This document discusses the importance of testing in digital marketing. It addresses common questions and challenges around testing, provides tips on getting started with testing, and outlines the key components of a testing process including research, prioritization, experimentation, and analysis. Testing is presented as an ongoing process of learning what performs best through changing variables and measuring results.
Relationships are complicated: how data analysis and UX research come togethe...UXinsight
This document discusses how data analysis and research come together at Zendesk. It provides three ways that data can help: 1) model projected impact, 2) determine severity of problems, and 3) recruit the right users for research. The document also includes quotes from a customer feedback interview about using analytics to identify which products are causing a disproportionate number of support tickets.
Be more certain - a practical approach to scaling a research practiceUXinsight
This document outlines a practical approach to user research. It recommends assuming data already exists and working backwards to find it, employing diplomacy to connect different parts of an organization, and recognizing that some data is better than no data. Small samples can provide large opportunities if they generate information-rich stories rather than just numbers of participants. Research accumulates over time like compound interest to help organizations be more certain in their decisions.
This document summarizes common analytic mistakes made in business intelligence projects. It discusses mistakes such as not asking the right questions, focusing on past metrics rather than future needs, misunderstanding metrics and their methodology, bottlenecking the value of analytics to the organization, overvaluing data visualization, compromising data through consensus, confusing insight with the ability to take action, and more. The document provides examples and recommendations to avoid these common mistakes in analytics projects.
Setting up Data Science for Success: The Data LayerCarl Anderson
This document discusses setting up data science projects for success by focusing on the importance of data preparation. It notes that 76% of data scientists view data preparation as the least enjoyable part of their work. The document outlines various facets of data preparation, including collecting, understanding, cleaning, and reshaping data. It emphasizes that data quality is important and a shared responsibility across data engineering, data science, and business intelligence teams. It recommends creating a single source of truth for data through techniques like data dictionaries to define data for all teams.
- The document discusses using customer journey analytics to better understand the customer experience. It recommends creating a customer journey map, validating it with metrics and analytics, and getting customer feedback. Predictive analytics can be used to find causes of behaviors and key message points. Tracking business metrics alongside the customer perspective is also important. Overall, linking the customer journey to analytics provides strategic and tactical benefits for businesses.
The document discusses using the Net Promoter Score (NPS) to gather meaningful post-event feedback. NPS asks attendees to rate their likelihood to recommend on a 0-10 scale. Scores of 0-6 are "Detractors", 7-8 are "Neutrals", and 9-10 are "Promoters". The percentage of Promoters minus Detractors gives the NPS. A positive NPS indicates good customer service and profits. The document recommends asking the NPS question a week after an event to better capture impressions. Identifying Promoters can help refine processes while Detractor feedback spots issues to address.
This document discusses the importance of testing in digital marketing. It addresses common questions and challenges around testing, provides tips on getting started with testing, and outlines the key components of a testing process including research, prioritization, experimentation, and analysis. Testing is presented as an ongoing process of learning what performs best through changing variables and measuring results.
Relationships are complicated: how data analysis and UX research come togethe...UXinsight
This document discusses how data analysis and research come together at Zendesk. It provides three ways that data can help: 1) model projected impact, 2) determine severity of problems, and 3) recruit the right users for research. The document also includes quotes from a customer feedback interview about using analytics to identify which products are causing a disproportionate number of support tickets.
Be more certain - a practical approach to scaling a research practiceUXinsight
This document outlines a practical approach to user research. It recommends assuming data already exists and working backwards to find it, employing diplomacy to connect different parts of an organization, and recognizing that some data is better than no data. Small samples can provide large opportunities if they generate information-rich stories rather than just numbers of participants. Research accumulates over time like compound interest to help organizations be more certain in their decisions.
This document summarizes common analytic mistakes made in business intelligence projects. It discusses mistakes such as not asking the right questions, focusing on past metrics rather than future needs, misunderstanding metrics and their methodology, bottlenecking the value of analytics to the organization, overvaluing data visualization, compromising data through consensus, confusing insight with the ability to take action, and more. The document provides examples and recommendations to avoid these common mistakes in analytics projects.
