Traditional approaches to strategic analysis focus on finding the single best path forward, but businesses who deal with uncertainty may require a strategic experimentation approach. Find out if this approach is right for you.
The document discusses decision making and various decision making processes. It defines decision as a choice between two or more alternatives. Decision making involves setting objectives, perceiving problems, analyzing problems, developing alternative solutions, screening alternatives, selecting the best alternative, implementing decisions, and providing feedback and control. It then describes the steps in the rational decision making process and various decision making techniques used, including operational research, the Delphi method, expert panels, decision trees, and responsibility for decision making within organizations.
We often want to know if our product will work as expected in our customers environment. Creating an environmental test plan that clearly shows weaknesses and strengths should be part of your program for each product.
Let’s explore the steps and resources you should consider when creating an environmental test plan for each product.
We will discuss why using standards or even the plan from the last program may not provide you meaningful information. Understanding how your product responds to your customer’s environment provides the information for your team to make improvements and improve product reliability.
More a discussion than a lecture, let’s talk about the challenges and benefits to creating a unique environmental test plan for each product under development.
This Accendo Reliability webinar originally broadcast on 11 November 2014.
Common Data Driven Mistakes with HAVI's Sr. Director of Advanced AnalyticsPromotable
You've received your data, found important insights for your business and are ready to present the information to your leadership. Could you be making a costly mistake?
In this talk, we discuss the importance of exploratory data analysis, what questions to ask from your data to make sure that the results are accurate, and the importance of applying business domain knowledge to your findings so you don't identify insights that could be both incorrect and very costly.
Takeaways:
What are the right questions to ask?
Common mistakes that lead to inaccurate results.
What to watch out for (correlation vs. causality, spurious results, and more)
Your Instructor: Jeanette Shutay is Senior Director of Advanced Analytics where she leads the Center of Excellence at HAVI, which is a leading organization whose services provide insights and solutions for the world’s largest foodservice brands.
The document discusses implementing an agile approach to developing predictive models at Allstate Insurance. It outlines how agile allows for more incremental and iterative development, with the ability to test and improve models more quickly. An agile process was adopted with sprints, backlogs, and daily standup meetings. This resulted in 38 predictive models being developed over 12 research and development sprints within 10 months, with only 3 model failures, allowing for faster delivery of business value.
This document outlines the framework for creating a data-driven experiment from start to finish. It includes identifying the problem being solved, forming a hypothesis based on qualitative and quantitative data, selecting target audiences and test locations, determining the primary and secondary metrics to track, and establishing the duration and proposed change for the experiment. The next steps would be to prioritize the experiment against other ideas and determine the effort required to design, build, and test the changes.
The document outlines the steps for outsourcing a division, including gathering facts, brainstorming solutions, identifying advantages and disadvantages of solutions, deciding on the most appropriate solution, implementing the solution, following through on results, defining issues that need changed, and repeating the process. It discusses using prescriptive and descriptive decision making and defending the reasoning for outsourcing through a transparent problem-solving approach. The resources cited provide additional information on factors that affect decisions, improving decision making, ethical considerations, and board room dynamics.
This document discusses RightShip's implementation of a maritime risk model to assess over 35,000 vessels per year and identify over 1,000 high-risk vessels for exclusion from vetting. The model considers inputs like vessel age, size, builder, casualties, and regulatory performance to predict the likelihood of an incident in the next 12 months. It uses decision trees to provide risk ratings and was launched after communication efforts including webinars, videos, and email campaigns. The model is retrained quarterly as new data arrives to improve accuracy, with the first retrain seeing over 50% of vessel ratings change and an improved accuracy score.
PROMISE 2011: What Prediction Model Should Be?CS, NcState
This document discusses establishing a prediction model to predict testing effort and schedule for a software development organization. It suggests identifying controllable factors that impact testing from historical project data, such as defects injected at different stages. Prediction models would be established using these factors to predict testing effort and schedule. The models would help manage iteration schedules and system testing to meet goals like reducing defects and detecting more during testing. Ongoing data collection and refinement of models is important.
The document discusses decision making and various decision making processes. It defines decision as a choice between two or more alternatives. Decision making involves setting objectives, perceiving problems, analyzing problems, developing alternative solutions, screening alternatives, selecting the best alternative, implementing decisions, and providing feedback and control. It then describes the steps in the rational decision making process and various decision making techniques used, including operational research, the Delphi method, expert panels, decision trees, and responsibility for decision making within organizations.
