Howard, Lead Product Manager, and Dennis Meisner, Senior Product Analyst, to learn their secret to making their hypothesis work harder and how getting their hypothesis right has improved Treatwell’s funnel progression and order health, as well as helped them make critical decisions on their product experience.
This document discusses best practices for optimizing Optimizely performance, including:
1. Implementing the Optimizely snippet synchronously before other tags to avoid flashing and integration issues.
2. Using a content delivery network to deliver the snippet for fast loading.
3. Understanding the order of execution for Optimizely experiments to ensure proper prioritization and evaluation.
4. Avoiding common issues that can cause flashing like using regular JavaScript in experiments.
Test Everything: TrustRadius Delivers Customer Value with ExperimentationOptimizely
When done right, experimentation can help you validate the product you’re building and create winning customer experiences. And it doesn’t take a big engineering team to make this happen.
TrustRadius, the most trusted review site for business technology, uses experimentation to build an online community through website and server-side experimentation. The small but mighty TrustRadius team runs experiments throughout the buyer’s journey to engage different user personas and understand outcomes in real-time.
Watch the webinar recording featuring Rilo Stark, product manager at TrustRadius, and Jack Peden, senior software engineer, to understand their data-driven experimentation strategy and how TrustRadius uses Optimizely Web and Full Stack products to tailor experiences to different customer segments and mitigate risk through A/B/N and painted door tests.
For a company like Blue Apron that is radically transforming the way we buy, prepare and eat meals, experimentation is mission critical for delivering a great customer experience. Blue Apron doesn’t just think about experimenting to improve short term conversion, they focus on ways to impact longer term metrics like retention, referrals, and lifetime value.
Join John Cline, engineering manager at Blue Apron, to learn how his team has built their experimentation program on Optimizely’s platform.
Attend this webinar to learn:
-How Blue Apron built their experimentation program on top of Optimizely Full Stack
-How developers play a critical role in experimentation
-The key considerations for developers when thinking about experimentation
The document provides an overview of test automation and discusses why organizations automate testing, the benefits of test automation including increased coverage, repeatability, and leverage of resources, and when automation may not be appropriate such as for unstable designs or applications with inexperienced testers. It emphasizes that test automation requires an initial investment and ongoing maintenance. Automation should not be seen as a way to reduce testing resources or compensate for lack of expertise. The document also outlines best practices for test automation, including developing a test framework to manage the process and avoid duplication of effort, and setting realistic expectations about the time required to realize benefits.
Driving Agile Product Development with ExperimentationSplit Software
The document discusses using experimentation to drive agile product development. It describes how experiments work by randomly assigning users to a control or treatment group to test hypotheses about new features. Key aspects covered include feature flags to target users, product instrumentation to capture metrics, and a statistical engine to analyze results. Well-designed experiments can rapidly deliver valuable software by ensuring outcomes are measured before wide releases. They provide benefits like reducing risk and improving the speed of iterating on ideas.
Drive Growth with Agile Product Development: How data-driven teams leverage e...Split Software
Historically, product managers spent a lot of their time in reactive mode, which made staying strategic increasingly difficult. That is no longer the case. Product teams today must measure and iterate throughout the product creation process to build products that are sticky, delightful and deliver obvious and immediate value to users.
We will discuss best practices for agile product management, where user insight, guidance and experimentation come together as continuous process.
We will also explore how product teams can pair deeper user insights with product experimentation to onboard new users, release new products and features, understand user feedback and continuously improve their products and user experience.
Making Your Hypothesis Work Harder to Inform Future Product StrategyOptimizely
At Treatwell, each experiment goes beyond improving a single business metric. Experimentation works to evolve their product while enriching customer insights in order to deliver the best digital experience to their users. Join Laura Howard, Lead Product Manager, and Dennis Meisner, Senior Product Analyst, to learn their secret to making their hypothesis work harder and how getting their hypothesis right has improved Treatwell’s funnel progression and order health, as well as helped them make critical decisions on their product experience.
This document discusses best practices for optimizing Optimizely performance, including:
1. Implementing the Optimizely snippet synchronously before other tags to avoid flashing and integration issues.
2. Using a content delivery network to deliver the snippet for fast loading.
3. Understanding the order of execution for Optimizely experiments to ensure proper prioritization and evaluation.
4. Avoiding common issues that can cause flashing like using regular JavaScript in experiments.
Test Everything: TrustRadius Delivers Customer Value with ExperimentationOptimizely
When done right, experimentation can help you validate the product you’re building and create winning customer experiences. And it doesn’t take a big engineering team to make this happen.
TrustRadius, the most trusted review site for business technology, uses experimentation to build an online community through website and server-side experimentation. The small but mighty TrustRadius team runs experiments throughout the buyer’s journey to engage different user personas and understand outcomes in real-time.
Watch the webinar recording featuring Rilo Stark, product manager at TrustRadius, and Jack Peden, senior software engineer, to understand their data-driven experimentation strategy and how TrustRadius uses Optimizely Web and Full Stack products to tailor experiences to different customer segments and mitigate risk through A/B/N and painted door tests.