Setting up Data Science for Success: The Data LayerCarl Anderson
This document discusses setting up data science projects for success by focusing on the importance of data preparation. It notes that 76% of data scientists view data preparation as the least enjoyable part of their work. The document outlines various facets of data preparation, including collecting, understanding, cleaning, and reshaping data. It emphasizes that data quality is important and a shared responsibility across data engineering, data science, and business intelligence teams. It recommends creating a single source of truth for data through techniques like data dictionaries to define data for all teams.
- The document discusses using customer journey analytics to better understand the customer experience. It recommends creating a customer journey map, validating it with metrics and analytics, and getting customer feedback. Predictive analytics can be used to find causes of behaviors and key message points. Tracking business metrics alongside the customer perspective is also important. Overall, linking the customer journey to analytics provides strategic and tactical benefits for businesses.
The document discusses using the Net Promoter Score (NPS) to gather meaningful post-event feedback. NPS asks attendees to rate their likelihood to recommend on a 0-10 scale. Scores of 0-6 are "Detractors", 7-8 are "Neutrals", and 9-10 are "Promoters". The percentage of Promoters minus Detractors gives the NPS. A positive NPS indicates good customer service and profits. The document recommends asking the NPS question a week after an event to better capture impressions. Identifying Promoters can help refine processes while Detractor feedback spots issues to address.
This document summarizes key points from an article on predictive analytics and provides recommendations for managers. It advises managers to appoint an analytics team focused on predictive analytics to foresee future growth. It also suggests important questions managers should ask analysts regarding their data and assumptions to better understand results and make decisions. The document stresses the importance of predictive analysis for managers and companies.
Predictive Data Analytics to Help Your CustomersExperian_US
The @ExperianDataLab hosts a #DataTalk on Thursdays at 5 p.m. ET on Twitter. Join us.
This week, we talked about data preparation, model evaluation, testing effectiveness of predictive analytics, challenges, and trends in predictive analytics.
We learned from Michael Beygelman, Co-founder and CEO of Joberate and Berry Diepeveen, Partner and Enterprise Intelligence Leader at EY in South Africa, and Chuck Robida, Chief Scientist for Experian Decision Analytics.
Learn about past and upcoming chats at:
http://experian.com/datatalk
Running Effective Controlled Experiments (aka A/B/n Tests) - Data Science Pop...Domino Data Lab
Microsoft's Analysis and Experimentation team has enabled many different feature teams to run controlled experiments in order to make feature ship/no-ship decisions. In this talk, I will review several case studies of experiments run by teams at Microsoft. I will highlight both the value that running controlled experiments has provided these teams as well as the challenges encountered in order to get trustworthy results from the controlled experiments. Presented by Brian Frasca, Partner Data Scientist Manager at
Microsoft.
Three reasons why analytics projects can fail to achieve expected business value are:
1. Ill-defined opportunity - addressing the wrong problem without clear data, targets, predictors, or value metrics.
2. Human bias - biases from preconceptions, storytelling, or vested interests that skew results.
3. Methodological overreach - being overly aggressive in modeling without proper validation, such as with limited data or complex unpredictable problems. Pragmatic analytics requires clearly defining opportunities, mitigating bias, rigorous validation, and post-deployment tracking of real business value.
This document discusses lessons learned from failures in predictive modeling projects. It outlines three key lessons: 1) Align priorities by obtaining business sponsorship and understanding timelines, 2) Focus on outcomes over outputs by defining success upfront and addressing value, and 3) Co-author solutions by acknowledging change resistance, forming diverse teams, and frequent communication. Examples of failures that taught these lessons include building models without business need and failing to make insights actionable.
The document discusses how many companies fail to use analytics effectively in their digital decision making. It provides examples of how marketing budgets are decided without data, initiatives are selected based on gut feelings rather than metrics, and success is measured with incomplete or made-up KPIs. Departments also work in silos instead of taking an integrated customer-centric approach.
The key issues are that decisions are not driven by data, there is no testing or learning from past results, customer needs are not truly understood, and a continuous improvement approach is not taken. The document argues analytics must be integrated throughout the digital process, continuous experimentation used, and customer research central to optimize the customer experience.
Pdf analytics-and-witch-doctoring -why-executives-succumb-to-the-black-box-me...OrateTeam
This document discusses common issues ("pathologies") that organizations face when adopting analytics and data-driven approaches. It summarizes these pathologies in 3 sentences or less:
Organizations often treat analytics as a "black box" without understanding how it works due to the technical nature of analytics and lack of transparency in algorithms and methods. Many projects fail because organizations jump into analytics without properly preparing their data, validating results, or planning how insights will be implemented and drive business changes. To successfully adopt analytics, organizations must ask critical questions about data quality, intended use cases, and consequences of results in order to focus efforts and avoid wasting resources on initiatives that do not provide value.
Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly.Venveo
The document discusses agile analytics and its benefits for businesses. Agile analytics involves rapidly testing hypotheses, analyzing results, and making improvements to gain a better understanding of customers, get results more quickly, and reduce risks. It recommends businesses focus on a single problem, develop small testable hypotheses, iterate testing every 2-4 weeks with specific changes, and use findings to direct the next round of improvements. Practicing agile analytics allows organizations to test, improve, fail, and succeed quickly.
Fix Problems not Symptoms: A Scientific Approach to MarketingChris Gaffney
This document discusses solutions-based marketing, which applies the scientific method to marketing decisions. It outlines the steps of the scientific method - formulating a question, gathering information, making a hypothesis and predictions, testing predictions, analyzing results, drawing conclusions, and retesting. It then provides examples of how to apply this approach to solve real client problems and maximize marketing efficiency over time.
This document summarizes the agenda and Q&A session from a webinar hosted by Andy Kirk and Andy Cotgreave on data visualization. The agenda included discussing the principles and purpose of data visualization, visual techniques and chart types, personal development and skills, and the current state of the data visualization field. The webinar involved Kirk and Cotgreave answering questions from participants on topics like the process of making visualizations, effective visualization design, software for creating visualizations, and developing skills in data visualization.
Nilan will be talking through how finding its cause helped TransferWise build an evangelical brand and drive product driven growth.
TransferWise is the leader in international money transfer transferring over £0,5bn a month, doubling in size every 3 to 4 months mainly through Word of Mouth. Nilan will be talking through how defining its purpose helped TransferWise unlock “NPS driven growth”.
Data is worthless if you don;t communicateAbhi Rana
This document discusses the importance of data communication for managers. It notes that while data scientists analyze data, managers must communicate insights to key stakeholders to enable effective decision making. Managers can now make decisions based on data rather than intuition. The document provides examples showing that communicating research findings can lead to adoption of ideas or creation of new businesses. It outlines a framework for communicating data that involves defining a business problem, measuring relevant variables, collecting and analyzing available data, developing initial and refined solutions, and communicating the business impact. The overall message is that data is only valuable if insights are effectively communicated.
Radical Analytics, Web à Québec, Mars 2017 (français)Stéphane Hamel
« Marketing strategy », « Big Data », « digital analytics », Internet des Objets… en voulez-vous du data, en v’là! En principe, l’analytique devrait permettre aux gestionnaires marketing et autres de prendre des décisions plus éclairées. Pourtant, après des années à définir des objectifs et des indicateurs de performance, à mesurer, à produire des rapports et des tableaux de bord, la majorité des entreprises tirent le diable par la queue. Peut-être est-ce le temps de changer notre façon de faire?
Info complémentaires: https://bit.ly/radicalanalytics
Data informed design - UX Australia august 2015 Alastair Simpson
Alastair Simpson discusses using data-informed design to improve products' time to value (TTV). He explains how Atlassian analyzed usage data showing new users spent little time with their products. Combining this data with qualitative customer insights, they identified engagement and conversion as key metrics. Experiments informed by both data and empathy increased conversion by 22%, helping new users realize value faster. Data and testing help optimize, but clear decision-making remains important for product success.
The document discusses the mission of democratizing data at ShopStyle to empower employees to make daily data-driven decisions. Key points:
- The head of data and analytics' goal is to train employees and provide self-service data access and analytics tools.
- Metrics show over 60% of employees regularly use data for decisions, with a goal of 75% by end of year.
- Examples provided include marketing testing emails daily and product managers running continuous A/B tests.
Having data doesn't solve any business problem. Finding actionable insights and stories and implementing them to optimize business processes does.
This presentation was created by Sundeep Reddy Mallu for a virtual session with people at Indian School of Business (ISB) - Institute of Data Science.
The slides talk about how to create data stories and what parameters to keep in mind while creating one. With real-time case-studies and use cases of data storytelling, this presentation talks about how business leaders can identify Big, Useful, and surprising insights from big data sets.
