Predictive analytics can be used in many domains to forecast future outcomes and trends based on existing data. It allows identifying at-risk customers, predicting customer lifetime value, segmenting markets, and assessing litigation risk. It also enables predictive maintenance, risk modeling, identifying emerging needs, developing innovative products and services, and simulating policies. Specific applications include predicting churn, readmission, housing hazards, bridge cable breaks, floods, food safety issues, traffic accidents, elderly care needs, child welfare risks, criminal recidivism, student dropout, and more. The document provides many examples of predictive analytics use across industries like retail, healthcare, transportation, government, and education.

1. CHATBOTS
Hoe werkt het
en wat kunnen we er mee aan?
Predictive Analytics

2. Wat is predictive analytics
• The practice of extracting information from existing data sets in order to
determine patterns and predict future outcomes and trends;
• Does not tell you what will happen in the future but forecasts what
might happen in the future with an acceptable level of reliability;
• Includes what-if scenarios and risk assessment.
https://www.webopedia.com/

4. Waar kunnen we het voor gebruiken?
• Churn Prevention
Predictive analytics help to prevent losing customers, by identifying signs of
dissatisfaction, identify those customers or customer segments that are at the
most risk for leaving. Companies can then make the necessary changes to keep
those customers happy and protect their revenue;
• Customer Lifetime Value
Identifies those customers that are going to spend the most money, in the most
consistent way and over the longest period of time. Allows companies to
increase their share of that segment of the business, and gain those customers
that will have the greatest lifetime value to your company;
• Customer Segmentation
Identifies the segments of those markets that are most receptive to what your
company offers;
• Provide an assessment of the likelihood of success in litigation
https://xmpro.com/10-predictive-analytics-use-cases-by-industry/

5. Waar kunnen we het voor gebruiken? (2)
• Predictive Maintenance
By analyzing metrics and data related to the lifecycle maintenance of technical
equipment, companies can predict both timelines for probable maintenance
events allowing them to avoid critical downtime.
• Risk Modeling
By combining predictive analytics with a risk management approach, companies
can capture and quantify risk issues, evaluate them, and decide on a course of
action to mitigate those risk factors deemed most critical.
https://xmpro.com/10-predictive-analytics-use-cases-by-industry/

6. Waar kunnen we het voor gebruiken? (3)
• Likelihood of readmission in hospital;
• Facilitate the spotting of emerging governmental and societal needs;
• Develop additional, more innovative and better products and services;
• Develop and simulate public policies and better target services;
• Discovering of stores selling bootlegged cigarettes;
• Fighting the prescription drug epidemic (New York) through detection
of the 21 pharmacies (=1%) that accounted for more than 60% of sold
Oxycodone;
https://xmpro.com/10-predictive-analytics-use-cases-by-industry/

7. Waar kunnen we het voor gebruiken? (4)
• Detection of dangerous buildings highly likely to result in firefighter
injury or death;
• Forecast urban development in specific areas for better infrastructure
investments;
• Group- and neighborhood profiling for execution of preventive policies;
• Segment tax debtors by risk and use predictive modeling for individual
escalation plans;
• Using cell phone data for predicting socio economic levels in developing
areas.
https://datasmart.ash.harvard.edu/news/article/how-can-data-and-analytics-be-used-to-enhance-city-operations-723

8. Waar kunnen we het voor gebruiken? (5)
• Enhance rat baiting (Chicago);
• Provide affordable safe housing;
• Identify homes that are most likely to still contain lead-based paint
hazards;
• Predict whether a individual strand in a bridge's cable is about to break;
• Predict flash floods.
https://xmpro.com/10-predictive-analytics-use-cases-by-industry/

9. Waar kunnen we het voor gebruiken? (6)
• Improve food inspections.
Predict ahead of time which establishments are the most likely to have food
safety issues.
• Prevent accidents.
The United Kingdom used predictive analytics to reduce traffic accidents. An
algorithm based on data collected over a decade which helped the team to
predict which type of drivers are at risk of dangerous accidents. The team
tweaked the notices sent to offenders.
• Predicting care needs of the elderly
Denmark municipalities experiments to predict when elderly citizens need
assistance. The Danish pilot project draws on personal health and assistance
history as well as semi-structured text by caretakers to predict (with an accuracy
of 80%) when a new assistance level is needed.
https://www2.deloitte.com/us/en/insights/industry/public-sector/government-trends/2020/predictive-analytics-in-government.html
Elderly & child welfare: https://www.liberalforum.eu/wp-content/uploads/2019/11/PUBLICATION_AI-in-e-governance.pdf

10. • Child welfare services
The Finnish city of Espoo examined 520,000 cases across 14 years. The AI
system identified 280 factors that influence the need for child welfare services;
• Detention or parole?
Prisons use data and detailed statistics in their predictions of which
prisoners should be allowed out on parole;
Jurisdictions use predictive models to determine who should be detained
before trial and who may be safely allowed out while awaiting trial;
• Solving criminal cases
Miami-Dade County predictive analytics is focused on solving cold cases and
catching repeat offenders. A list of potential suspects is created based on
match probability from existing data in the system. The system leverages crime
patterns and offender modus operandi information from huge volumes of
historical data
Waar kunnen we het voor gebruiken? (7)
https://publications.iadb.org/publications/english/document/Innovations-in-Public-Service-Delivery-Issue-No-4-Predictive-Analytics-Driving-Improvements-Using-Data.pdf

11. • Education
Flagging students who appear at risk of dropping out: educational institutions
can provide support for that student even before the student him or herself
realizes that he or she may be at risk.
Rich data is becoming available for use as indicators - systems can incorporate
grade data, financial aid data, and student data to build a predictive model to
allow the prioritization of interventions and the provision of additional
resources to high-risk students.
Waar kunnen we het voor gebruiken? (8)

12. Thank You!
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