Presentation by Dave Eglese
Looks at what Predictive Analytics actually is and how we can use this to inform Marketing and Recruitment Strategy and Tactics
Creating Wraparound Supports for Students through Internal PartnershipsJeremy Anderson
Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
Creating Wraparound Supports for Students through Internal PartnershipsJeremy Anderson
Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
Institutional Success Via a Data-Centric Technology EcosystemIT Consultant
The content and data landscape is vast and decentralized, and aggregating assets via a unified technology strategy poses significant challenges. This presentation describes how to design a federated technology ecosystem to inform recruitment, marketing, and outreach efforts, and increase student retention through the innovative use of data modeling and predictive analytics. Collecting data for actionable insight, leveraging CRM, web, and mobile channels, and tracking retention and graduation rates will be highlighted.
In this age of digital disruption we should take a step back and have the digital literacy discussion. We might have to change our thought process around training and empowering people.
What is Digital Literacy – 8 pillars
How Microsoft Office 365 supports digital literacy
Literacy statistics and ROI on training
Why does Digital Literacy affect user adoption?
EMPOWER A CAREER JOURNEY: FOSTER YOUR WORKFORCE’S GROWTH AND DEVELOPMENTHuman Capital Media
Learning and development is critical to an organization, if you don't help the workforce learn and grow in their jobs and their roles, they're not going to be engaged in their positions. Join Ryan Rippy, Talent Management System Administrator at Trustmark Bank as he discusses the challenges of taking a manual process and automating it to achieve business goals and track performance across roles - using succession planning to create a talent pipeline for key positions and developing all associates along their journey.
By the conclusion of the webinar, you’ll leave with:
Ways to help your workforce be engaged in their jobs and be engaged as employees
The benefits a succession plan has to your organization and your employees
Effective LMS strategies to integrate talent modules
View successful metrics and how it begins with onboarding through performance management and into development
Institutional Success Via a Data-Centric Technology EcosystemIT Consultant
The content and data landscape is vast and decentralized, and aggregating assets via a unified technology strategy poses significant challenges. This presentation describes how to design a federated technology ecosystem to inform recruitment, marketing, and outreach efforts, and increase student retention through the innovative use of data modeling and predictive analytics. Collecting data for actionable insight, leveraging CRM, web, and mobile channels, and tracking retention and graduation rates will be highlighted.
In this age of digital disruption we should take a step back and have the digital literacy discussion. We might have to change our thought process around training and empowering people.
What is Digital Literacy – 8 pillars
How Microsoft Office 365 supports digital literacy
Literacy statistics and ROI on training
Why does Digital Literacy affect user adoption?
EMPOWER A CAREER JOURNEY: FOSTER YOUR WORKFORCE’S GROWTH AND DEVELOPMENTHuman Capital Media
Learning and development is critical to an organization, if you don't help the workforce learn and grow in their jobs and their roles, they're not going to be engaged in their positions. Join Ryan Rippy, Talent Management System Administrator at Trustmark Bank as he discusses the challenges of taking a manual process and automating it to achieve business goals and track performance across roles - using succession planning to create a talent pipeline for key positions and developing all associates along their journey.
By the conclusion of the webinar, you’ll leave with:
Ways to help your workforce be engaged in their jobs and be engaged as employees
The benefits a succession plan has to your organization and your employees
Effective LMS strategies to integrate talent modules
View successful metrics and how it begins with onboarding through performance management and into development
PANORAMA NECTO 14 TRAINING - Panorama is leading a Business Intelligence 3.0 revolution and a creation of a new generation of Business Intelligence & Data Discovery solutions that enable organizations to leverage the power of Social Decision Making and Automated Intelligence to gain insights more quickly, more efficiently, and with greater relevancy.
www.panorama.com
Deep Dive on Vox's User Engagement - User Engagement TeardownIterable
This Iterable User Engagement Teardown looks at Vox, a news site that is part of the Vox Media group.
It analyzes how Vox engages users in the first 3 weeks post-signup. The User Engagement Timeline lets you visualize all engagement, and we also evaluate individual emails and suggest improvements.
