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This document summarizes research to improve statistical models for evaluating NFL players and coaches based on win probability added (WPA). The researchers analyzed over 132,000 plays from 2011-2014, incorporating additional data like score differentials and quarters. Their logistic regression model predicts win probability for each play based on down, yards to go, yards from goal line, time in game, score difference, and quarter. This WPA model evaluates individual player and coaching impacts and could help sportscasters and coaches. It accurately classified 2014 game outcomes with 86% accuracy in the 4th quarter.
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The document discusses different types of scales used to measure variables in marketing research, including nominal, ordinal, interval, and ratio scales. It explains what each scale measures and provides examples. Various scaling techniques are also covered, such as paired comparison scales, rank order scales, and constant sum scales that can be used to measure attitudes, preferences, and opinions.
Attitudes have cognitive, affective, and behavioral components and can be measured using various scales. Common scaling techniques include Likert scales, semantic differentials, and rankings. Likert scales ask respondents to rate level of agreement with statements, semantic differentials use bipolar adjective scales, and rankings order objects based on a given criterion. While scales provide attitude measurements, their ability to predict actual behavior is limited, as external factors also influence behavior.
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This document summarizes research to improve statistical models for evaluating NFL players and coaches based on win probability added (WPA). The researchers analyzed over 132,000 plays from 2011-2014, incorporating additional data like score differentials and quarters. Their logistic regression model predicts win probability for each play based on down, yards to go, yards from goal line, time in game, score difference, and quarter. This WPA model evaluates individual player and coaching impacts and could help sportscasters and coaches. It accurately classified 2014 game outcomes with 86% accuracy in the 4th quarter.
Score More: Using Weighted Score in Reports | SoGoSurveySogolytics
We all want passing scores on our quizzes and assessments, but what's "score" when it comes to rating questions? Grow understanding of your own results with Weighted Score. Learn more -- and learn better!
Score More: Using Percent Favorable in Reports | SoGoSurveySogolytics
Ready to take a look on the brighter side? Use Percent Favorable to review the positive ratings in your report at a glance and you'll know exactly where you stand. Learn more, then score more!
Vax, Masks, and The Space Between: Rating, Segmenting, and Cross TabsSogolytics
In part two of our series on Omni reporting drill-down, we're exploring how to make the best use of rating questions, segmenting data, and cross tabs -- all in the same report. Ready to learn more? Dive in!
This document discusses different types of measurement, ranking, and rating scales. It defines nominal, ordinal, interval, and ratio scales. It also defines and provides examples of various types of rating scales including dichotomous, category, Likert, numerical, semantic differential, itemized, fixed-sum, Stapel, graphic, and consensus scales. It discusses paired comparison and forced choice comparative scales as well as ranking scales.
The document discusses different types of scales used to measure variables in marketing research, including nominal, ordinal, interval, and ratio scales. It explains what each scale measures and provides examples. Various scaling techniques are also covered, such as paired comparison scales, rank order scales, and constant sum scales that can be used to measure attitudes, preferences, and opinions.
Attitudes have cognitive, affective, and behavioral components and can be measured using various scales. Common scaling techniques include Likert scales, semantic differentials, and rankings. Likert scales ask respondents to rate level of agreement with statements, semantic differentials use bipolar adjective scales, and rankings order objects based on a given criterion. While scales provide attitude measurements, their ability to predict actual behavior is limited, as external factors also influence behavior.
The document discusses compensation surveys, specifically the National Compensation Survey. It provides details on what compensation surveys are, how they measure wages and benefits, and how organizations use the data from surveys to develop competitive compensation packages to attract and retain employees. Compensation surveys give companies information to ensure their pay plans are in line with industry standards.
This document provides an orientation for experts on the Directly app, which allows experts to earn rewards for answering customer service questions. It outlines how the app works, including how experts can view questions, answer, vote on answers, and earn points and rewards. It provides guidance on best practices for answering questions, such as introducing oneself and being helpful, harmless, and honest. It also explains how customer ratings work and how points translate to monetary rewards.
This document discusses various methods for measuring attitudes, including ranking, rating, sorting, and choice tasks. It describes multi-category scales like Likert scales that measure the intensity of agreement/disagreement. Semantic differentials and behavioral differentials use bipolar adjective scales to measure attitudes. Paired comparisons directly compare preferences between objects or brands. The document provides examples of different types of scales and their uses in measuring attitudes.