For a company like Blue Apron that is radically transforming the way we buy, prepare and eat meals, experimentation is mission critical for delivering a great customer experience. Blue Apron doesn’t just think about experimenting to improve short term conversion, they focus on ways to impact longer term metrics like retention, referrals, and lifetime value.
Join John Cline, engineering manager at Blue Apron, to learn how his team has built their experimentation program on Optimizely’s platform.
Attend this webinar to learn:
-How Blue Apron built their experimentation program on top of Optimizely Full Stack
-How developers play a critical role in experimentation
-The key considerations for developers when thinking about experimentation
Test environment management anti patterns Niall Crawford
A list of the top 8 Anti-Patterns* found in organizations Test Environment Management space. Anti-Patterns that cause disruption, low productivity, delivery delays and unwanted costs. And conversely recommended patterns to replace them.
Today, Atlassian runs experiments and personalization campaigns across all their products, campaigns, and commerce. But it wasn’t always like this. Atlassian started small and at their own pace, grew across the enterprise.
Join Tom Tsao, the head of eCommerce at Atlassian, to learn how they evolved, from the origins of their program, to their process and team structure.
-Learn how to build a program around digital experience optimization using Optimizely.
-Get advanced use cases that deliver business impact.
-Learn what metrics are important in measuring the growth of your program.
This document contains the resume of Dipti Kale. She has over 4 years of experience as a Test Analyst at Tata Consultancy Services and 6 months of internship experience at Seed Infotech. She has skills in languages like C, C++, Java, HTML, testing tools like HPQC, Selenium, databases like Oracle and MySQL. She holds a BCA degree from University of Pune. Her work experience includes manual and automation testing of web and desktop applications for clients like TCS and conducting testing of an HR management system during her internship. She is looking to enhance her skills in a challenging environment.
Negative testing is all about ensuring that a product or application under test does NOT fail when an unexpected input is being fed. The purpose of Negative testing is to break the system and to verify the application response during unintentional inputs.
To be most effective, test managers must develop and use metrics to help direct the testing effort and make informed recommendations about the software’s release readiness and associated risks. Because one important testing activity is to “measure” the quality of the software, test managers must measure the results of both the development and testing processes. Collecting, analyzing, and using metrics are complicated because many developers and testers are concerned that the metrics will be used against them. Join Rick Craig as he addresses common metrics—measures of product quality, defect removal efficiency, defect density, defect arrival rate, and testing status. Learn the guidelines for developing a test measurement program, rules of thumb for collecting data, and ways to avoid “metrics dysfunction.” Rick identifies several metrics paradigms and discusses the pros and cons of each.
This document promotes switching from Quality Center to qTest, citing several advantages of qTest for agile software testing. Quality Center is not well-suited for agile workflows, has poor usability and integration, and is very expensive. qTest is designed for agile teams, integrates seamlessly with popular agile tools, and provides better visibility, collaboration, and test case management capabilities. Migrating from Quality Center to qTest is straightforward and qTest users report improved efficiency and a better overall testing experience.
Micro understand without Micro managing: E.g., one can identify that a
specific tester is unable to execute a test case for 2 days due to a defect unresolved by developer
Intuit - How to Scale Your Experimentation ProgramOptimizely
Here’s the playbook Intuit uses to increase its experimentation velocity — even when they face traffic limitations.
Mike Loveridge is not new to running experimentation teams. Before Intuit, he built out programs at Ancestry.com, GE, Humana, and CheapOair. He's an expert at making experimentation work at high velocity, even in traffic-challenged situations.
In this webinar, Mike Loveridge shared his best practices for making CRO work at high velocity, key lessons from scaling multiple teams, and why he's bullish on the future of "test and learn".
Join us for another #ImpactSalesforceSaturday, a series of online Salesforce Saturday sessions.
We invite all – Developers – Administrators – Group Leaders – Consultants with advanced, intermediate or beginner level knowledge on Salesforce(Sales Cloud, Service Cloud, Pardot, Marketing Cloud, IOT, CPQ, Einstein, etc).
Topic: Drum into understanding of prediction builder with NBA
Date and Time: Saturday, October 3, 2020,
07:30 PM to 08:30 PM IST
Speaker: Rajat Jain
Rajat is a Salesforce Einstein Champion. He is a 8x Salesforce Certified and Currently working as a Program Specialist at MTX Group.
Agenda:
1. Introduction
2. Drum into understanding of prediction builder with NBA
This document provides a checklist of questions for vetting call center software providers. It covers key areas to evaluate such as uptime, scalability, redundancy, service level agreements, pricing, data security, support, implementation, APIs, integrations, operations, and compliance. For each area, 3-4 example questions are given to assess the provider's capabilities and policies in that area. The conclusion emphasizes that support quality should not be underestimated and the best providers revolutionize their industry by prioritizing customer needs.
This document introduces two statistical packages, Prophet and Causal Impact, that can help SEO recommendations get implemented by proving their value. Prophet uses an additive model to forecast performance based on past seasonal trends. Causal Impact infers the impact of interventions by comparing actual results to a synthetic control. The document provides an example of how these packages could be used to get signoff for an SEO proposal and then prove the value of changes after implementation. They help place SEO in a market context, quantify commercial ROI, and clearly show impact to gain developer buy-in for future work.