VMCS14 REanalyze: What is your EVP Data Saying?VolunteerMatch
2014 VolunteerMatch Client Summit Best Practice Cafe
Demonstrating your employee volunteer program's impact internally and externally is critical to its success. While the industry as whole is still looking for ways to get beyond traditional metrics, some companies are taking it upon themselves to identify outcomes that reflect their priorities. They are also looking for new ways to quantify the engagement and impact of their employees so that they can better tell their stories to leadership, employees, nonprofit partners, and the community.
Join Jake Sanches, internal metrics and analytics guru at Palantir Technologies, to discuss VolunteerMatch's recent metrics benchmarking project. We'll review our findings and key takeaways, cover industry trends across key metric benchmarks, and discuss metrics analysis in finer detail and how it can be leveraged to drive improved programmatic and reporting approaches. Jake will also provide recommendations and demonstrate examples of ways to increase your use and presentation of data in your communications.
Hiring for Data Scientists - Data Science Pop-up SeattleDomino Data Lab
Tales from the other side.
What you might be missing if you don't know what you are looking for. Presented by Amanda Casari, Senior Data Scientist at Concur.
7 Steps to Put Your Lead Nurturing on SteroidsCraig Rosenberg
The document outlines 7 steps to improve lead nurturing programs: 1) Clean marketing and sales data, 2) Complete infrastructure like filling data gaps, 3) Optimize landing pages to capture more leads, 4) Refine buyer personas, 5) Create a lead scoring system, 6) Implement a nurturing program based on personas, and 7) Continuously iterate and refresh content. It emphasizes the importance of data quality, personalizing content for different buyer types, and measuring results to further improve performance.
Growth Hacking, Growth Marketing, Technical Marketing... whatever you want to call it. I presented this deck at 2016's Digital Elite Camp in Tallinn, Estonia. In my talk, I covered the definition of growth hacking, or technical marketing (if you're sick of hearing that word), went through all the attributes that technical marketing is compiled of and shared some low hanging fruit so that your company or startup reaches growth as quickly as possible.
Marketers should be held to the same level of quality as developers. That's why we must teach more technical growth skills.
Check out growthtribe.io for crash courses, no-bullshit workshops and a free email course.
ETE 2013: Going Big with Big Data...one step at a timeAnita Andrews
This document provides an overview of using data analytics to optimize business performance. It begins with introductions and defines "big data." It then discusses how most companies are not truly leveraging big data and challenges they face. The document recommends doing an initial assessment of goals, team capabilities, data sources, and tools. It suggests starting with a small, focused data set to quickly test the analytics process and prove value. Finally, it outlines two approaches to optimization: funnel optimization and "Russian doll" optimization, where differentiating characteristics of high-performing users or items are used to improve lower-performing ones in an iterative process. The key messages are doing analytics incrementally, interpreting data intelligently, and using insights to optimize measurable
Start With Why: Build Product Progress with a Strong Data CultureAggregage
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
This document summarizes key points from an article on predictive analytics and provides recommendations for managers. It advises managers to appoint an analytics team focused on predictive analytics to foresee future growth. It also suggests important questions managers should ask analysts regarding their data and assumptions to better understand results and make decisions. The document stresses the importance of predictive analysis for managers and companies.
Predictive Data Analytics to Help Your CustomersExperian_US
The @ExperianDataLab hosts a #DataTalk on Thursdays at 5 p.m. ET on Twitter. Join us.
This week, we talked about data preparation, model evaluation, testing effectiveness of predictive analytics, challenges, and trends in predictive analytics.
We learned from Michael Beygelman, Co-founder and CEO of Joberate and Berry Diepeveen, Partner and Enterprise Intelligence Leader at EY in South Africa, and Chuck Robida, Chief Scientist for Experian Decision Analytics.
Learn about past and upcoming chats at:
http://experian.com/datatalk
Running Effective Controlled Experiments (aka A/B/n Tests) - Data Science Pop...Domino Data Lab
Microsoft's Analysis and Experimentation team has enabled many different feature teams to run controlled experiments in order to make feature ship/no-ship decisions. In this talk, I will review several case studies of experiments run by teams at Microsoft. I will highlight both the value that running controlled experiments has provided these teams as well as the challenges encountered in order to get trustworthy results from the controlled experiments. Presented by Brian Frasca, Partner Data Scientist Manager at
Microsoft.