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2) To understand why donors churn, organizations need to collect feedback directly from donors to identify which touchpoints need to be improved, scaled back or dropped. Feedback also helps organizations better service donors by addressing the root causes of negative experiences.
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The customer satisfaction survey is a feedback survey that will ask the customer how satisfied they are with your products or services. It usually consists of one question that covers everything that a company wants to know about customer satisfaction.
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Snapshot of winning submissions- Jigsaw Academy ValueLabs Sentiment Analysis ...Jigsaw Academy
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This document provides an orientation for new experts on the Directly app. It outlines the key features of the dashboard including stats, tasks, routing status and customer satisfaction ratings. It describes how to answer questions, provide feedback through upvotes/downvotes, and choose best answers. The document reviews how points, rewards and cashouts work and emphasizes responding helpfully, empathetically and professionally to customers according to the expert code of conduct. It directs experts to settings, resources and forums for further assistance.
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Credit Card Marketing Classification Trees Fr.docxShiraPrater50
Credit Card Marketing
Classification Trees
From Building Better Models with JMP® Pro,
Chapter 6, SAS Press (2015). Grayson, Gardner
and Stephens.
Used with permission. For additional information,
see community.jmp.com/docs/DOC-7562.
2
Credit Card Marketing
Classification Trees
Key ideas: Classification trees, validation, confusion matrix, misclassification, leaf report, ROC
curves, lift curves.
Background
A bank would like to understand the demographics and other characteristics associated with whether a
customer accepts a credit card offer. Observational data is somewhat limited for this kind of problem, in
that often the company sees only those who respond to an offer. To get around this, the bank designs a
focused marketing study, with 18,000 current bank customers. This focused approach allows the bank to
know who does and does not respond to the offer, and to use existing demographic data that is already
available on each customer.
The designed approach also allows the bank to control for other potentially important factors so that the
offer combination isn’t confused or confounded with the demographic factors. Because of the size of the
data and the possibility that there are complex relationships between the response and the studied
factors, a decision tree is used to find out if there is a smaller subset of factors that may be more
important and that warrant further analysis and study.
The Task
We want to build a model that will provide insight into why some bank customers accept credit card offers.
Because the response is categorical (either Yes or No) and we have a large number of potential predictor
variables, we use the Partition platform to build a classification tree for Offer Accepted. We are primarily
interested in understanding characteristics of customers who have accepted an offer, so the resulting
model will be exploratory in nature.1
The Data Credit Card Marketing BBM.jmp
The data set consists of information on the 18,000 current bank customers in the study.
Customer Number: A sequential number assigned to the customers (this column is hidden and
excluded – this unique identifier will not be used directly).
Offer Accepted: Did the customer accept (Yes) or reject (No) the offer.
Reward: The type of reward program offered for the card.
Mailer Type: Letter or postcard.
Income Level: Low, Medium or High.
# Bank Accounts Open: How many non-credit-card accounts are held by the customer.
1 In exploratory modeling, the goal is to understand the variables or characteristics that drive behaviors or particular outcomes. In
predictive modeling, the goal is to accurately predict new observations and future behaviors, given the current information and
situation.
3
Overdraft Protection: Does the customer have overdraft protection on their checking account(s)
(Yes or No).
Credit Rating: Low, Medium or High.
# Credit Cards Held: The number of cred ...
There are several methods for measuring attitudes, including ranking, rating, sorting, and choice tasks. The Likert scale is a popular method that uses statements with response options ranging from "strongly agree" to "strongly disagree". The semantic differential uses bipolar adjective scales to measure attitudes. Numerical scales use numbers instead of words to identify response categories. Paired comparisons directly compare objects to determine preferences. Graphic rating scales use visual representations instead of words.
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See the recording here: http://customer-success.getamity.com/customer-success-resources
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The document discusses ServiceNow assessments, which are used to gather customer feedback on tickets. It provides details on assessment structure, reporting, notifications, and best practices for assignment group managers. Key points include that assessments use a Likert scale, generate scorecard reports for analysts and groups, and managers should review negative feedback to ensure customer satisfaction.
The document provides instructions for completing an online benefits survey in 3 steps: 1) Login using provided credentials, 2) Complete the survey by answering questions grouped in sections and saving after each section, monitoring progress on a progress bar, and 3) Submit the completed survey once the progress bar reaches 100% after reviewing all answers. The survey consists of groups that can be completed individually and saved before the final submission deadline.
