ANALYTICS | CASE STUDY

Use Data to Drive Personalized Marketing

COMPANY OVERVIEW


A large provider of travel and vacation services was looking to personalize their marketing efforts with the overall goal of increasing revenue and creating returning customers.

BUSINESS NEED


The company needed to stimulate growth and gain more returned value from marketing campaigns. Their goals were to increase sales of vacation packages, re-engage previous customers, and apply a smarter marketing campaign.

SOLUTION


Ironside’s BI team used the client’s data to deliver a solution by doing the following:


Refine the Focus


  • Collect data at a granularity that permits propensity score creation for each prior customer

  • Use predictive analytics to identify customers most likely to be interested in purchasing specific vacation packages

  • Target the flagged customers with promotional material tailored to their vacation interests and propensities

  • Understand the model drivers and characteristics of the customers associated with specific types of vacation packages and use this information in other marketing endeavors


Define the Roadmap


  • Collect necessary historical customer data and cleanse it

  • Build a propensity to buy model which identifies patterns in customer data associated with purchasing specific vacation packages

  • Use the model to assign a likelihood to purchase score which will align prospective customers with prior customers similar to them on key features

  • Group prospective customers according to likelihood to purchase each package and design distinct marketing campaigns

  • Follow campaign success and continue to enhance model performance with continuous learning


Develop the Model


  • Compile and cleanse data from multiple sources

  • Ask relevant questions to ensure the resulting model is accurate

  • Test predictive modeling methods and use the best to predict likelihood to buy each package type


Inform Action


  • Enable the client to zero in on key segments of their prospective customer base and market to them in a more customized fashion

  • Afford the ability to apply less costly and more generalized marketing efforts to individuals unlikely to make a purchase; therefore diverting more of their marketing funds and efforts to those more likely to purchase

  • Provide insight into customer characteristics and other drivers associated with specific vacation packages


KEYS TO SUCCESS


The propensity model engagement resulted in the following benefits:

  • Insight into what drives customer preferences

  • Specialized the sales/marketing efforts on customers

  • Monetized data that otherwise was under-utilized

  • Empowered the client to use advanced techniques to maximize sales

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ANALYTICS | CASE STUDY

Predict Higher Education Student Retention Risk

COMPANY OVERVIEW


A community college providing open access to quality higher education was looking for a way of predicting student retention risk so they could intervene and help at-risk students succeed.

BUSINESS NEED


The college’s student retention rate over 6 years was below 50 percent. They knew they needed to find proactive ways of identifying at-risk students, but weren’t sure how to best leverage their data to do it. The college’s Institutional Research team brought in Ironside to test predictive modeling techniques, evaluate and select a set of predictor variables on which to base student risk scores, segment students into groups based on GPA attainment and retention likelihood, integrate with the college’s student services model, data warehouse, and BI platform, and output individualized student risk scores and intervention/retention strategies for at-risk students.

SOLUTION


The Ironside Data Science & Advanced Analytics team is dedicated to helping organizations realize the full potential of their data resources. The client brought in the team to do the following:


Define the Vision


  • Collect data at a granularity that permits risk score creation for each enrolled student

  • Trigger actions and alerts based on student behaviors or academic events

  • Fit recommendations to each student’s needs, allowing long-term intervention plans

  • Feed into the data warehouse to compare with actuals/the student services model


Prepare the Data


  • Client needed to prepare and transform their data for use in predictive models

  • Analysts spent 70-80% of their total project time working with Ironside experts to extract and refine the information needed for their predictive model


Develop & Refine Scoring Methods


  • Isolate productive variables and designate variable categories for each model

  • Train models against 10 years’ worth of student data (approx. 68,000 unique records) using multiple predictive algorithms

  • Implement linear regression models based on findings from modeling tests

  • Weight chosen variables based on relative importance in overall risk score outputs

  • Identify four student service quadrants: GPA & retention safe, GPA safe but retention at risk, GPA at risk but retention safe, and GPA & retention at risk

  • Feed results into data warehouse and begin defining next best actions based on reports


Ensure Future Progress


  • Client’s internal resources gained hands-on experience with their student retention risk models and with variable and algorithm selection