Influence of emphasized automation in ciBugRaptors
To choose testing during software development, Bugraptors always uses the Continuous Integration and continuous deployment to decide the way of testing i.e: Automation or Manual. It is very important to decide the testing during software development to ensure quality meeting project constraints.
Why and When to Use Automation in Software TestingV2Soft
Automation in software testing is becoming increasingly popular due to its ability to reduce costs, improve accuracy and efficiency, and allow for faster delivery of products. Automated testing can help developers identify bugs early in the development cycle, leading to fewer errors and better-quality software. Automation also reduces the need for manual testing, freeing up resources that can be used elsewhere. By automating specific tasks, testers can focus on more complex tasks that require human judgement and experience. Ultimately, automation helps reduce time-to-market while improving the quality of the product.
Testing Data & Data-Centric Applications - WhitepaperRyan Dowd
This document discusses the importance of data-centric testing for organizations that rely on data to drive their business. It provides an overview of a methodology for implementing data-centric testing that involves testing data during development and verifying data quality in production. Some key challenges discussed include the lack of tools specifically for data testing and the time required to create and manage test data sets. The methodology advocates for the involvement of developers, dedicated testers, and quality assurance in testing at the unit, integration and system levels with a focus on automated testing and data verification.
The Leaders Guide to Getting Started with Automated TestingJames Briers
Conventional testing is yesterday’s news, is required but needs the same overhaul that has happened in development. It needs to be a slicker operation that really identifies the risk associated with release and protects the business from serious system failure. The only way to achieve this is to remove the humans, they are prone to error, take a long time, cost a lot of money and don’t always do what they are told.
Automation needs to be adopted as a total process, not a bit part player. Historically automation has focussed on the User Interface, which can be a start, but is often woefully lacking. Implementing an Automation Eco-System, sees automation drive through to the interface or service layer, enabling far higher reuse of automated scripts, encompasses the environment and the test data within it’s strategy, providing a robust, repeatable and reusable asset.
Don’t just automate the obvious. Automation is not a black box testing technique. Rather it is mirroring the development and building an exercise schedule for the code. Take your testing to the next level and realise the real benefits of a modern Automation Eco-system.
Tackling software testing challenges in the agile eraQASymphony
This document provides an overview of testing challenges in the Agile development era and discusses different testing methodologies. It contains introductions to four chapters that will be included in the eBook. The chapters are written by Vu Lam, CEO of QASymphony, and Sellers Smith, Director of Quality Assurance and Agile Evangelist for Silverpop.
The first chapter discusses how testers need to be reimagined for the Agile age. Testers must adopt an Agile mindset and be involved earlier in the development process. They also need tools designed specifically for Agile testing. The second chapter explores different testing methods including automated, exploratory, and user acceptance testing. It advises using
Tips To Enhance Your Cross Browser Testing With Minimal Effort.pdfpCloudy
With millions of websites being developed every day, it becomes challenging to test them on different browsers. And more importantly not all of them survive.
OberservePoint - The Digital Data Quality PlaybookObservePoint
There is a big difference between having data and having correct data. But collecting correct, compliant digital data is a journey, not a destination. Here are ten steps to get you to data quality nirvana.
Test environment management anti patterns Niall Crawford
A list of the top 8 Anti-Patterns* found in organizations Test Environment Management space. Anti-Patterns that cause disruption, low productivity, delivery delays and unwanted costs. And conversely recommended patterns to replace them.
Today, Atlassian runs experiments and personalization campaigns across all their products, campaigns, and commerce. But it wasn’t always like this. Atlassian started small and at their own pace, grew across the enterprise.
Join Tom Tsao, the head of eCommerce at Atlassian, to learn how they evolved, from the origins of their program, to their process and team structure.
-Learn how to build a program around digital experience optimization using Optimizely.
-Get advanced use cases that deliver business impact.
-Learn what metrics are important in measuring the growth of your program.
This document contains the resume of Dipti Kale. She has over 4 years of experience as a Test Analyst at Tata Consultancy Services and 6 months of internship experience at Seed Infotech. She has skills in languages like C, C++, Java, HTML, testing tools like HPQC, Selenium, databases like Oracle and MySQL. She holds a BCA degree from University of Pune. Her work experience includes manual and automation testing of web and desktop applications for clients like TCS and conducting testing of an HR management system during her internship. She is looking to enhance her skills in a challenging environment.
Negative testing is all about ensuring that a product or application under test does NOT fail when an unexpected input is being fed. The purpose of Negative testing is to break the system and to verify the application response during unintentional inputs.
To be most effective, test managers must develop and use metrics to help direct the testing effort and make informed recommendations about the software’s release readiness and associated risks. Because one important testing activity is to “measure” the quality of the software, test managers must measure the results of both the development and testing processes. Collecting, analyzing, and using metrics are complicated because many developers and testers are concerned that the metrics will be used against them. Join Rick Craig as he addresses common metrics—measures of product quality, defect removal efficiency, defect density, defect arrival rate, and testing status. Learn the guidelines for developing a test measurement program, rules of thumb for collecting data, and ways to avoid “metrics dysfunction.” Rick identifies several metrics paradigms and discusses the pros and cons of each.