Presentación sobre el tema de programacion neurolinguistica PNL metaprogramas, que son, los filtros bajo los cuales las personas toman decisiones, conocer los metaprogramas de las personas es una herramienta muy util en la programacion neurolinguistica, ya que pueden ser herramientas que ayudan en mucho en los procesos de cambio de las personas
At the University of Calgary, we used real‐time data on student applications to provide the Enrolment Services with better predictive analytics on the students that were offered a place at the University. IR offices are well placed to leverage institutional data to make these predictions. Our knowledge of the data and analytical tools can make us leaders in predictive analytics at our institutions. This presentation will discuss the issues about developing the models, finding the best model and putting it to use. These lessons are applicable to applying these techniques to many situations.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Data Analyst Interview Questions & AnswersSatyam Jaiswal
Practice Best Data Analyst Interview Questions for the best preparation of the data analyst interview. these interview questions are very popular and asked various times in data analyst interview.
Quantitative techniques for business analysissmumbahelp
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Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docxtodd271
Running head: CS688 – Data Analytics with R1
CS688 – Data Analytics with R10
CS688 – Data Analytics with R
Surendra Parimi
CS688 – Introduction to CRISP-DM and the R platform IP 1
Colorado Technical University
07/10/2019
Table of Contents
Introduction to CRISP-DM and the R Platform Organizational Background3
Organizational Background:3
CRISP-DM(Cross-industry standard process for data mining):3
Data Maturity:4
Role of Data Analyst:6
How Do we Implement the R Platform:6
R Modeling With Regressions and Classifications (TBD)7
Model Performance Evaluation (TBD)8
Visualizations With R (TBD)9
Machine Learning (TBD)10
References11
Introduction to CRISP-DM and the R Platform Organizational BackgroundOrganizational Background:
The organization I currently work for and planning to implement the techniques of the data analytics course is T-Mobile USA, which offers wireless mobile phone services to 0ver 80 million customers in the United States. It’s a huge enterprise with large scale information technology systems that support the business that T-Mobile does. The company is seeing significant growth in terms of business and therefore the IT systems that are supporting the business. Myself as a DEVOPS engineer works on deploying the code to these mission critical systems, host them and operate to make sure the systems are working as expected. As the land scape of our IT systems grow, we want to be able to identify the issues in our systems in advance so that we can prevent them before causing any outage to the business. To achieve such a result, our IT systems logs needs to be analyzed in-depth to unleash the critical insights about the system performance and apply the feedback to improve our systems.
CRISP-DM(Cross-industry standard process for data mining):
The CRISP-DM helps us ensure our data analysis adheres certain standards and CRISP-DM is a proven strategy worldwide. Corporations like IBM have further enhanced and or customized the standard and came up with their own methodology knows as ‘Analytics
Solution
s Unified Method for Data Mining/Predictive Analytics(ASUS_DM)’
The CRISP-DM methodology involves 6 different steps
Business Understanding: Building the knowledge about business requirements and objectives from functional aspect and transforming this knowledge as a data mining objective with an implementation plan.
Data Understanding: Involves the process of data collection from diverse sources of data, review and understand the data to be able to identify the problems which compromise data quality and also give the initial understanding of what the data can deliver.
Data Preparation: The data preparation phase covers all activities to build the final dataset from the initial raw data collected.
Modeling: Modeling techniques are based on the objective of the problem being tried. So, based on the problem, model is decided and based on the model, data is collected.
Evaluation: The evaluation phase is taken up once.
DAT 520 Final Project Guidelines and Rubric Overview .docxsimonithomas47935
DAT 520 Final Project Guidelines and Rubric
Overview
You must complete a decision analysis research project as your final project for this course. Your research project will focus on a real-world topic of your choice,
as approved by your instructor. You will pick a topic from the list provided or with approval from your instructor, and create a data analysis plan and decision
tree model based on a real-world scenario. This assessment will provide you with the opportunity to employ highly valued decision support skills and concepts
for data within a real-world context. You can use the Final Project Notes document, found in the Assignment Guidelines and Rubrics section of the course.