We often want to know if our product will work as expected in our customers environment. Creating an environmental test plan that clearly shows weaknesses and strengths should be part of your program for each product.
Let’s explore the steps and resources you should consider when creating an environmental test plan for each product.
We will discuss why using standards or even the plan from the last program may not provide you meaningful information. Understanding how your product responds to your customer’s environment provides the information for your team to make improvements and improve product reliability.
More a discussion than a lecture, let’s talk about the challenges and benefits to creating a unique environmental test plan for each product under development.
This Accendo Reliability webinar originally broadcast on 11 November 2014.
Common Data Driven Mistakes with HAVI's Sr. Director of Advanced AnalyticsPromotable
You've received your data, found important insights for your business and are ready to present the information to your leadership. Could you be making a costly mistake?
In this talk, we discuss the importance of exploratory data analysis, what questions to ask from your data to make sure that the results are accurate, and the importance of applying business domain knowledge to your findings so you don't identify insights that could be both incorrect and very costly.
Takeaways:
What are the right questions to ask?
Common mistakes that lead to inaccurate results.
What to watch out for (correlation vs. causality, spurious results, and more)
Your Instructor: Jeanette Shutay is Senior Director of Advanced Analytics where she leads the Center of Excellence at HAVI, which is a leading organization whose services provide insights and solutions for the world’s largest foodservice brands.
The document discusses implementing an agile approach to developing predictive models at Allstate Insurance. It outlines how agile allows for more incremental and iterative development, with the ability to test and improve models more quickly. An agile process was adopted with sprints, backlogs, and daily standup meetings. This resulted in 38 predictive models being developed over 12 research and development sprints within 10 months, with only 3 model failures, allowing for faster delivery of business value.
This document outlines the framework for creating a data-driven experiment from start to finish. It includes identifying the problem being solved, forming a hypothesis based on qualitative and quantitative data, selecting target audiences and test locations, determining the primary and secondary metrics to track, and establishing the duration and proposed change for the experiment. The next steps would be to prioritize the experiment against other ideas and determine the effort required to design, build, and test the changes.
The document outlines the steps for outsourcing a division, including gathering facts, brainstorming solutions, identifying advantages and disadvantages of solutions, deciding on the most appropriate solution, implementing the solution, following through on results, defining issues that need changed, and repeating the process. It discusses using prescriptive and descriptive decision making and defending the reasoning for outsourcing through a transparent problem-solving approach. The resources cited provide additional information on factors that affect decisions, improving decision making, ethical considerations, and board room dynamics.
This document discusses RightShip's implementation of a maritime risk model to assess over 35,000 vessels per year and identify over 1,000 high-risk vessels for exclusion from vetting. The model considers inputs like vessel age, size, builder, casualties, and regulatory performance to predict the likelihood of an incident in the next 12 months. It uses decision trees to provide risk ratings and was launched after communication efforts including webinars, videos, and email campaigns. The model is retrained quarterly as new data arrives to improve accuracy, with the first retrain seeing over 50% of vessel ratings change and an improved accuracy score.
PROMISE 2011: What Prediction Model Should Be?CS, NcState
This document discusses establishing a prediction model to predict testing effort and schedule for a software development organization. It suggests identifying controllable factors that impact testing from historical project data, such as defects injected at different stages. Prediction models would be established using these factors to predict testing effort and schedule. The models would help manage iteration schedules and system testing to meet goals like reducing defects and detecting more during testing. Ongoing data collection and refinement of models is important.
A focused practice aimed at using simulations from simple System Dynamics models to help us better understand the intended and unintended consequences of our actions.
This document discusses challenges with software estimation and provides strategies for improving estimates. It notes that estimation is necessary but often inaccurate, with typical cost overruns of 30-40%. Expert estimation is most common but formal methods don't consistently improve accuracy. Prediction intervals from students tend to be better than experts who may feel pressure to appear more skilled. Common causes of overruns include optimistic plans and frequent changes. The document suggests using complexity measures and conversion factors to generate more realistic estimates.
The document discusses Social Six Sigma, which adapts the DMAIC (Define, Measure, Analyze, Improve, Control) methodology used in manufacturing process improvement to social media strategies. It defines each phase for social media as follows:
Define means listening to understand customer wants from both direct and indirect feedback on social media. Measure means engaging customers to clarify and expand on their feedback to obtain complete data for analysis. Analyze means analyzing what is important both to the organization and customers based on the collected data. Improve means addressing customer concerns and using reaction data to refine social media strategies. Control means making changes to social strategies based on new data and monitoring to ensure the right adjustments were made.