This document promotes switching from Quality Center to qTest, citing several advantages of qTest for agile software testing. Quality Center is not well-suited for agile workflows, has poor usability and integration, and is very expensive. qTest is designed for agile teams, integrates seamlessly with popular agile tools, and provides better visibility, collaboration, and test case management capabilities. Migrating from Quality Center to qTest is straightforward and qTest users report improved efficiency and a better overall testing experience.
Micro understand without Micro managing: E.g., one can identify that a
specific tester is unable to execute a test case for 2 days due to a defect unresolved by developer
Intuit - How to Scale Your Experimentation ProgramOptimizely
Here’s the playbook Intuit uses to increase its experimentation velocity — even when they face traffic limitations.
Mike Loveridge is not new to running experimentation teams. Before Intuit, he built out programs at Ancestry.com, GE, Humana, and CheapOair. He's an expert at making experimentation work at high velocity, even in traffic-challenged situations.
In this webinar, Mike Loveridge shared his best practices for making CRO work at high velocity, key lessons from scaling multiple teams, and why he's bullish on the future of "test and learn".
Join us for another #ImpactSalesforceSaturday, a series of online Salesforce Saturday sessions.
We invite all – Developers – Administrators – Group Leaders – Consultants with advanced, intermediate or beginner level knowledge on Salesforce(Sales Cloud, Service Cloud, Pardot, Marketing Cloud, IOT, CPQ, Einstein, etc).
Topic: Drum into understanding of prediction builder with NBA
Date and Time: Saturday, October 3, 2020,
07:30 PM to 08:30 PM IST
Speaker: Rajat Jain
Rajat is a Salesforce Einstein Champion. He is a 8x Salesforce Certified and Currently working as a Program Specialist at MTX Group.
Agenda:
1. Introduction
2. Drum into understanding of prediction builder with NBA
This document provides a checklist of questions for vetting call center software providers. It covers key areas to evaluate such as uptime, scalability, redundancy, service level agreements, pricing, data security, support, implementation, APIs, integrations, operations, and compliance. For each area, 3-4 example questions are given to assess the provider's capabilities and policies in that area. The conclusion emphasizes that support quality should not be underestimated and the best providers revolutionize their industry by prioritizing customer needs.
This document introduces two statistical packages, Prophet and Causal Impact, that can help SEO recommendations get implemented by proving their value. Prophet uses an additive model to forecast performance based on past seasonal trends. Causal Impact infers the impact of interventions by comparing actual results to a synthetic control. The document provides an example of how these packages could be used to get signoff for an SEO proposal and then prove the value of changes after implementation. They help place SEO in a market context, quantify commercial ROI, and clearly show impact to gain developer buy-in for future work.
Influence of emphasized automation in ciBugRaptors
To choose testing during software development, Bugraptors always uses the Continuous Integration and continuous deployment to decide the way of testing i.e: Automation or Manual. It is very important to decide the testing during software development to ensure quality meeting project constraints.
Why and When to Use Automation in Software TestingV2Soft
Automation in software testing is becoming increasingly popular due to its ability to reduce costs, improve accuracy and efficiency, and allow for faster delivery of products. Automated testing can help developers identify bugs early in the development cycle, leading to fewer errors and better-quality software. Automation also reduces the need for manual testing, freeing up resources that can be used elsewhere. By automating specific tasks, testers can focus on more complex tasks that require human judgement and experience. Ultimately, automation helps reduce time-to-market while improving the quality of the product.
Testing Data & Data-Centric Applications - WhitepaperRyan Dowd
This document discusses the importance of data-centric testing for organizations that rely on data to drive their business. It provides an overview of a methodology for implementing data-centric testing that involves testing data during development and verifying data quality in production. Some key challenges discussed include the lack of tools specifically for data testing and the time required to create and manage test data sets. The methodology advocates for the involvement of developers, dedicated testers, and quality assurance in testing at the unit, integration and system levels with a focus on automated testing and data verification.
The Leaders Guide to Getting Started with Automated TestingJames Briers
Conventional testing is yesterday’s news, is required but needs the same overhaul that has happened in development. It needs to be a slicker operation that really identifies the risk associated with release and protects the business from serious system failure. The only way to achieve this is to remove the humans, they are prone to error, take a long time, cost a lot of money and don’t always do what they are told.
Automation needs to be adopted as a total process, not a bit part player. Historically automation has focussed on the User Interface, which can be a start, but is often woefully lacking. Implementing an Automation Eco-System, sees automation drive through to the interface or service layer, enabling far higher reuse of automated scripts, encompasses the environment and the test data within it’s strategy, providing a robust, repeatable and reusable asset.
Don’t just automate the obvious. Automation is not a black box testing technique. Rather it is mirroring the development and building an exercise schedule for the code. Take your testing to the next level and realise the real benefits of a modern Automation Eco-system.
Tackling software testing challenges in the agile eraQASymphony
This document provides an overview of testing challenges in the Agile development era and discusses different testing methodologies. It contains introductions to four chapters that will be included in the eBook. The chapters are written by Vu Lam, CEO of QASymphony, and Sellers Smith, Director of Quality Assurance and Agile Evangelist for Silverpop.