The project is divided into three milestones, which will be submitted at various points throughout the course to scaffold learning and ensure quality final
submissions. These milestones will be submitted in Modules Two, Five, and Seven. The final submission will occur in Module Nine.
This project will address the following course outcomes:
Appraise data in context according to industry-standard methods and techniques for its utility in supporting decision making
Determine suitable data manipulation and modeling methods for decision support
Articulate data frameworks for organizational decision support by applying data manipulation, modeling, and management concepts
Evaluate the ethical issues surrounding organizational use of decision-oriented data based on industry standards and one’s personal ethical criteria
Create and assess the agility of solutions through application of data-mining procedures for decision support in various industries
Prompt
Your decision analysis model and report should answer the following prompt: How does your model and evaluation resolve uncertainty in making a decision? In
order to produce your analytic report, you will need to choose and investigate a data set using the decision analysis techniques you learned in class. Then you
will formulate a research question, write an analytic plan, and implement it. Your report should not solely consist of descriptions of what you did. It should also
contain detailed explorations into the meaning behind your model and the implications of its results. You will also be testing your model’s fitness and evaluating
its strengths and weaknesses.
The project in a nutshell:
1. Choose a data set (get ideas from the source list in the spreadsheet Final Project Topics and Sources.xls)
2. Formulate your decision analysis research question
3. Write an analytic plan
4. Perform the top-down or bottom-up modeling
5. Perform model diagnostics
6. Evaluate
These activities are broken up into milestones so that the work is spread throughout the term and you can get early assistance with any obstacles.
A decision analysis report is similar to any other analytic report. These reports introduce a problem, state a line of inquiry, explain a model th.
NCV 3 Business Practice Hands-On Support Slide Show - Module 6Future Managers
This slide show complements the learner guide NCV 3 Business Practice Hands-On Training by Nickey Cilliers, published by Future Managers Pty Ltd. For more information visit our website www.futuremanagers.net
PLEASE READ BACKGROUND INFO BELOW. TURNIT IN IS USEDModule 4 .docxjanekahananbw
PLEASE READ BACKGROUND INFO BELOW. TURNIT IN IS USED
Module 4 - SLP
Strategy Implementation and Strategic Controls
Simulation
In Module 4, you will continue with the CVP analysis you completed in the Module 3 SLP.
Scenario Continuation:
It is still January 2, 2012.
You have just completed your revised SLP3 strategy using CVP analysis, and you are eager to implement your decisions for 2012 through 2014.
Using the CVP analysis from SLP3, run the simulation for a final time. Again, be sure to take notes about your analysis and the document the reasoning behind your decisions.
Finalize your report showing the strategy you have used.
Assignment Overview
Using the strategy that you developed in SLP3, run the simulation. Document your results as you did previously. Review and analyze these results, and develop a final strategy.
Please turn in a
6- to 8
-
page
paper, not including cover and reference pages.
Keys to the Assignment
The key aspects of this assignment that should be covered and taken into account in preparing your paper include:
The revised strategy consists of the Prices, R&D Allocation %, and any product discontinuations for the X5, X6, and X7 tablets for each of the four years: 2012, 2013, 2014, and 2015.
You must present a rational justification for this strategy. In other words, you must provide support for your proposed strategy using financial analysis and relevant theories.
Use the CVP Calculator and review the PowerPoint that explains CVP and provides some examples.
You will need to
crunch
some numbers (CVP Analysis) to help you determine your prices and R&D allocations.
Make sure your proposed changes in strategy are firmly based in this analysis of financial and market data and sound business principles.
Your goal is to practice using CVP and get better at it.
Present your analysis professionally, making strategic use of tables, charts, and graphs.
Time Line Summary:
SLP1
2015: Hired on December 15.
Turned in first report to CEO Smothers.
SLP2
You are returned – via Time Warp – to January 1, 2012.
You make decisions for 2012 – 2015.
December 31, 2015 – You have revised all four years, and you write up your summary report.
SLP3
Apparently, your SLP2 decisions were not “good enough,” as you’ve again been returned to January 1, 2012.