  • Ensured they could use their student retention risk models as a springboard into other modeling projects

  • Opened the door for future insights into student behavior and other operational projections like donor optimization


KEYS TO SUCCESS


Through predictive analysis, the client was able to:

  • Enable mass deployment of individualized student services and target first year experiences

  • Refine the views on established business practices with hard facts and accurate predictions, fueling change

  • Affirm the importance of specific variables for student success and tie them to actual effects on the student population

  • Empower analysts with a predictive analytics knowledge base so they can better improve policies and assist students

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ANALYTICS | CASE STUDY

Optimize Donations at Michigan State University

COMPANY OVERVIEW


Michigan State University (MSU) is a public research university in East Lansing, Michigan and is the eighth-largest university in the United States in terms of enrollment. As recently as the early 1990s, MSU was last among the Big Ten schools in endowments, with barely over $100 million in funds. This changed in the 2000s, when the University started a campaign to increase the size of the endowment.

BUSINESS NEED


MSU’s University Advancement team needed to strategically engage nearly one million alumni and friends of the university in the most strategic way possible. They needed help providing a team of 200 professionals and support staff with actionable insights, managing an Oracle transactional database with more than 1 million records (alumni, donors, friends, organizations). The staff was working with limited resources to analyze the volume of data and maximize the return on the smallest number of targets.

SOLUTION


From strategy through execution, business analytics is a process. Ironside helped MSU with the predictive modeling process by doing the following:


Define A Roadmap


  • Gather and understand information about consumer needs, behaviors, and preferences

  • Analyze a multitude of formats including postal mail, telemarketing, email marketing, texting, crowd sourcing, and social media

  • Focus on high net-worth individuals segmented by region, high-paying degrees, industries, and salaries

  • Profile potential and/or likely candidates for major gift giving


Organize Data & Information


  • Identify internal and external sources that can give you the information you need to make data-driver decisions

  • Identify which best-of-breed technologies will give you the insights you need


Implement, Measure, Refine


  • Layer the predictive models over the existing BI implementation to calculate affinity scores for potential donors

  • Calculate affinity scores for potential donors based on the vast array of variables

  • Implement a predictive model, taking in both structured and unstructured data. Update the model daily so users can keep pace with any changes

  • Create one reporting environment that manages dynamically enriched data. Support with data integrity, data governance, maintenance, and real-time intelligence through analytics

  • Improve organizational performance through new BI tool introductions and training


Hands-on Coaching & Mentoring


  • Provide MSU analytics team with hands-on experience of being actively involved in the predictive analytics and data mining activities during the development of each of the scoring models


KEYS TO SUCCESS


With Ironside’s Advanced Analytics team, MSU was able to:

  • Increase data visibility across the institution, allowing for quicker action on findings

  • Boost business analyst and director productivity turning multi-week studies into real-time report insights

  • Reduce internal resource cost of identifying strong donation prospects

  • Achieve a 55% ROI payback after 2.1 years

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ANALYTICS | CASE STUDY

Formulate a Predictive Admissions Strategy

COMPANY OVERVIEW


A leading New England technical institute that looking for a way to refine its admissions process.

BUSINESS NEED


The college was struggling to admit the right students to their academic programs – specifically, students who would continue at the school beyond their freshman year. The school needed a way of evaluating prospective students’ likelihood of success that was integrated into the admissions process and provided actionable recommendations admissions officers could use.

SOLUTION


The client’s leadership team brought in Ironside data scientists to help realize the full potential of their data by doing the following:


Plan an Approach


  • Collect necessary historical and current data from relevant departments and cleanse it

  • Evaluate predictive approaches and choose the most effective model

  • Feed predictive results into BI ecosystem to allow visually rich report displays and comparison with actuals

  • Score prospective students dynamically as information comes in to direct admissions decisions


Prepare the Data


  • Get individualized scores for each prospective student by preparing and transforming their historical admissions data to work in a predictive model

  • Select meaningful categories of information and merge data source formats from different departments into a standard presentation


Test and Integrate Models


  • Isolate useful data sources and build a clear project roadmap

  • Prepare data sets for predictive modeling, ensuring quality and standardizing format