The first chapter discusses how testers need to be reimagined for the Agile age. Testers must adopt an Agile mindset and be involved earlier in the development process. They also need tools designed specifically for Agile testing. The second chapter explores different testing methods including automated, exploratory, and user acceptance testing. It advises using
Tips To Enhance Your Cross Browser Testing With Minimal Effort.pdfpCloudy
With millions of websites being developed every day, it becomes challenging to test them on different browsers. And more importantly not all of them survive.
OberservePoint - The Digital Data Quality PlaybookObservePoint
There is a big difference between having data and having correct data. But collecting correct, compliant digital data is a journey, not a destination. Here are ten steps to get you to data quality nirvana.
Quality Engineering Best Practices Balancing Speed, Quality, and Precision fo...pcloudy2
In today’s business world, where customers are at the centre, people expect high-quality mobile and web applications. Users want their apps to work smoothly, be easy to use, and have quick response time. To keep up with these expectations, companies need to adopt strong quality engineering practices that maintain a balance between speed, quality, and precision.
Kanoah Tests is a test management tool that integrates seamlessly with JIRA. It allows coordinating all test management activities like planning, authoring, execution, and reporting from within JIRA. Users praise Kanoah Tests for its simple and elegant solution compared to other plugins, and for the responsive customer service. The tool provides features like test case authoring at the story level, test planning and execution, test importing, and a REST API for test automation. It offers benefits like centralized test management, end-to-end traceability, and real-time insights into testing progress through built-in reports.
The document discusses introducing automated testing to software projects using the Automated Testing Lifecycle Methodology (ATLM). The ATLM provides a structured six-phase approach to deciding on, acquiring, introducing, planning, executing, and reviewing automated testing. It addresses common misconceptions around test automation and outlines the methodology's phases and processes to help organizations implement automated testing successfully.
5 Tips to Bulletproof Your Analytics ImplementationObservePoint
Your digital properties—websites, mobile apps and more—are central to your business. And your customers spend an incredible 5.6 hours per day with digital media. With all of that data to collect—and the technology to pull reports instantly—marketers like you are now able to understand their customers like never before.
But is your web analytics implementation bulletproof?
In this newly released eBook, you will learn in five simple steps how to:
Produce data that you can trust
Use free debugging tools to spot-check your implementations
Avoid common mistakes in analytics validation
The Value of Data Governance & Performance MeasurementObservePoint
Driving growth requires collecting accurate, complete customer data and using that data to improve customer experiences and generate new revenue. So what do you do if your data is untrustworthy or incomplete?
In this tip sheet, The Value of Data Governance & Performance Measurement, you'll learn how you can leverage automated data governance and performance measurement to:
- Ensure data is standardized, unified, and validated—so that nothing slips through the cracks
- Test critical pathways to ensure quality experiences on your site
- Track end-to-end customer journeys for holistic insights
- Implement ongoing data validation and sophisticated attribution to drive growth
Automation simplifies and speeds up the testing process for large projects. Test automation is crucial to achieve test coverage and speed for large projects. A combination of manual testing and test automation can provide adequate test coverage. Automation testing powered by crowd sourcing provides a cost-effective solution that helps access skilled testing experts and combat challenges in achieving full test coverage. Some benefits of crowd-sourced automation include expert support in creating scripts, script maintenance, ability to test on different devices, and savings in time and money.
Automation testing is crucial for large projects to achieve test coverage and speed. It is best suited when tests are repetitive, such as regression testing of unchanged parts of an application. Automation allows companies to execute repetitive and difficult tests faster to get quick feedback on new builds. However, automation requires significant investment and effort, so it is best to start with critical workflows that are stable and unlikely to change. Leveraging a crowd testing platform can help combat challenges in achieving full test coverage through a strategic combination of in-house and crowd-sourced testing.
How to Improve Quality and Efficiency Using Test Data AnalyticsTequra Analytics
Discover 8 ways in our guide for advanced manufacturers.
Do you perform advanced manufacturing in an industry such as aerospace, automotive, medical devices or telecoms? Is product testing part of your manufacturing process? If you can answer yes to these questions, keep reading to learn how test data analytics can enable many improvements.
The document discusses key factors to consider when choosing a provider for an online survey and feedback management system. It outlines questions in two categories - feature factors and company factors. Feature factors include questions about functionality, customization options, reporting capabilities, and ease of use. Company factors include the provider's stability, support resources, account management structure, and commitment to ensuring client success. The document advises evaluating these criteria to identify the right solution and safeguard important data, time, and financial investment.
Appliance Warehouse Service Plan.The discussion focuses on the.docxfestockton
Appliance Warehouse Service Plan.
The discussion focuses on the appliance Warehouse Service Plan that is made up of the testing plan, an implementation plan and the training plan for the sake of the bettering of services in a warehouse. The testing plan is meant to manage the systems through QA standards meeting the needs of the customers. The implementation plan elaborates and indicates whether one should use parallel, direct, phased, or pilot changeover strategies. The training plan, on the other hand, indicates what a training plan would include for affected employees, such as appointment setters, technicians, management, and the parts department.