It is once again
January 1, 2012:
You decide to use CVP analysis to develop a revised four-year plan for your strategy. You analyze the results of your first decisions from SLP2, taking notes, and documenting your decision-making process. You use the CVP Calculator to help you develop your strategy. Your notes explaining the logic behind your decisions.
SLP4
It is still January 2, 2012. Using your CVP analysis from SLP3, you run the simulation, implementing your revised four-year plan. You keep track of your financial and marketing results year over year.
You submit your final
6-8 page
report, which includes your Final Total Score.
You compare – and report – your results with previous re.
This presentation, presented by Ellen Wagner and Howard Bell at the ASU+GSV Conference in May 2017, outlines the need for supports when it comes to student success.
SUNY Broome is one of 64 campuses in the State University of New York System and a new member of Achieving the Dream. “Joining Achieving the Dream was important for us,” said Heather Darrow, Staff Associate for Student Retention. “We are striving to become a college that is proactive and not reactive. I think that’s why we joined when we did - and why we invested in Starfish. Both investments demonstrate our administration’s commitment to student success.” SUNY Broome focused on early alert flags and Kudos in their initial implementation, and now they are eager to do more. They are training faculty, building automated workflows around flags, and developing ways to encourage participation both within the faculty and for those in non-academic roles. This Webinar will focus on advice and “lessons learned” in the early stages of implementing the Starfish platform at a community college. As Heather Darrow said, “In the beginning it seemed very abstract – I know it can be hard to conceptualize how Starfish will work. But I figured it out, and others can too. I look forward to helping other schools!” Speakers: Heather Darrow, Staff Associate for Student Retention Michelle Beatty, Online Student Advisor
Opportunities to Engage First Year Students at Community CollegesHobsons
As part of the Student Success and Support Program (SSSP) led by the Chancellor’s Office, Los Medanos College began implementing tools from the Starfish Enterprise Success Platform – specifically, early alert and degree planning – in 2015. In this Webinar, you’ll learn about their recipe for implementing student success technologies within a statewide initiative.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
4. # H U E M E A 1 6
We’ll be looking at what Predictive Analytics actually is
and how we can use this to inform Marketing and
Recruitment Strategy and Tactics
Itmaybebig,itmaybescary,butPredictiveAnalyticscan
giveyounewperspectives
5. To understand what Predictive Analytics is.
To relate examples you may have come
across in the sector and in other industries to
understand the breadth and range of
applications.
To provoke thinking about the
problems/situations you want to solve or
investigate to make changes.
1 Understanding that there are many different
tools from Excel to data mining in
programming languages like R and Python.
I’m using SPSS Modeler in this presentation.
Looking at a CRISP – DM process as a
framework
Measuring and evaluating engagement through
CRM and conversion activities to
optimise/prioritise/identify opportunities and
reduce threats.
2
3
4
5
6
What on earth? How?
Where is this happening?
Where do I start? What does this have to do with
CRM?
Yeah…but, what’s the process?
Today’s outcomes
6. # H U E M E A 1 6
Examples
Drag or drop your photograph here
7. # H U E M E A 1 6
Google notification the other day
8. # H U E M E A 1 6
Examples continued…(How an UBER style service may use PA)
• Overview of an Uber style model:
9. # H U E M E A 1 6
What new data are we creating with Predictive
Analytics?
Estimates, Forecasts, Probabilities, Recommendations, Propensity Scores (Lead
Scoring), Classifications etc.
Drag or drop your photograph here
11. # H U E M E A 1 6
Some problems may we look at in universities using PA?
Drag or drop your photograph here
12. # H U E M E A 1 6
Let’s take prioritisation as an example - Admissions
• Should we treat every application the same when certain factors may indicate a higher
propensity to register or a higher desirability based on other set criteria?
• One test we’re looking at is for the introduction a level of prioritisation to improve response
times for certain applicants (initially focussing on International PGT students)
What’s in the Model?
13. # H U E M E A 1 6
Modelling using historical data
• We can look at demographics – age, region, gender
• We can look at the application detail – application time, subject, qualifications, school,
provider etc.