  • Test modeling approaches and integrate best results into the BI workflow

  • Work prospective student scores into BI reports for easy comparison and consumption

  • Automate score updates to provide admissions with the most current model results in real time

  • Create easy to interpret recommendations individualized to each candidate to allow quick action


Inform Action


  • Output a score alongside current reporting numbers

  • Translate projections into usable recommendations for admissions counselors

  • Allow for full collaboration and rapid response across all functions related to admissions, such as financial aid and business offices, optimizing planning

  • Boost flexibility, allowing instant adjustments to the database to reflect predictions


KEYS TO SUCCESS


The client achieved successes through predictive admissions that enabled them to:

  • Connect data resources from multiple departments to allow a full view of admissions variables

  • Provide easy access to incentivization recommendations for desirable prospects

  • Unify BI actuals with predictive admissions strategy for comparisons and constant refinement

  • Deliver real-time recommendations empowering admissions counselors in a timely fashion

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ANALYTICS | CASE STUDY

Maximize Efficiency for Utilities Demand Forecasting

COMPANY OVERVIEW


A natural gas and electricity utilities provider that serves nearly 4 million customers and invests over $1 billion in capital each year to improve service.

BUSINESS NEED


The company was struggling to accurately anticipate how much demand different areas of their network would have. This led to supply problems and scrambles to fill service gaps. They needed a predictive modeling platform that could automate processes and maximize efficiency by allowing accurate, proactive supply adjustment.

SOLUTION


The client’s leadership team brought Ironside on as an expert resource to help with the following:


Discover Data Resources


  • Assess existing data quality and refine it for use in new predictive models

  • Run prepared data against regression and time series analysis algorithms

  • Implement a single platform for predictive analytics using SPSS Modeler and C&DS

  • Connect demand forecasts to the company’s energy management system


Prepare the Data


  • Leverage skills of analysts and Ironside experts to clean, transform and impute demand data from over 40 different geographic points of delivery

  • Enrich the core facts with geographic and weather data to account for the factors affecting utilities usage in each area


Develop & Test the Model


  • Refine and consolidate data from all points of delivery

  • Migrate to a more extensible and flexible predictive analytics solution

  • Test multiple predictive modeling approaches to find the most relevant

  • Reduce the need for manual rework of results through standard workflows

  • Automate connections between predictive analytics tools and other analytics assets/operational systems

  • Mentor internal resources to show future possibilities for predictive analytics and build comfort with the platform


Deploy to the Organization


  • Integrate the projections with their BI platform to compare against actuals

  • Use each point of delivery’s forecasts to trigger automated alerts in the energy management system upon generation

  • Allow proactive energy supply adjustments in different geographies based on predictive trends


KEYS TO SUCCESS


The demand forecasting project enabled the client to:

  • Increase data richness to generate more accurate and flexible predictive modeling results

  • Boost visibility and actionability of demand forecasts through energy management system integration

  • Empower internal analyst resources through a formal platform and hands-on mentorship

  • Implement a flexible SPSS predictive analytics platform able to extend to additional use cases

About IBM

IBM is a leading cloud platform and cognitive solutions company. Leverage IBM’s Analytics portfolio of high-value products and solutions to turn the company you’re running today into the company you’re building for tomorrow.

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border_color='' radius='10px,10px,0,' background_color='#ffffff' src='' attachment='' attachment_size='' background_position='top left' background_repeat='no-repeat' animation='' mobile_breaking='' mobile_display='' custom_class='' av_uid='av-320m815'] [av_heading tag='h2' padding='10' heading='Become one of our successful clients.' color='' style='blockquote modern-quote modern-centered' custom_font='' size='' subheading_active='' subheading_size='15' custom_class='' admin_preview_bg='' av-desktop-hide='' av-medium-hide='' av-small-hide='' av-mini-hide='' av-medium-font-size-title='' av-small-font-size-title='' av-mini-font-size-title='' av-medium-font-size='' av-small-font-size='' av-mini-font-size='' av_uid='av-2os9ax5'][/av_heading] [/av_one_full][av_one_fourth first min_height='av-equal-height-column' vertical_alignment='av-align-top' space='no_margin' margin='0px' margin_sync='true' padding='0px,0px,50px,0px' border='' border_color='' radius='0px,0px,0px,10px' 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[/av_one_half][av_one_fourth min_height='' vertical_alignment='av-align-top' space='' margin='0px' margin_sync='true' padding='0px' padding_sync='true' border='' border_color='' radius='0px,0px,10px,0px' background_color='#ffffff' src='' attachment='' attachment_size='' background_position='top left' background_repeat='no-repeat' animation='' mobile_breaking='' mobile_display='' custom_class='' av_uid='av-2cum7d'] [/av_one_fourth] [/av_section]