Testing Plan
The main reason for the testing plan is to validate and verify the information from the main source or the end to end target warehouse. The two major testing plans for include program testing and acceptance testing (Lewis, 2017). The plan should verify the following, the business required documents, ETL design for the documents, sources to target on the mapping process and the data model for the source and the target schemas. The documents that are considered are meant for the ETL development process in the testing plan. The testing plan is meant further for the supervisors or the quality analysis team to confirm that the work is concerning the objective of the organization. The process of testing might also include the configuration management system and the data quality validation and verification process.
Implementation Plan
The plan for the implementation of the systems is the same as the process that is considered during the development process of the entire system to meet the goals of the organization. The steps to consider for the whole plan of the implementation include the analysis and the enhancement requests, the writing of very simplified and new programs, restructuring of the database, analysis of the program library and its cost, and the reengineering of the test program. The first phase parallels the analysis phase as the parallel strategy is considered for the entire process, which entails the analysis phase of the SDLC. The steps two to four process entails the combining and the construction activities that are done on a new system majorly on a small scale. The last step is meant to parallel the testing that is commonly done during the implementation process. The testing process ensures that the process is free of risk as a quality assurance process (Liang & Hui, 2016).
Training Plan
The training plan should be made up of a training matrix in which it will guide them to know who needs the training what they need from the training and why they want the training not forgetting when they need the training(Kwak,2016). The matrix will allow for the planning and the preparation for the training avoiding scrambling when the due date for the training comes around. The requirements are automatically updated when the employees get done with the first training before transferri ...
Appliance Warehouse Service Plan.The discussion focuses on the.docxRAHUL126667
Appliance Warehouse Service Plan.
The discussion focuses on the appliance Warehouse Service Plan that is made up of the testing plan, an implementation plan and the training plan for the sake of the bettering of services in a warehouse. The testing plan is meant to manage the systems through QA standards meeting the needs of the customers. The implementation plan elaborates and indicates whether one should use parallel, direct, phased, or pilot changeover strategies. The training plan, on the other hand, indicates what a training plan would include for affected employees, such as appointment setters, technicians, management, and the parts department.
Testing Plan
The main reason for the testing plan is to validate and verify the information from the main source or the end to end target warehouse. The two major testing plans for include program testing and acceptance testing (Lewis, 2017). The plan should verify the following, the business required documents, ETL design for the documents, sources to target on the mapping process and the data model for the source and the target schemas. The documents that are considered are meant for the ETL development process in the testing plan. The testing plan is meant further for the supervisors or the quality analysis team to confirm that the work is concerning the objective of the organization. The process of testing might also include the configuration management system and the data quality validation and verification process.
Implementation Plan
The plan for the implementation of the systems is the same as the process that is considered during the development process of the entire system to meet the goals of the organization. The steps to consider for the whole plan of the implementation include the analysis and the enhancement requests, the writing of very simplified and new programs, restructuring of the database, analysis of the program library and its cost, and the reengineering of the test program. The first phase parallels the analysis phase as the parallel strategy is considered for the entire process, which entails the analysis phase of the SDLC. The steps two to four process entails the combining and the construction activities that are done on a new system majorly on a small scale. The last step is meant to parallel the testing that is commonly done during the implementation process. The testing process ensures that the process is free of risk as a quality assurance process (Liang & Hui, 2016).
Training Plan
The training plan should be made up of a training matrix in which it will guide them to know who needs the training what they need from the training and why they want the training not forgetting when they need the training(Kwak,2016). The matrix will allow for the planning and the preparation for the training avoiding scrambling when the due date for the training comes around. The requirements are automatically updated when the employees get done with the first training before transferri.
Similar to Order vs Chaos: Taming Digital Analytics Complexity With Automation (20)
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2. 2
Table of Contents
Introduction
You Need to Test
Build or Purchase an Automated Testing Solution
Implement Your Automated Testing Solution
Benefits of Automated Testing
Establish Analytics Order and Move Your Business Forward
3
4
6
7
9
11
3. 3
INTRODUCTION
As the world of digital analytics moves forward, tracking customer behavior becomes more
and more sophisticated, making the processes involved in collecting accurate customer data
increasingly complex.
This complexity comes largely from the fact that companies create extensively complicated
analytics implementations in an effort to track vast amounts of customer data across their
digital properties, and then these companies struggle to manage and maintain their imple-
mentations over time because of the following:
So, what can companies do to manage the complexity involved in maintaining accurate data?
The answer: Automated analytics testing.
Ever-changing websites and analytics implementations. Websites and their
associated analytics implementations change frequently, creating a moving target
for analytics professionals.
Disorganized analytics tagging. Robust analytics implementations are large and
complex, often containing thousands of tags, which opens the door for potential tagging
disorganization if analytics professionals aren’t careful.
Team misalignment. When teams don’t communicate effectively, their implementation
can become misconfigured and data integrity suffers as a result. A change made by one
team can break a requirement of another.
4. 4
Analytics testing is the process of auditing the analytics
implementation associated with your website or mobile
app to ensure your tracking meets expectations and
collects the right data. Regularly testing your analytics
implementation is critical to maintaining accurate data
on an ongoing basis.