• We need known outcomes from data to base a model on– THANKFULLY we keep a recent
record of previous cycles of admissions data that we can interrogate
• Any created model can be applied to new data to get probabilities or in other
words…predictions
• Created models (patterns and formulas) need to be examined and tested thoroughly to
ensure you can select a winning model.
14. # H U E M E A 1 6
CRISP-DM - Cross-Industry Standard Process for Data Mining
1. Business Understanding – To improve turnaround times for valuable applications to improve
conversion – set objectives for evaluation purposes, and understand how this data will be used
operationally.
2. Data Understanding –
– What data sources can we use (Application DB/warehouse, CRM?)
– What fields will effect objective – explore data
– Essential – what is the target field – “Registered Student”
3. Data Preparation – this is where you should be spending your time. Getting the data together in
correct format – integrating data, banding variables (perhaps application month?)
4. Modelling – Run data through model to generate results
– Data led or hypothesis led – what variables are you including?
5. Evaluate – run models with know outcome (70% train, 30% test, possibly also an evaluation set)
6. Deploy winning model … against new applications that are received to give a probability score to be
processed.
15. # H U E M E A 1 6
Example using SPSS Modeler
1. After aggregating data needed I add the testing records into SPSS Modeler as a node
2. This could be may types of files, but this one is an SPSS var file. This is then connected to a
filter node where I choose which fields I want any model to consider.
16. # H U E M E A 1 6
Example using SPSS Modeler continued…
3. These is the first attempt at filtering the fields I want to
include:
4. You can see many are allowed to pass.
5. Others are not considered worthy to be put in the model
6. Determine what role each field has in the model using Type
node
17. # H U E M E A 1 6
Example using SPSS Modeler continued…
7. This is where you can choose the data types and the role
the field plays in the model:
18. # H U E M E A 1 6
Example using SPSS Modeler continued…
8. Now we can see if a model can be run.
9. Skipping a few steps I’m going to run the data in 2
sets. This is partitioning the data
– 1 step will be to generate the model
– the other will be to test the model to see if it has
worked correctly.
10. I can run an automated mode to see if the software
can decide which statistical model(s) to use:
19. # H U E M E A 1 6
Example using SPSS Modeler continued…
11. The autoclassifier node will look at
different models and suggest a shortlist
based on the accuracy achieved. I could
use the result or use it as a guide to look at
the individual models in detail:
20. # H U E M E A 1 6
Example using SPSS modeler continued…
• CHAID model – Easy to understand and present
• Splits the data into a decision tree based with the most important factors at the top of the
tree
– Can develop ensemble models to improve complexity/considered factors
– Settings can also be made in the model to determine how many splits you want to allow in the data
• It will tell also tell us the most important factors in the data affecting the model:
• Will give us a view of whether a registration is likely or not, but crucially in this example
where conversion is very low anyway it will give us a propensity score and an adjusted
propensity score
21. # H U E M E A 1 6
Using a propensity score
• The propensity score used can be the lead scoring to apply to new applications without a
decision, if you feel you have developed the model to the required accuracy
22. # H U E M E A 1 6
Apply model to new data
• The model node created can be connected to a new set of data using the same field names,
although this time there doesn’t need to be a target.
• The propensity scores given can be use to create priority lists within the admissions service
to process in order to improve turnaround times for applicants and improve conversion from
these applications
23. # H U E M E A 1 6
CRM Sources
• Many of you will be evaluating how successful your CRM campaigns are – can we take results
of these evaluations and test this with admissions data?
• We plan to test the following CRM activity alongside application data:
– Opening conversion e-mails
– Interacting with x e-mails may be a good indicator of engagement
– Registered/attended within designated events
– Took part in a call campaign
– Attended webinar
24. # H U E M E A 1 6
In summary
Enquiring, applying and registering at a university is
normally a long journey and there are many
TOUCH/DATA POINTS along the way to create rich
data that we can use to enhance our marketing
strategies and recruitment tactics to facilitate these
journeys.