ANALYTICS | CASE STUDY

Enable Self-Service Analytics

COMPANY OVERVIEW


Major regional bank and financial services provider that’s been in business for almost 200 years, with over $140 billion in total assets and more than 1,000 branches.

BUSINESS NEED


The company was experiencing consistency issues with reporting. IT believed the answer was to enable users to be more self-sufficient, but disparate results from multiple sources were causing confusion. The bank needed a centralized self-service solution to help users get reliable business information independent of IT, and in the process, build on the bank’s solid enterprise analytics foundation.

SOLUTION


The Ironside Business Intelligence team’s approach is based on balance. It is equal parts governance and flexibility, with an emphasis on delivering BI solutions that enable both scheduled reporting results and spontaneous investigations into new business questions. The solution consists of:


Focus on Data


  • Focus on risk-related information, using a performance and balance metrics delivered in multiple business contexts through a multidimensional cube for easy viewing

  • Determine how to present data from the cube to increase user adoption, consistency of experience and ability of users to test scenarios and get meaningful results on their own


Experience Design


  • Create a UX in which user needs, technology and business goals smoothly intersect

  • Set up a self-service extracts capability for non-technical users who need access to risk data contained in their cube, so they can pull information in real time simply by selecting a business dimension


Portal Creation


  • Create an easy-to-use, unified portal through which users can access multiple, useful analytics functions

  • Set up a simplified interface containing preconfigured reporting options designed to answer common questions about risk data occurring across different departments


Benchmarking


  • Establish a set of limits and benchmarks to determine risks of different portfolios

  • Build a limits management solution into the portal, letting users add different limits and benchmarks to their reports, while making quick performance comparisons between different areas


KEYS TO SUCCESS


This Self-Service Analytics project allowed the company to:

  • Increase user adoption of analytics resources, and establish user trust in data

  • Enable consistent reporting based on verified data across different business groups

  • Implement a simple yet flexible self-service model that’s repeatable across departments

  • Provide measurable corporate benchmarks to focus user efforts on company goals

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ANALYTICS | CASE STUDY

Transition to Bimodal BI Migration in Health Insurance

COMPANY OVERVIEW


Fortune 500 health insurance company with over $30 billion in revenue, more than 30,000 employees worldwide, and over 85 million customer relationships.

BUSINESS NEED


The company’s business users were hungry for analytics results, but weren’t getting them in time due to an aging Hyperion reporting system. IT regularly ran several hundred tabular-style reports on request, but users had to export and manually manipulate them to get insights. The need, therefore, was to find a way to get fresh data to business users as questions arose, while also maintaining regular reporting on core metrics.

SOLUTION


Ironside Analytics Advisory is all about developing and implementing a practical strategy, which involved helping the client do the following:


Evaluate the Options


  • Assess current analytics strategy in light of new goal of evolving analytics delivery

  • Run an assessment workshop to determine how to best capture and meet users’ needs

  • Explore two delivery directions—a report redesign or migration of key reports to Tableau or Cognos


Build a Migration Plan


  • Pick the use case that fits the desired bimodal approach and map the transition away from the current solution

  • Build strategy based on assessment findings and user goals, including establishing which reports/SQR extracts to migrate, and setting a timeline for project milestones


Perform the Migration


  • Enlist Ironside Solution Center consultants to move through the many reports isolated for transfer, determine the changes required to migrate them to Tableau or Cognos, and migrate each report

  • Work with client’s internal team to rapidly transition and test the assets


Foster User Adoption


  • Provide Tableau training with Ironside instructors to promote use of self-service platform

  • Transition migrated tabular data into visualizations for new insights

  • Keep admin control with IT through central Tableau server, plus regular Cognos KPI reporting