Three reasons why analytics projects can fail to achieve expected business value are:
1. Ill-defined opportunity - addressing the wrong problem without clear data, targets, predictors, or value metrics.
2. Human bias - biases from preconceptions, storytelling, or vested interests that skew results.
3. Methodological overreach - being overly aggressive in modeling without proper validation, such as with limited data or complex unpredictable problems. Pragmatic analytics requires clearly defining opportunities, mitigating bias, rigorous validation, and post-deployment tracking of real business value.
This document discusses lessons learned from failures in predictive modeling projects. It outlines three key lessons: 1) Align priorities by obtaining business sponsorship and understanding timelines, 2) Focus on outcomes over outputs by defining success upfront and addressing value, and 3) Co-author solutions by acknowledging change resistance, forming diverse teams, and frequent communication. Examples of failures that taught these lessons include building models without business need and failing to make insights actionable.
The document discusses how many companies fail to use analytics effectively in their digital decision making. It provides examples of how marketing budgets are decided without data, initiatives are selected based on gut feelings rather than metrics, and success is measured with incomplete or made-up KPIs. Departments also work in silos instead of taking an integrated customer-centric approach.
The key issues are that decisions are not driven by data, there is no testing or learning from past results, customer needs are not truly understood, and a continuous improvement approach is not taken. The document argues analytics must be integrated throughout the digital process, continuous experimentation used, and customer research central to optimize the customer experience.
Pdf analytics-and-witch-doctoring -why-executives-succumb-to-the-black-box-me...OrateTeam
This document discusses common issues ("pathologies") that organizations face when adopting analytics and data-driven approaches. It summarizes these pathologies in 3 sentences or less:
Organizations often treat analytics as a "black box" without understanding how it works due to the technical nature of analytics and lack of transparency in algorithms and methods. Many projects fail because organizations jump into analytics without properly preparing their data, validating results, or planning how insights will be implemented and drive business changes. To successfully adopt analytics, organizations must ask critical questions about data quality, intended use cases, and consequences of results in order to focus efforts and avoid wasting resources on initiatives that do not provide value.
Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly.Venveo
The document discusses agile analytics and its benefits for businesses. Agile analytics involves rapidly testing hypotheses, analyzing results, and making improvements to gain a better understanding of customers, get results more quickly, and reduce risks. It recommends businesses focus on a single problem, develop small testable hypotheses, iterate testing every 2-4 weeks with specific changes, and use findings to direct the next round of improvements. Practicing agile analytics allows organizations to test, improve, fail, and succeed quickly.
Fix Problems not Symptoms: A Scientific Approach to MarketingChris Gaffney
This document discusses solutions-based marketing, which applies the scientific method to marketing decisions. It outlines the steps of the scientific method - formulating a question, gathering information, making a hypothesis and predictions, testing predictions, analyzing results, drawing conclusions, and retesting. It then provides examples of how to apply this approach to solve real client problems and maximize marketing efficiency over time.
This document summarizes the agenda and Q&A session from a webinar hosted by Andy Kirk and Andy Cotgreave on data visualization. The agenda included discussing the principles and purpose of data visualization, visual techniques and chart types, personal development and skills, and the current state of the data visualization field. The webinar involved Kirk and Cotgreave answering questions from participants on topics like the process of making visualizations, effective visualization design, software for creating visualizations, and developing skills in data visualization.
Nilan will be talking through how finding its cause helped TransferWise build an evangelical brand and drive product driven growth.
TransferWise is the leader in international money transfer transferring over £0,5bn a month, doubling in size every 3 to 4 months mainly through Word of Mouth. Nilan will be talking through how defining its purpose helped TransferWise unlock “NPS driven growth”.
Data is worthless if you don;t communicateAbhi Rana
This document discusses the importance of data communication for managers. It notes that while data scientists analyze data, managers must communicate insights to key stakeholders to enable effective decision making. Managers can now make decisions based on data rather than intuition. The document provides examples showing that communicating research findings can lead to adoption of ideas or creation of new businesses. It outlines a framework for communicating data that involves defining a business problem, measuring relevant variables, collecting and analyzing available data, developing initial and refined solutions, and communicating the business impact. The overall message is that data is only valuable if insights are effectively communicated.