The document discusses principles for effective performance management goals. It recommends that goals should be connected to overall objectives, actionable to allow controlling outcomes, predictive of big-picture metrics, and set up for sustained results over time. It provides guidance on cascading goals from overall objectives and defining metrics that move from low-level and actionable to predictive of higher goals. Tools like Tashiro charts, flag charts, and balanced scorecards can help sustain momentum and results. Even inherently non-quantitative efforts can track leading indicators to monitor progress.
In developing research for impact, science should support decisions as decision makers are always hungry for information.
Research should be tailored to specifically address particular decisions. This can be achived through direct engagement with decison makers especially in decision making under uncertainity.
Tailor research specifically to address particular decisions
1. A/B testing involves splitting users into a test group that sees a new feature and a control group that sees the original version. Users are typically assigned randomly with equal percentages to each group.
2. Metrics are tracked for both groups to measure the impact of the new feature, such as additions to cart or purchases. Significance testing determines if results are likely real or due to chance.
3. Long-term experiments require holding back some users from the test to measure novelty or learning curve effects over time.
This document outlines the problem solving process, which includes gathering and analyzing data, developing alternative solutions, evaluating options, implementing the chosen solution, monitoring and managing the solution, verifying the solution, using adaptive techniques when appropriate, and developing ethical solutions. Guidelines are provided for each step of the process.
The document discusses structured problem solving techniques including situational awareness, process mapping, identifying customer requirements, problem identification, root cause analysis, implementing changes, and control methods. It provides examples of tools that can be used at each step such as affinity diagrams, histograms, scatter plots, 5 whys, tree diagrams, and benchmarks.
Serve your customers better with User Experience ResearchAmanda Stockwell
This document discusses user experience (UX) research and marketing research. It defines UX research as understanding users and the context in which they use products in order to uncover opportunities and understand why things happen from the user's perspective. Marketing research is defined as understanding purchasers and the context of purchase in order to uncover market opportunities and understand what is happening from the company's perspective. The document then outlines different types of research methods that can be used for UX and marketing research like interviews, usability testing, surveys, and analytics reviews. It provides guidance on choosing methods based on the product stage and type of questions being asked.
The document describes an intelligent decision system that can be used to support business innovation assessments. The system was developed as part of an EU-funded project to create a model for assessing innovation capabilities in businesses. It uses an evidential reasoning approach to model uncertainties and aggregate information from self-assessments. The system provides various outputs like rankings, strengths/weaknesses identification, and sensitivity analysis to help with assessment consistency, effectiveness and clear communication. It has applications in areas like business excellence, risk, supplier and customer satisfaction assessments.
1) How to design powerful experiments provides tips for setting up successful A/B tests and experiments to make data-driven decisions and reduce risks.
2) Key tips include having the proper experimentation infrastructure in place, following an iterative process of developing hypotheses, designing experiments, analyzing results, and executing.
3) Case studies show that A/B tests at Google and REA Group led to increased annual revenue and conversion rates, demonstrating the value of experimentation.
This document discusses ways to improve a company's analytic maturity. It begins by introducing the author and their background in analytics. It then discusses viewing analytics from a company perspective using an "Analytic Diamond" model. It introduces an Analytic Maturity Model consisting of 5 levels from basic reporting to strategy-driven analytics. Key differences between the levels are described. The document provides two case studies and discusses goals of analytic governance. It concludes by summarizing five ways to get started improving maturity and seven key process areas to focus on.
The document outlines the 5 main steps of a research process: 1) Locating and defining the issue or problem, 2) Designing the research project to solve the problem, 3) Collecting necessary data, 4) Interpreting the data to draw conclusions, 5) Reporting findings and conclusions to relevant parties through written reports and oral presentations using visual aids.
This document discusses strategies for integrating segmentation and predictive modeling. It begins by outlining a typical agenda, including whether to use segmentation, modeling, or both. It then covers strategic approaches like value-based behavioral segmentation and clustering to define customer segments. Tactical segmentation involves using outcomes from predictive models to segment customers. The document provides examples of integrating segmentation with different modeling techniques and discusses how segmented models can outperform single models. It emphasizes that both strategic and tactical approaches are useful but strategic provides more insights for improving communications.