This document outlines a proposal to analyze customer relationship management (CRM) data to predict young female customers' propensity to apply for a debit card. The objective is to test hypotheses about factors that influence application rates. The analysis would involve segmenting customers, predictive modeling using logistic regression, and multivariate testing of marketing campaigns on social media. The expected results are identification of key customer parameters, predictive models to increase conversion rates, and insights to improve targeted advertisements.
Jargon can be a useful tool to communicate with employees or customers. But it should be used carefully, and your target audience must know what you're talking about.
More Related Content
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This document discusses various methods for measuring attitudes, including ranking, rating, sorting, and choice tasks. It describes multi-category scales like Likert scales that measure the intensity of agreement/disagreement. Semantic differentials and behavioral differentials use bipolar adjective scales to measure attitudes. Paired comparisons directly compare preferences between objects or brands. The document provides examples of different types of scales and their uses in measuring attitudes.
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1) Donor churn occurs when donors stop giving to an organization due to negative interactions or a lack of consistency across interactions. The root causes of churn develop over time due to unmet expectations from acquisition through ongoing interactions.
2) To understand why donors churn, organizations need to collect feedback directly from donors to identify which touchpoints need to be improved, scaled back or dropped. Feedback also helps organizations better service donors by addressing the root causes of negative experiences.
3) Collecting donor feedback, even just once, and applying the insights can significantly improve donor retention, satisfaction and fundraising performance over time.
Donations and Pledges Part 2_BLG Build.pdfBloomerang
This document provides a summary of a Bloomerang Academy webinar on donations and pledges. It discusses the differences between pledges and recurring donations, how to create and edit pledges and add pledge payments, how to handle payment failures, and how to refund or delete transactions. It also covers splitting donations, pledge and recurring donation reports, and creating pledge and recurring donation reminders. Resources for more information on these topics from the Bloomerang knowledgebase are provided at the end.
This document discusses different types of measurement scales that can be used to measure attitudes and preferences. It describes rating scales, ranking scales, sorting scales, and other preference scales. Some key decisions in selecting a measurement scale include the research objectives, number of dimensions being measured, whether choices are forced or unforced, and the number of scale points. The document provides examples of different scales such as Likert scales, semantic differentials, rankings scales, and sorting techniques. It also discusses concepts like balanced vs unbalanced scales and errors to avoid in measurement.
The document provides an orientation for new experts in the Directly app. It summarizes the key aspects of the Directly dashboard and tools for experts. It explains how experts can view and track their stats, tasks, and rewards. It also provides guidance on best practices for answering questions, interacting with customers, and collaborating with other experts.
The customer satisfaction survey is a feedback survey that will ask the customer how satisfied they are with your products or services. It usually consists of one question that covers everything that a company wants to know about customer satisfaction.
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Snapshot of winning submissions- Jigsaw Academy ValueLabs Sentiment Analysis ...Jigsaw Academy
Rajesh Peruri analyzed a dataset containing client feedback comments and recommended scores (RECOM) to predict RECOM using sentiment analysis of the comments. The analysis included: determining sentiment scores of comments using association matrices and clustering; and building a linear regression model relating RECOM to other variables and sentiment. Rajanikar performed sentiment analysis of comments by assigning sentiment scores to words based on a dictionary and identifying sentiment polarity. Priyadarshini used a sentiment algorithm to assign integer sentiment scores to comments and analyzed the distribution and relationship of sentiment scores and average ratings scores.
This document provides an orientation for new experts on the Directly app. It outlines the key features of the dashboard including stats, tasks, routing status and customer satisfaction ratings. It describes how to answer questions, provide feedback through upvotes/downvotes, and choose best answers. The document reviews how points, rewards and cashouts work and emphasizes responding helpfully, empathetically and professionally to customers according to the expert code of conduct. It directs experts to settings, resources and forums for further assistance.
This document discusses conducting customer needs surveys. It describes different types of surveys, including printed, phone, in-person, and internet surveys. Customer surveys can provide information like customers' revenue, location, current purchases, and decision-making factors. The best survey type depends on how to reach customers. Surveys have advantages like judging loyalty and satisfaction, but also disadvantages like privacy issues and limited responses. Various groups conduct customer research, including marketing departments, research firms, and government. Customer research can improve products but also raises privacy and cost concerns. The document outlines summarizing survey data through graphs, proportions, and ratings.