Radical Analytics, Web à Québec, Mars 2017 (français)Stéphane Hamel
« Marketing strategy », « Big Data », « digital analytics », Internet des Objets… en voulez-vous du data, en v’là! En principe, l’analytique devrait permettre aux gestionnaires marketing et autres de prendre des décisions plus éclairées. Pourtant, après des années à définir des objectifs et des indicateurs de performance, à mesurer, à produire des rapports et des tableaux de bord, la majorité des entreprises tirent le diable par la queue. Peut-être est-ce le temps de changer notre façon de faire?
Info complémentaires: https://bit.ly/radicalanalytics
Data informed design - UX Australia august 2015 Alastair Simpson
Alastair Simpson discusses using data-informed design to improve products' time to value (TTV). He explains how Atlassian analyzed usage data showing new users spent little time with their products. Combining this data with qualitative customer insights, they identified engagement and conversion as key metrics. Experiments informed by both data and empathy increased conversion by 22%, helping new users realize value faster. Data and testing help optimize, but clear decision-making remains important for product success.
The document discusses the mission of democratizing data at ShopStyle to empower employees to make daily data-driven decisions. Key points:
- The head of data and analytics' goal is to train employees and provide self-service data access and analytics tools.
- Metrics show over 60% of employees regularly use data for decisions, with a goal of 75% by end of year.
- Examples provided include marketing testing emails daily and product managers running continuous A/B tests.
Having data doesn't solve any business problem. Finding actionable insights and stories and implementing them to optimize business processes does.
This presentation was created by Sundeep Reddy Mallu for a virtual session with people at Indian School of Business (ISB) - Institute of Data Science.
The slides talk about how to create data stories and what parameters to keep in mind while creating one. With real-time case-studies and use cases of data storytelling, this presentation talks about how business leaders can identify Big, Useful, and surprising insights from big data sets.
VMCS14 REanalyze: What is your EVP Data Saying?VolunteerMatch
2014 VolunteerMatch Client Summit Best Practice Cafe
Demonstrating your employee volunteer program's impact internally and externally is critical to its success. While the industry as whole is still looking for ways to get beyond traditional metrics, some companies are taking it upon themselves to identify outcomes that reflect their priorities. They are also looking for new ways to quantify the engagement and impact of their employees so that they can better tell their stories to leadership, employees, nonprofit partners, and the community.
Join Jake Sanches, internal metrics and analytics guru at Palantir Technologies, to discuss VolunteerMatch's recent metrics benchmarking project. We'll review our findings and key takeaways, cover industry trends across key metric benchmarks, and discuss metrics analysis in finer detail and how it can be leveraged to drive improved programmatic and reporting approaches. Jake will also provide recommendations and demonstrate examples of ways to increase your use and presentation of data in your communications.
Hiring for Data Scientists - Data Science Pop-up SeattleDomino Data Lab
Tales from the other side.
What you might be missing if you don't know what you are looking for. Presented by Amanda Casari, Senior Data Scientist at Concur.
7 Steps to Put Your Lead Nurturing on SteroidsCraig Rosenberg
The document outlines 7 steps to improve lead nurturing programs: 1) Clean marketing and sales data, 2) Complete infrastructure like filling data gaps, 3) Optimize landing pages to capture more leads, 4) Refine buyer personas, 5) Create a lead scoring system, 6) Implement a nurturing program based on personas, and 7) Continuously iterate and refresh content. It emphasizes the importance of data quality, personalizing content for different buyer types, and measuring results to further improve performance.
Growth Hacking, Growth Marketing, Technical Marketing... whatever you want to call it. I presented this deck at 2016's Digital Elite Camp in Tallinn, Estonia. In my talk, I covered the definition of growth hacking, or technical marketing (if you're sick of hearing that word), went through all the attributes that technical marketing is compiled of and shared some low hanging fruit so that your company or startup reaches growth as quickly as possible.
Marketers should be held to the same level of quality as developers. That's why we must teach more technical growth skills.
Check out growthtribe.io for crash courses, no-bullshit workshops and a free email course.