The document outlines the key steps in the marketing research process, including defining the problem, developing an approach, formulating a research design, data collection and analysis, and reporting. It discusses defining the management decision problem versus the marketing research problem, and exploring different types of research design such as exploratory, descriptive, and causal research. The marketing research process aims to systematically gather and analyze information to help organizations make better decisions.
The document discusses best practices for visualizing analytics results. It emphasizes that visualization is critical for effectively communicating insights from data analysis. Good visualizations exploit the human visual system by presenting information simply and clearly. Practitioners should understand their data and audience to develop visualizations that tell the right story. Iterative experimentation is important to arrive at visualizations that provide global understanding from the data. Overall the document stresses that visualization is a key part of deriving meaningful insights from analytics work.
You need to demonstrate past successes with clients to increase credibility. Get this template @ http://www.demandmetric.com/content/case-study-template
The document describes the key ingredients of quantitative process performance models used in CMMI, including that they statistically or probabilistically predict interim and final project outcomes based on controllable factors tied to sub-processes, model the variation of those factors to understand the predicted range of outcomes, and enable "what-if" analysis and mid-course corrections to help ensure project success.
Jakub Chour, Mobile Marketing Manager at AppAgent, presented at App Promotion Summit 2017 in London how he decreased churn of Liftago (an Uber competitor in Central Europe) users by 20% using a logistic regression-backed campaign.
Learn how you can apply churn models to your mobile application and save the most valuable users.
The document discusses decision analysis and outlines the steps involved in making good decisions, including clearly defining the problem, listing alternatives and outcomes, evaluating alternatives using decision models, and selecting the best alternative. It provides an example of a lumber company evaluating whether to expand its product line by manufacturing backyard storage sheds, walking through the steps of defining the problem, listing alternatives, assessing potential profits in favorable and unfavorable market conditions, and selecting the optimal alternative using a decision table.
This document discusses strategic thinking and developing action plans. It provides guidance on analyzing employee engagement survey results to select priority issues, ensuring actions are linked to business strategy and measurable. An example tracking spreadsheet is outlined for monitoring progress of actions. The document also discusses treating customers fairly principles and analyzing the internal and external environment using tools like PEST, Porter's five forces, value chain analysis and the intelligence cycle to inform strategic planning. Famous strategic thinkers like Steve Jobs and Oprah Winfrey are mentioned.
A focused practice aimed at using simulations from simple System Dynamics models to help us better understand the intended and unintended consequences of our actions.
This document discusses challenges with software estimation and provides strategies for improving estimates. It notes that estimation is necessary but often inaccurate, with typical cost overruns of 30-40%. Expert estimation is most common but formal methods don't consistently improve accuracy. Prediction intervals from students tend to be better than experts who may feel pressure to appear more skilled. Common causes of overruns include optimistic plans and frequent changes. The document suggests using complexity measures and conversion factors to generate more realistic estimates.
The document discusses Social Six Sigma, which adapts the DMAIC (Define, Measure, Analyze, Improve, Control) methodology used in manufacturing process improvement to social media strategies. It defines each phase for social media as follows:
Define means listening to understand customer wants from both direct and indirect feedback on social media. Measure means engaging customers to clarify and expand on their feedback to obtain complete data for analysis. Analyze means analyzing what is important both to the organization and customers based on the collected data. Improve means addressing customer concerns and using reaction data to refine social media strategies. Control means making changes to social strategies based on new data and monitoring to ensure the right adjustments were made.
The document discusses principles for effective performance management goals. It recommends that goals should be connected to overall objectives, actionable to allow controlling outcomes, predictive of big-picture metrics, and set up for sustained results over time. It provides guidance on cascading goals from overall objectives and defining metrics that move from low-level and actionable to predictive of higher goals. Tools like Tashiro charts, flag charts, and balanced scorecards can help sustain momentum and results. Even inherently non-quantitative efforts can track leading indicators to monitor progress.
In developing research for impact, science should support decisions as decision makers are always hungry for information.
Research should be tailored to specifically address particular decisions. This can be achived through direct engagement with decison makers especially in decision making under uncertainity.
Tailor research specifically to address particular decisions
1. A/B testing involves splitting users into a test group that sees a new feature and a control group that sees the original version. Users are typically assigned randomly with equal percentages to each group.
2. Metrics are tracked for both groups to measure the impact of the new feature, such as additions to cart or purchases. Significance testing determines if results are likely real or due to chance.