There are two primary methods of conducting
analytics testing:
Through manual testing
Through test automation
Manually testing an analytics implementation is the
lesser of the two options and generally involves using
the developer tools in your browser to inspect individ-
ual tags on your site to make sure they are meeting
your expectations. This process requires using human
effort to comb through lines and lines of code, verifying
analytics tags one-by-one.
Manual analytics testing is highly time-intensive and prone
to human error, as the tedious nature of manual testing
leads people to regularly miss analytics tracking errors
that negatively impact accuracy and customer experienc-
es. Additionally, as digital analytics tracking increases in
complexity, manually validating analytics implementations
becomes harder and more time-consuming.
However, there are times when manual testing is appro-
priate. For example, if you’re testing the tracking on a cus-
tomer journey that will only be run one or two times, or if
the nature of your test is extremely dynamic, you would
not want to take the time and resources to set up an auto-
mated test of that journey.
Setting up automated testing for that journey would likely
take longer than simply testing the journey manually.
Still, due to the cumbersome and error-prone nature
of manual analytics tracking, we recommend minimiz-
ing this route wherever possible. In most cases, you will
be better off utilizing an automated testing solution like
ObservePoint, but you need to weigh the needs of each
testing situation individually.
You Need to Test
These two testing strategies are not created equal.
Let’s dive into each separately.
Manual Testing
5. 5
The complexity involved with analytics tracking doesn’t
have to keep you from obtaining reliable insights, especial-
ly since this complexity becomes manageable when you
apply test automation to your analytics implementation.
The goal of automated testing is to continually test the
functionality of each component of your analytics imple-
mentation to ensure you obtain valuable insights from ac-
curate data on an ongoing basis.
More specifically, the process of utilizing test automation
for analytics data entails the use of automated audits to
run against your analytics implementation following a
change. These audits check for tracking errors such as
missing tags, missing variable data, incorrect formatting of
variable data, and other errors. A fundamental piece to
discovering these errors involves setting up tests—called
Rules in ObservePoint—within your solution that allow
you to compare and evaluate individual elements of your
implementation to make sure your expectations are being
met at the tag, variable, and variable-value level.
Regularly performing these tests will help you ensure the
integrity of your implementation by systematically scanning
your website pages for tagging errors to ensure accurate
data collection.
In order to effectively utilize an automated testing solution,
you need to complete a couple of steps.
1. Build or purchase an automated testing solution
2. Implement your automated testing solution with the
following best practices:
Target your analytics testing
Use monitoring and alerts
Train and align your teams on your analytics testing strategy
Synchronize tests with implementation updates
Automated Testing
What you need to do
Let’s jump into each of these steps.
6. 6
Obviously, if you want to engage in automated testing, you
need a testing solution. Your options here are to either
build an automated testing solution yourself or purchase
a pre-existing third-party option like ObservePoint.
Due to the labor-intensive nature of building and main-
taining an automated testing solution yourself, you’re
likely better off finding a pre-existing solution, especially
since you will be able to configure and customize a well-
made automated testing solution to your specific needs
at a much quicker rate than you could ever build one.
Also, with a third-party solution, you don’t have to worry
about having someone on your team maintaining and up-
dating that solution as you move your business forward.
Additionally, with a third-party solution, the third party
takes care of all the needs of your users. An adequate
third party solution provider will have extensive resources
dedicated to supporting your users so your users get the
most out of your solution. The third-party will even help
you get buy-in from others in your organization.
Regardless of your choice between building and buying,
once you have your solution up and running, you will need
to set up testing processes to ensure your analytics imple-
mentation is collecting accurate data on an ongoing basis.
These processes are outlined in the remaining steps.
Build or Purchase an Automated Testing Solution
Building a solution yourself is possible but is also a task of
serious magnitude, as doing so would require individuals
with comprehensive expertise in data collection, process-
ing, and querying. You would also need to consider how to
incorporate functional visualization, UX/UI, notifications,
and reporting functionality.
Additionally, if you build your own solution, you will have to
deal with the extensive burden of training. You will have to
train and support all the individuals who use the solution,
and essentially sell your solution to different teams in order
to get company-wide buy-in. The people in your organiza-
tion involved with using the solution basically become your
customers, and those people need to be supported, which
will require a significant amount of time and resources.
Also, even if you were able to build a solution, you would
need to dedicate manpower to updating and maintaining
that solution over time so the solution continues to ade-
quately serve your needs as time goes on.
Undoubtedly, you would need to dedicate extensive man-
hours and resources to the creation, customization, and
maintenance of such a solution.
Build your own solution
Purchase a third-party solution
6
7. 7
Once you have an automated testing solution at your
disposal, you’ll want to strategically deploy it to yield the
greatest benefit. Here are some tips to get you started. You need to regularly keep tabs on your implementation.
As a result, you should use an automated testing solu-
tion to set up monitoring and alerting to regularly scan
your site’s analytics implementation and alert you when
something goes wrong. With the help of integrations, you
can configure these alerts to notify you of errors through
your preferred means of communication, whether that be
through email, Slack, text, or in-app notifications.
By using monitoring and alerts, you can verify the integrity
of your analytics solution is preserved on an ongoing basis.