ETE 2013: Going Big with Big Data...one step at a timeAnita Andrews
This document provides an overview of using data analytics to optimize business performance. It begins with introductions and defines "big data." It then discusses how most companies are not truly leveraging big data and challenges they face. The document recommends doing an initial assessment of goals, team capabilities, data sources, and tools. It suggests starting with a small, focused data set to quickly test the analytics process and prove value. Finally, it outlines two approaches to optimization: funnel optimization and "Russian doll" optimization, where differentiating characteristics of high-performing users or items are used to improve lower-performing ones in an iterative process. The key messages are doing analytics incrementally, interpreting data intelligently, and using insights to optimize measurable
Start With Why: Build Product Progress with a Strong Data CultureAggregage
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
Start With Why: Build Product Progress with a Strong Data CultureBrittanyShear
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
As part of your fundraising campaigns and online engagement, you likely collect many metrics and data points. But do you take the time to reflect on this data and use it to improve for next time? In this session, we’ll discuss metrics you can collect, share each other’s best practices for data collection processes, and demo dashboard tools that will help you see the big picture.
Training Taster: Leading the way to become a data-driven organizationGoDataDriven
The document discusses becoming a data-driven organization. It provides an overview of the value chain of data science and an analytics maturity journey. The value chain of data science shows how data can be measured, optimized, used to generate predictions and insights, and ultimately create value. It emphasizes starting with the desired value and working backwards to the necessary data. The analytics maturity journey outlines four phases - initialization, continuous experimentation, enterprise empowerment, and data democratization - with different focuses at each stage to build analytical capabilities and business adoption of data and analytics. Key roles in a minimal viable data science team are also outlined.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
How to Measure What Matters:
What is a KPI and what makes a good one?
Who should be involved in data driven decision making in your business?
What tools do you need to start being data-driven?
What should you measure?
Next Steps & Best Practices
The Elusive Data-Driven Marketing Unicorn - Using Google Analytics to Stand O...Alexander Abell
This document provides an overview of using Google Analytics to stand out as a data-driven marketer. It discusses the difference between marketing and analytics and how the roles are converging. It promotes developing skills in both areas to become a "digital marketing unicorn" that can understand data insights and drive the right actions. The document also highlights resources for learning Google Analytics, such as its free individual certification and sandbox for hands-on practice.
This document provides an overview of predictive analytics for non-programmers. It defines predictive analytics as extracting data from existing datasets to identify trends and patterns which are then used to predict future outcomes. The document discusses why predictive analytics is useful for improving decision making and gaining a competitive advantage. It also outlines common predictive analytic models like classification, clustering, time series forecasting and associative rule mining. Finally, the document lists some popular tools for predictive analytics and concludes with a brief demo.
The document provides an outline for a training on fundamentals of data analytics. It introduces the presenter, Daniel Meyer, who has over 20 years of experience in higher education, business process outsourcing, and financial services. The agenda covers topics such as descriptive, predictive, and prescriptive analytics, finding and using data, and driving decisions with data analytics. It also discusses challenges around big data and unstructured data, and the importance of business intelligence, data visualization, and data-driven decision making.
This document discusses how startups can leverage big and small data to improve their business. It recommends that startups collect usage, purchase, and interaction data to gain insights. The same data can provide different views to optimize aspects like aesthetics, fraud detection, marketing, and pricing. Startups should experiment by conducting A/B tests and act on what the data shows rather than vanity metrics. More sophisticated statistical and machine learning algorithms can help understand metrics when experimenting is not possible, but correlation does not imply causation. Automating business reactions to real-time data is ideal, or reacting to appropriate data manually using visual summaries.
Optimizely building your_data_dna_e_booktthhciciedeng
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2. ¡ A
bit
about
me
¡ The
“Big
Data”
myth
¡ What
it
takes
to
leverage
data
in
your
biz
¡ An
approach
to
using
analytics
in
your
biz
¡ QUESTIONS
3. ¡ General
Manager,
Analytics
&
Optimization
§ Founded
Sepiida,
an
A&O
consultancy
in
2009
with
clients
including
Zynga
and
Haymarket
Media
–
sold
to
Delphic
in
2012
§ Previously,
VP
E-‐commerce
at
Nutrisystem
§ Dir
of
Program
Management
at
Ingenio,
sold
to
AT&T
YellowPages.com
¡ MS
Computer
Science
–
Stanford
University
¡ BA
Politics
–
New
York
University
¡ Love
numbers.
Hate
endless
(and
needless)
discussions.
Constantly
iterating.
4.