3. Long-term experiments require holding back some users from the test to measure novelty or learning curve effects over time.
This document outlines the problem solving process, which includes gathering and analyzing data, developing alternative solutions, evaluating options, implementing the chosen solution, monitoring and managing the solution, verifying the solution, using adaptive techniques when appropriate, and developing ethical solutions. Guidelines are provided for each step of the process.
The document discusses structured problem solving techniques including situational awareness, process mapping, identifying customer requirements, problem identification, root cause analysis, implementing changes, and control methods. It provides examples of tools that can be used at each step such as affinity diagrams, histograms, scatter plots, 5 whys, tree diagrams, and benchmarks.
Serve your customers better with User Experience ResearchAmanda Stockwell
This document discusses user experience (UX) research and marketing research. It defines UX research as understanding users and the context in which they use products in order to uncover opportunities and understand why things happen from the user's perspective. Marketing research is defined as understanding purchasers and the context of purchase in order to uncover market opportunities and understand what is happening from the company's perspective. The document then outlines different types of research methods that can be used for UX and marketing research like interviews, usability testing, surveys, and analytics reviews. It provides guidance on choosing methods based on the product stage and type of questions being asked.
The document describes an intelligent decision system that can be used to support business innovation assessments. The system was developed as part of an EU-funded project to create a model for assessing innovation capabilities in businesses. It uses an evidential reasoning approach to model uncertainties and aggregate information from self-assessments. The system provides various outputs like rankings, strengths/weaknesses identification, and sensitivity analysis to help with assessment consistency, effectiveness and clear communication. It has applications in areas like business excellence, risk, supplier and customer satisfaction assessments.
1) How to design powerful experiments provides tips for setting up successful A/B tests and experiments to make data-driven decisions and reduce risks.
2) Key tips include having the proper experimentation infrastructure in place, following an iterative process of developing hypotheses, designing experiments, analyzing results, and executing.
3) Case studies show that A/B tests at Google and REA Group led to increased annual revenue and conversion rates, demonstrating the value of experimentation.
This document discusses ways to improve a company's analytic maturity. It begins by introducing the author and their background in analytics. It then discusses viewing analytics from a company perspective using an "Analytic Diamond" model. It introduces an Analytic Maturity Model consisting of 5 levels from basic reporting to strategy-driven analytics. Key differences between the levels are described. The document provides two case studies and discusses goals of analytic governance. It concludes by summarizing five ways to get started improving maturity and seven key process areas to focus on.
The document outlines the 5 main steps of a research process: 1) Locating and defining the issue or problem, 2) Designing the research project to solve the problem, 3) Collecting necessary data, 4) Interpreting the data to draw conclusions, 5) Reporting findings and conclusions to relevant parties through written reports and oral presentations using visual aids.
This document discusses strategies for integrating segmentation and predictive modeling. It begins by outlining a typical agenda, including whether to use segmentation, modeling, or both. It then covers strategic approaches like value-based behavioral segmentation and clustering to define customer segments. Tactical segmentation involves using outcomes from predictive models to segment customers. The document provides examples of integrating segmentation with different modeling techniques and discusses how segmented models can outperform single models. It emphasizes that both strategic and tactical approaches are useful but strategic provides more insights for improving communications.
The document outlines the key steps in the marketing research process, including defining the problem, developing an approach, formulating a research design, data collection and analysis, and reporting. It discusses defining the management decision problem versus the marketing research problem, and exploring different types of research design such as exploratory, descriptive, and causal research. The marketing research process aims to systematically gather and analyze information to help organizations make better decisions.
The document discusses best practices for visualizing analytics results. It emphasizes that visualization is critical for effectively communicating insights from data analysis. Good visualizations exploit the human visual system by presenting information simply and clearly. Practitioners should understand their data and audience to develop visualizations that tell the right story. Iterative experimentation is important to arrive at visualizations that provide global understanding from the data. Overall the document stresses that visualization is a key part of deriving meaningful insights from analytics work.
You need to demonstrate past successes with clients to increase credibility. Get this template @ http://www.demandmetric.com/content/case-study-template
The document describes the key ingredients of quantitative process performance models used in CMMI, including that they statistically or probabilistically predict interim and final project outcomes based on controllable factors tied to sub-processes, model the variation of those factors to understand the predicted range of outcomes, and enable "what-if" analysis and mid-course corrections to help ensure project success.