Once you have your solution and processes in place by
going through the previous steps, you’ll want to train and
align your team members around those processes.
Implement Your Automated Testing Solution
A key way to ensure accurate data over time is to target
your analytics testing. You have critical, high-traffic pages
and user journeys on your site, and testing these high-traf-
fic elements needs to take precedence over testing other
site elements. With an automated testing solution, you
should prioritize testing on these pages and user paths
by focusing frequent scans on these critical site elements.
As a rough guideline, you should prioritize running daily
testing on your top 5-10 critical customer experiences,
but you could easily have more than that if your site is
especially large. Just remember that tests require mainte-
nance of their own, so the more tests you run, the more
you have to maintain.
Target Your Analytics Testing
Use Monitoring and Alerts
8. 8
Luckily, an effective automated testing solution will help
you align communication between your teams. An ade-
quate solution will allow you to accomplish the following:
Configure notification settings to alert and align
team members on problems as they arise.
Create data owners who are responsible for specific
aspects of the data collection process.
Use permissions to keep unauthorized employees
from messing with data.
Train and Align Your Teams On Your
Analytics Testing Strategy
Synchronize Tests With
Implementation Updates
For automated testing to be effective for your organization,
you need to train and align your teams around processes
and practices that ensure accurate data. You do yourself
no favors if you obtain a testing solution, put a bunch of
analytics processes in place, and then fail to organize and
optimize the manpower behind those processes.
The last step involves synchronizing your tests with up-
dates (also known as release validation). Release validation
is the practice of running timely tests after making changes
to your website and/or the associated analytics implemen-
tation. To maintain accurate data on an ongoing basis, you
should conduct regular release validation as your site and
analytics implementation go through updates.
Additionally, to ensure ongoing alignment, you’ll want to
appoint a data champion with company clout (prefera-
bly someone on the C-level) who can be the driving force
behind your analytics testing strategy and give you the
support you need.
A well-rounded automated testing solution will allow you
to schedule—or better yet, trigger—automatic tests when-
ever you make changes to your analytics implementation
or website, which will help you quickly resolve potential
errors before they impact your data or your customers.
Now, keep in mind that automated testing isn’t a silver bul-
let, as you still have to deal with maintaining your testing
solution and continually calibrating your tests. But overall,
the benefits of automated testing far outweigh the costs.
Once you accomplish the aforementioned steps, you put
yourself in a position to reap all the benefits of using an
automated testing solution.
9. 9
Let’s break down each of these benefits individually.
Change is an inevitable part of managing websites and
analytics implementations, and an automated test-
ing solution will allow you to monitor the integrity of
your analytics implementation through each and every
change as time goes on.
Tagging can get messy, especially with large, complex im-
plementations. An automated testing solution will help
you ensure all the tags on your site are in the appropriate
place and functioning properly, and you can be notified
whenever your tagging goes sour.
You will also be able to ensure your site is complying
with regulations and protecting your customers, as an
automated testing solution can help you quickly find and
eliminate any rogue or piggybacking tags on your site that
could be compromising your data.
Benefits of Automated Testing
Effectively using an automated testing solution will
help you:
Manage the ever-changing nature of websites
and analytics implementations
Use site-wide tag governance to monitor
tags and comply with regulations
Gain ongoing confidence in your analytics
data for decision-making
Align teams around your analytics
testing strategy
Save time and resources
Increase your data quality
Manage the ever-changing
nature of websites and analytics
implementations
Use site-wide tag governance to
monitor tags and comply with
regulations
10. 10
Managing an analytics strategy is a dynamic process with
lots of moving parts, which creates opportunities for mis-
communication and misalignment between teams. An
automated testing solution will help you align teams and
eliminate potential miscommunication by allowing you to
set up data owners, user permissions, and notifications.
Using an automated testing solution frees you from having
to expend manual effort on monitoring your implementa-
tion. As a result, you’ll save time and human energy that
can be spent on increasing your analytics testing capacity—
helping you obtain even more accurate insights—or other
important initiatives that will move your business forward.
Through automated testing, you can quickly obtain knowl-
edge about the status of analytics tags on your site, in-
cluding if any tags, variables, or values are missing or if du-
plicate tags are present. These tests allow you to quickly
discover errors in your implementation and ensure data
quality on an ongoing basis.
By increasing your data accuracy and saving time and re-
sources, you are able to more fully dedicate your teams
to understanding your customers. In turn, this greater
understanding allows your company to confidently serve
your customers through customized experiences.
Align teams around your
analytics strategy
Save time and resources
Increase your data quality
Gain conf idence in your analytics
data for decision-making
11. 11
Establish Analytics Order and
Move Your Business Forward
Utilizing an automated testing solution makes the sheer complexity of a robust analytics implementation manageable,
especially because these testing solutions help teams hit the moving target of an ever-changing analytics implementation.
Ultimately, the chaos associated with managing an analytics implementation can be transformed into order with the help of
an automated testing solution, as these solutions allow you to put your focus into creating more accurate data and moving
your business forward with other priorities.
Schedule Demo
To find out how ObservePoint’s automated testing solution can help you bring order to
your digital analytics practice, schedule a demo today.