KEYS TO SUCCESS


The bimodal analytics migration allowed stakeholders to:

  • Answer their own questions through a central, pain-free, data discovery oriented BI platform

  • Make decisions more efficiently, without affecting IT governance or the reports essential for performance checks

  • Create a central repository for storing/sharing analysis results, allowing reuse and IT oversight

  • Have an enhanced user experience through intuitive controls and easy visualization generation

  • Increase speed to action, removing obstacles between business stakeholders and data

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ANALYTICS | CASE STUDY

Predictive Policing Success for Manchester PD

COMPANY OVERVIEW


Manchester is located in southern New Hampshire and has a population of approximately 111,196 residents. The city had a high crime rate due to the heroin crisis they were facing and the Police Chief wanted to find a way to reduce the amount of crime that takes place.

BUSINESS NEED


Manchester PD was looking for a solution to help them understand where and when incidents occur, improve crime prevention by better understanding the drivers of the incident and improving forecasting for future events, and receive hands-on mentorship to make crime analysts self-sufficient with modeling technology. The ultimate goal being to reduce crime throughout the city.

SOLUTION


Predictive analytics strategies and technologies can make a huge difference in crime prevention and public safety efforts, improving people’s well being in communities of all sizes. The Manchester Police Department wanted to make this kind of impact in their city, and chose to bring in Ironside’s Data Science team to implement Ironside’s Predictive Policing platform to achieve their crime reduction goals.


Define the Plan


  • Implement an SPSS modeler that would complement their fledgling hot spot policing system

  • Develop predictive models

  • Mentor the department’s crime analysts throughout the process so they would be fully enabled on the product and with the assets created


Design the Model


  • Worked with the department’s internal resources to design a model matching the project’s goals

  • Provided instruction focused on the formal methodologies for data science and the data preparation, modeling, evaluation, and deployment activities needed to meet those standards

  • Equipped the police department to run the predictive hot spots module, test hypotheses, and design further crime reduction strategies


Develop the Model


  • Developed the model based on well-established theories on criminal behavior that have long been cornerstones of the law enforcement community

  • Provided the intelligence necessary to put officers in key positions during the times when they can have the most impact in order to stop crimes in progress and prevent/discourage them from happening entirely


Generate Impactful Results


  • Yielded geographically relevant forecasts that give officers the jump on crime and allow them to target precise areas of their jurisdiction

  • Crime analysts on the force are diving into new possibilities with SPSS Modeler, generating hypotheses and driving toward further crime prevention models


KEYS TO SUCCESS


Ironside’s Predictive Policing model allowed Manchester PD to see significant benefits, including:

  • Provided forecasts for each 8-hour officer shift, allowing appropriate resource allocation and daily updates to the hot spot maps so that forecasts are continually accurate to 7 days out

  • Set up workflows for pushing hot spot maps out to the mobile data terminals (MDTs) in Manchester PD cruisers so officers can see where hot spots are predicted on the go in near real-time

  • Created 60% crime prediction accuracy within a 500-foot radius of where the crime was forecast to occur

  • Produced a total crime reduction impact of 28% within the first 5 weeks of deployment

  • Reduced crime to 24% for robberies, 13% for burglaries, and 34% for thefts from motor vehicles within the first 10 weeks of deployment

“Initially, what the Manchester Police Department did was use the information that we had to try to figure out where to put our personnel to prevent crimes. Over a 3-month period, we did this and saw amazing results when it came to armed robberies of corner stores. It was actually the springboard into ‘there’s got to be more that we can even do with this.’ If we can do it on an elementary level, imagine what we could do on a level where we actually have algorithms and a system to feed all of our information in to get out the best places to put our people.”

“The technology is extremely easy. We’ve all had laptops in our cruisers for years now. It’s not new technology, it’s just another window that we’re opening up. It’s a map of the city that we’re all familiar with. I’ve had people from neighborhoods where their cars have all been broken into come out and thank me at 2:00 AM because they see the cruiser driving around and they’re saying it’s an absolute deterrent. They’re saying that their cars haven’t been broken into in weeks, so you’ve got that immediate effect that it’s there.”

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