Jakub Chour, Mobile Marketing Manager at AppAgent, presented at App Promotion Summit 2017 in London how he decreased churn of Liftago (an Uber competitor in Central Europe) users by 20% using a logistic regression-backed campaign.
Learn how you can apply churn models to your mobile application and save the most valuable users.
The document discusses decision analysis and outlines the steps involved in making good decisions, including clearly defining the problem, listing alternatives and outcomes, evaluating alternatives using decision models, and selecting the best alternative. It provides an example of a lumber company evaluating whether to expand its product line by manufacturing backyard storage sheds, walking through the steps of defining the problem, listing alternatives, assessing potential profits in favorable and unfavorable market conditions, and selecting the optimal alternative using a decision table.
This document discusses strategic thinking and developing action plans. It provides guidance on analyzing employee engagement survey results to select priority issues, ensuring actions are linked to business strategy and measurable. An example tracking spreadsheet is outlined for monitoring progress of actions. The document also discusses treating customers fairly principles and analyzing the internal and external environment using tools like PEST, Porter's five forces, value chain analysis and the intelligence cycle to inform strategic planning. Famous strategic thinkers like Steve Jobs and Oprah Winfrey are mentioned.
The document discusses concepts related to decision-making and planning in management. It covers topics like benefits and pitfalls of planning, steps to make an effective plan, setting goals, developing commitment to goals, developing action plans, tracking progress, maintaining flexibility in planning, and planning at different management levels from strategic to operational. It also discusses rational decision-making processes, limits to rationality, and techniques for group decision-making.
Media Boot Camp Power Session — Marketing Effectivenessadtech_fan
The document discusses improving marketing accountability and effectiveness to impact business performance. It outlines six common reasons why marketing accountability efforts fail, including not measuring what matters and not focusing on decision making. The document recommends classifying existing marketing investments based on effectiveness, increasing spending on proven effective investments, and using experimentation to validate unproven investments. It also stresses the need for capabilities, processes, analytics, and data to systematically improve marketing accountability over time.
This document outlines the 8 step process of decision making: 1) Identify the problem, 2) Gather information and identify decision criteria, 3) Assign weights to criteria, 4) Develop alternatives, 5) Analyze alternatives, 6) Select the best alternative, 7) Implement the decision, and 8) Evaluate the results. Each step of the process is then described in more detail. The conclusion emphasizes that effective decision making involves weighing positives and negatives and favoring outcomes that avoid losses and support sustained organizational growth.
The document discusses managerial decision making and different models for decision making processes. It describes programmed and non-programmed decisions and outlines the classical, administrative, and incremental models. It provides a 7 step process for decision making: 1) identifying opportunities and problems, 2) setting objectives, 3) generating alternatives, 4) evaluating alternatives, 5) reaching a decision, 6) implementation strategies, and 7) monitoring and evaluating. Key aspects of evaluating alternatives and effective implementation are also covered.
This document outlines the process of decision making in a business organization. It discusses that decision making involves selecting between alternative courses of action to solve problems. The 9 step decision making process is then described, including identifying the problem, diagnosing it, establishing objectives, collecting information, generating alternatives, evaluating alternatives, selecting an alternative, implementing it, and monitoring the implementation. Programmed decisions that are routine and nonprogrammed decisions that are unique are also defined.
The marketing research process involves 9 steps: 1) Formulating the problem, 2) Choosing a method of inquiry (experimental or non-experimental), 3) Selecting a research method, 4) Designing the research, 5) Collecting data through techniques like interviews and observation, 6) Designing the sample, 7) Analyzing and interpreting the data, 8) Collecting the data, and 9) Creating a marketing research report to communicate the results. The overall process helps address a marketing problem by gathering and analyzing relevant data to inform recommendations.
The document discusses various aspects of decision making in businesses. It defines strategic, tactical and operational decisions and provides examples. It also discusses tools that can help in decision making such as SWOT analysis, structured decision making models and use of IT. Constraints on decision making such as availability of resources and external factors are also highlighted.
There are 3 key points in the document:
1. Experimentation is important for marketers to test strategies, connect with consumers, and answer questions about the best ways to reach audiences. Regular experimentation can lead to improved ad performance and increased performance over time.
2. The document outlines some fundamental principles of experimentation including having a clear hypothesis, defining success metrics, assigning test and control groups, and taking action on results. It also describes some common experiment methodologies including incrementality, optimization, and causal impact analysis.