Credit Card Marketing Classification Trees Fr.docxShiraPrater50
Credit Card Marketing
Classification Trees
From Building Better Models with JMP® Pro,
Chapter 6, SAS Press (2015). Grayson, Gardner
and Stephens.
Used with permission. For additional information,
see community.jmp.com/docs/DOC-7562.
2
Credit Card Marketing
Classification Trees
Key ideas: Classification trees, validation, confusion matrix, misclassification, leaf report, ROC
curves, lift curves.
Background
A bank would like to understand the demographics and other characteristics associated with whether a
customer accepts a credit card offer. Observational data is somewhat limited for this kind of problem, in
that often the company sees only those who respond to an offer. To get around this, the bank designs a
focused marketing study, with 18,000 current bank customers. This focused approach allows the bank to
know who does and does not respond to the offer, and to use existing demographic data that is already
available on each customer.
The designed approach also allows the bank to control for other potentially important factors so that the
offer combination isn’t confused or confounded with the demographic factors. Because of the size of the
data and the possibility that there are complex relationships between the response and the studied
factors, a decision tree is used to find out if there is a smaller subset of factors that may be more
important and that warrant further analysis and study.
The Task
We want to build a model that will provide insight into why some bank customers accept credit card offers.
Because the response is categorical (either Yes or No) and we have a large number of potential predictor
variables, we use the Partition platform to build a classification tree for Offer Accepted. We are primarily
interested in understanding characteristics of customers who have accepted an offer, so the resulting
model will be exploratory in nature.1
The Data Credit Card Marketing BBM.jmp
The data set consists of information on the 18,000 current bank customers in the study.
Customer Number: A sequential number assigned to the customers (this column is hidden and
excluded – this unique identifier will not be used directly).
Offer Accepted: Did the customer accept (Yes) or reject (No) the offer.
Reward: The type of reward program offered for the card.
Mailer Type: Letter or postcard.
Income Level: Low, Medium or High.
# Bank Accounts Open: How many non-credit-card accounts are held by the customer.
1 In exploratory modeling, the goal is to understand the variables or characteristics that drive behaviors or particular outcomes. In
predictive modeling, the goal is to accurately predict new observations and future behaviors, given the current information and
situation.
3
Overdraft Protection: Does the customer have overdraft protection on their checking account(s)
(Yes or No).
Credit Rating: Low, Medium or High.
# Credit Cards Held: The number of cred ...
There are several methods for measuring attitudes, including ranking, rating, sorting, and choice tasks. The Likert scale is a popular method that uses statements with response options ranging from "strongly agree" to "strongly disagree". The semantic differential uses bipolar adjective scales to measure attitudes. Numerical scales use numbers instead of words to identify response categories. Paired comparisons directly compare objects to determine preferences. Graphic rating scales use visual representations instead of words.
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Measuring Customer Success performance is a challenge. Customer Success teams have a wide range of goals, and creating a balanced scorecard is not always easy.
In this webinar, Joel Carron, Customer Success Analyst at Mode, shares a framework you can adopt to analyze and visualize Customer Success performance. Using interactive 3D charting, we discuss how to measure your performance as a CSM - and that of your team - focusing on Retention Rate, Expansion MRR %, and NPS.
See the recording here: http://customer-success.getamity.com/customer-success-resources
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The document discusses ServiceNow assessments, which are used to gather customer feedback on tickets. It provides details on assessment structure, reporting, notifications, and best practices for assignment group managers. Key points include that assessments use a Likert scale, generate scorecard reports for analysts and groups, and managers should review negative feedback to ensure customer satisfaction.
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This document outlines a proposal to analyze customer relationship management (CRM) data to predict young female customers' propensity to apply for a debit card. The objective is to test hypotheses about factors that influence application rates. The analysis would involve segmenting customers, predictive modeling using logistic regression, and multivariate testing of marketing campaigns on social media. The expected results are identification of key customer parameters, predictive models to increase conversion rates, and insights to improve targeted advertisements.
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Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
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Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
2. • Hot on the heels of Score More:
Weighted Scores, this entry is all
about Net Intent.
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3. • Net Intent lets you quantify the
overall sentiment of your responses.
• You can assign a positive, neutral, or
negative sentiment to each answer
option on a rating question type.
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4. • The percent of negative responses is
subtracted from the percent of
positive responses, and you get a
number between -100 and 100 that
represents the overall sentiment of
your participants.
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9. • In the Omni Report, choose Score and
select Net Intent.
• Within options, choose to
customize the calculation.
• Show Net Intent as a number or
graph.
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