5. Multibillion
dollar
companies
who
didn’t
look
at
their
Google
Analytics
until
this
year
Angel-‐funded
start-‐ups
who
are
tracking
everything
with
innovative
reporting
software
6. ¡ Size
of
company
has
little
correlation
to
size
of
dataset?
¡ Size
of
company
has
little
correlation
to
facility
with
data
and
analytics?
¡ Size
of
company
has
little
correlation
to
current
status
of
analytics
activities?
¡ Size
of
company
has
little
correlation
to
where
future
efforts
should
be
focused?
7. ¡ Large
company
bureaucracy
§ How
many
stifled
data
geeks
do
you
have?
§ How
much
lost
revenue?
§ Lots
of
boxes
checked.
But
how
many
smarter,
more
efficient
decisions?
¡ Data
mania
§ Don’t
lose
sight
of
the
forest
for
the
trees
§ How
does
all
the
data
actually
connect
to
the
steps
needed
for
growth?
§ More
data
doesn’t
mean
more
revenue
8. ¡ Using
data
to
create
à
Creative
Marketing
§ Big
new
opportunities
▪ Loyalty
program
creation,
Geo-‐targeting,
etc.
§ What
data
to
look
at
is
often
unknown
¡ Using
data
to
optimize
à
A&O
§ Often,
the
metric
that
is
suffering
is
known
§ The
data
subset
is
typically
easier
to
identify
9. ¡ Goals
¡ Team
capabilities
¡ Sources
of
data
¡ Tools
for
reporting
¡ Opportunities
10. ¡ What
specific
metrics
or
KPIs
do
you
want
to
improve?
¡ What
are
the
formulas
for
these?
§ Need
consistent
definitions!
¡ What
will
move
your
Analytics
practice
forward?
§ Think
of
A&O
as
sales
and
evangelization
§ If
you
do
it
right,
you’re
the
source
of
improvement
for
other
parts
of
the
business
11. Bet
you
have
LOTS
of
data
§ Web
traffic
data
§ Transactional
databases
§ Internal
toolsets
(often
different
DBs)
§ Third
party
(email,
CRM,
etc.)
Key
questions
1. How
accurate
are
each
of
these?
2. How
much
of
what
you
need
are
you
actually
tracking?
3. Which
of
these
has
the
answers
to
your
goals?
12. ¡ Fight
the
impulse
to
“track
everything”
§ Technically
painful
§ Painful
for
business
people
§ You
don’t
need
it
to
drive
your
business
forward
§ There
is
no
glory
in
having
lots
of
data.
Size
does
NOT
matter
here…
13. ¡ Collecting
Data
&
Reporting
§ GA
vs.
the
rest
(KISSMetrics,
MixPanel,
Omniture)
§ GoodData,
Domo,
RJ
Metrics,
WebTrends
§ Excel!
¡ There
are
no
good
analysis
or
analytics
tools.
Yea,
I
said
it.
Stop
looking
for
them.
It’s
about
people
and
practices.
14. ¡ What
should
you
do
NOW?
People
Low
KPIs
Tools
Good
Data
IDENTIFY
THIS
15. ¡ It
may
not
target
the
largest
pool
¡ It
may
not
even
be
web-‐based
¡ It
may
not
be
obvious
¡ It
may
FAIL
¡ Goal
is
to
experiment
with
process,
prove
value
and
get
data-‐driven
results
quickly
¡ Data
driven
culture
will
come
from
doing
data
driven
things
16. ¡ Have
perspective
about
the
process
¡ It’s
all
iterative.
It’s
not
sexy,
but
it
drives
the
numbers
UP.
§ And
that
gets
teams
excited,
grows
your
capabilities,
increases
confidence,
and
so
on.
¡ Two
approaches:
§ Funnel
optimization
§ Russian
Doll
optimization
17. Decent Users
“Grade D”
Good Users
“Grade C”
Great Users
“Grade B”
Best Users
“Grade A”
1. Determine
differentiating
characteristics
of
“A”
2. Use
that
to
move
more
“B’s”
into
“A”
3. Repeat
4. Lessen
the
Delta
=
Widen
the
Base
18. The
right
data,
from
the
right
places
–
accurately
&
actionably
reported
Harness
Synthesize
Optimize
D
A
T
A
Intelligent
Interpretation
&
Insights
Iterative,
measured
execution
of
prioritized
data-‐
driven
tactics
Faster,
Better,
Decision-‐Making
to
Improve
KPIs