3. The document provides an overview of different experiment solutions offered by Google including those for video, search, display, and geo-based testing. It also gives examples of using different
VWO Webinar: How To Plan Your Optimisation RoadmapVWO
If your conversion optimization sprints are dependent on surprise wins, then here’s something you should know —”A surprise win might be buried deep in your A/B testing cycle; you might have to wait for weeks, maybe months to see that.”
The good news is that an experimentation roadmap can open up the possibility of seeing those wins a lot faster. This session will help you uncover ways to manage and prioritize testing ideas in a systematic manner and improve your chances of seeing wins faster with your optimization program.
This document provides an overview of strategic planning and decision-making processes for organizations. It discusses key concepts like levels of strategy, strategic planning versus operational planning, strategic analysis tools like SWOT and Porter's Five Forces, and classical versus behavioral decision-making theories. The document also outlines the typical stages in a strategic planning process including developing a vision/mission, assessment, setting objectives, crafting a strategy, implementation, and evaluation.
A Playbook for Diversity Analytics and Strategy DevelopmentWilliam Gaker
Diversity and Inclusion is an increasingly important topic for organizations; however, these initiatives are often misaligned across HR functions and not designed to address specific gaps.
This presentation makes the case that organizations should view the workforce as an integrated systems, use analytics to identify gaps in diversity, and create holistic strategies to address those gaps.
This document discusses decision making in business. It begins by stating that decisions are made consciously or subconsciously in daily life and must be made scientifically in business since they affect operations. It then defines decision making as selecting between alternative courses of action to solve a problem. The document outlines the key features and steps of the decision making process, which includes identifying the problem, diagnosing causes, setting objectives, gathering information, generating alternatives, evaluating options, selecting a choice, implementing it, and monitoring the results. It also describes different types of decisions as either programmed, routine decisions or non-programmed unique decisions.
Marketing research process, Process of Marketing Research & Marketing Researc...Ashutosh Dubey
for detail explanaition you can check my youtube channel www.youtube.com/c/SuccessG in this presentation i have tried to explain all the important Steps in Marketing Research Process, and their Description in easy language.
#SuccessG
Using a rational, logical decision making model will help solve most issues. The following model identifies seven steps in the decision making process.
The document discusses the ROI Methodology, a proven process for measuring the impact and ROI of projects and programs. It provides a 5-level evaluation framework that progresses from measuring reaction and learning to quantifying ROI. The methodology uses a 10-step process model and 12 standards to ensure evaluations are consistent, conservative, and credible. It has been widely implemented across various industries to conduct impact and ROI analysis in a balanced, results-oriented way.
This document discusses decision making under uncertainty. It provides definitions of decision making and outlines an 8 step decision making process. It also defines risk, certainty and uncertainty and how decisions are made under each condition. The document then provides a case study of the mobile network operator Zong, outlining its vision, mission, organizational structure and conducting a SWOT analysis. It concludes that decision making is important for organizations and managers often face risk and uncertainty. It recommends strategies to improve decision making under uncertainty.
This document discusses decision making under uncertainty. It provides definitions of decision making and outlines an 8 step decision making process. It also defines risk, certainty and uncertainty and how decisions are made under each condition. The document then provides a case study of the mobile network operator Zong, outlining its vision, mission, organizational structure and performing a SWOT analysis. It concludes that decision making is important for organizations and managers often face risk and uncertainty. It recommends strategies to improve decision making under uncertainty.
Similar to Executive Briefing: Introduction to Strategic Experimentation (20)
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
The Tata Group, a titan of Indian industry, is making waves with its advanced talks with Taiwanese chipmakers Powerchip Semiconductor Manufacturing Corporation (PSMC) and UMC Group. The goal? Establishing a cutting-edge semiconductor fabrication unit (fab) in Dholera, Gujarat. This isn’t just any project; it’s a potential game changer for India’s chipmaking aspirations and a boon for investors seeking promising residential projects in dholera sir.
Visit : https://www.avirahi.com/blog/tata-group-dials-taiwan-for-its-chipmaking-ambition-in-gujarats-dholera/
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
Discover timeless style with the 2022 Vintage Roman Numerals Men's Ring. Crafted from premium stainless steel, this 6mm wide ring embodies elegance and durability. Perfect as a gift, it seamlessly blends classic Roman numeral detailing with modern sophistication, making it an ideal accessory for any occasion.
https://rb.gy/usj1a2
How MJ Global Leads the Packaging Industry.pdfMJ Global
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
Top mailing list providers in the USA.pptxJeremyPeirce1
Discover the top mailing list providers in the USA, offering targeted lists, segmentation, and analytics to optimize your marketing campaigns and drive engagement.
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2. 2
Traditional approaches to strategic analysis
focus on finding the single best path forward
Traditional Strategic Analysis Process
Identify Potential
Courses of
Action
Gather Data
Related to Each
Prospective
Course of Action
Analyze the Data
Select a Specific
Course of Action
Process Goal:
Find the One Best Way
3. 3
But traditional approaches do not work well in
highly uncertain situations, or ones with lots of options
When uncertainty is high, it’s highly unlikely that
a strategic analysis process designed to find the
optimal solution will define the route that leads to
the best future state before any action is taken.
4. 4
Strategy development / execution that is based on Strategic
Experimentation works much better in these situations
Provides a way to adapt
quickly and effectively to
rapidly changing conditions
Provides a way to explore
multiple options quickly,
cheaply and effectively
Provides a way to reduce or
eliminate key uncertainties
quickly and cheaply
Strategic Experimentation is a formal process for:
1. Identifying and prioritizing key risks and
uncertainties associated with a strategic initiative.
2. Applying lessons learned from inside and outside
the company to address as many aspects of the
risks and uncertainties as possible.
3. Designing and executing quick, inexpensive
experiments to address the remaining aspects.
4. Using the results of these experiments to inform
further action-planning and decision-making.
Strategic Experimentation is action-driven, not planning-driven
5. 5
Step 1: Identify, assess and plot all key decisions and risks
Key Decisions / Risks
MagnitudeofRisk
HighLow
Low High
Degree of Uncertainty
5
Uncertainty about which
option or approach is best
Strategic or financial risk if
you make the wrong choice
Focus efforts on
reducing or
eliminating biggest
risks / uncertainties
2
1
4
3
6. 6
Step 2: Analyze previous efforts to learn what
has and hasn’t worked in similar situations
ID
Similar Uncertainties or Risks…
We’ve Faced Others Have Faced
What Worked What Didn’t Work What Worked What Didn’t Work
1
2
4
Results of this analysis represent the collective
insights gained from leveraging your organization’s
experience and “learning on someone else’s dime”
7. 7
Step 3: Design / Execute quick, cheap experiments
to address remaining risks / uncertainties
Uncertainty: What is the best initial price for our widget?
Planning
Assumption:
$40
Experiment: Test prices in $5 increments from $30 to $60
Metric(s): % of click-throughs for purchase at each price
Results:
Test Price % of Click-Throughs
$30 16%
$35 20%
$40 19%
$45 21%
$50 19%
$55 13%
$60 8%
This is a specific
example, rather than a
generic example (as
used Steps 1-2), to
clarify the approach.
Experiments should be
devised and run to
address all key risks
and uncertainties that
remain after the
analyses in Step 2.
8. 8
Step 4: Use results of experiments to inform decision-making,
and incorporate insights into revised action plans
Results:
Test Price % of Click-Throughs
$30 16%
$35 20%
$40 19%
$45 21%
$50 19%
$55 13%
$60 8%
Result: Responses are similar
(within margin of error)
at several price points.
Decision: Use highest price in
the range.
Action: Set price at $50 rather
than $40, and adjust
revenue forecasts.
9. 9
Information services provider used strategic experimentation
to support launch of successful new business unit
Situation: Information services provider wants to launch a new information products business.
Key
Uncertainty:
What kinds of information products would appeal to existing customer base?
Application:
Company rapidly developed and market-tested low-fidelity prototypes of 15 potential products.
Discovered five that had broad appeal.
Conducted additional experiments to define initial set of product designs and product features.
Designed / staffed new unit to support development, sale, installation / operation and support
of initial product set.
Result: Rapid adoption by existing customer base. Exceeded Year 1 revenue target by 40%.
10. 10
A Strategic Experimentation-based approach to
strategy development / execution has several benefits
Using Strategic Experimentation in your organization will:
• Enable you to deal more effectively with risk and uncertainty
• Increase execution speed and the quality of outcomes for key strategic initiatives
• Create a more action-oriented culture
• Increase organizational agility and adaptability
• Increase organizational performance
11. Metre22, LLC
3001 Knox Street • Suite 204
Dallas, Texas 75205
Mark Bills
mark.bills@metre22.com • 312.391.3272