Background
Source:
https://www.kaggle.com/wiki/DataScienceUseCases
For each type of analysis think about:
- What problem does it solve and for who?
- How is it being solved today?
- What are the data inputs and where do they come from?
- What are the outputs and how are they consumed- (online algo, static report, etc)
- Is this a revenue leakage (“saves us money”) or a revenue growth (“makes us money”) problem?
Use Cases By Function
Marketing
- Predicting Lifetime Value (LTV)
- what for: if you can predict the characteristics of high LTV customers, this supports customer segmentation, identifies upsell opportunties and supports other marketing initiatives
- usage: can be both an online algorithm and a static report showing the characteristics of high LTV customers
- Wallet share estimation
- working out the proportion of a customer’s spend in a category accrues to a company allows that company to identify upsell and cross-sell opportunities
- usage: can be both an online algorithm and a static report showing the characteristics of low wallet share customers
- Churn
- working out the characteristics of churners allows a company to product adjustments and an online algorithm allows them to reach out to churners
- usage: can be both an online algorithm and a statistic report showing the characteristics of likely churners
- Customer segmentation
- If you can understand qualitatively different customer groups, then we can give them different treatments (perhaps even by different groups in the company). Answers questions like: what makes people buy, stop buying etc
- usage: static report
- Product mix
- What mix of products offers the lowest churn? eg. Giving a combined policy discount for home + auto = low churn
- usage: online algorithm and static report
- Cross selling/Recommendation algorithms/
- Given a customer’s past browsing history, purchase history and other characteristics, what are they likely to want to purchase in the future?
- usage: online algorithm
- Up selling
- Given a customer’s characteristics, what is the likelihood that they’ll upgrade in the future?
- usage: online algorithm and static report
- Channel optimization
- what is the optimal way to reach a customer with cetain characteristics?
- usage: online algorithm and static report
Discount targeting – What is the probability of inducing the desired behavior with a discount – usage: online algorithm and static report
- Reactivation likelihood
- What is the reactivation likelihood for a given customer
- usage: online algorithm and static report
- Adwords optimization and ad buying
- calculating the right price for different keywords/ad slots
Sales
- Lead prioritization
- What is a given lead’s likelihood of closing
- revenue impact: supports growth
- usage: online algorithm and static report
- Demand forecasting
Logistics
- Demand forecasting
- How many of what thing do you need and where will we need them? (Enables lean inventory and prevents out of stock situations.)
- revenue impact: supports growth and militates against revenue leakage
- usage: online algorithm and static report
Risk
- Credit risk
- Treasury or currency risk
- How much capital do we need on hand to meet these requirements?
- Fraud detection
- predicting whether or not a transaction should be blocked because it involves some kind of fraud (eg credit card fraud)
- Accounts Payable Recovery
- Predicting the probably a liability can be recovered given the characteristics of the borrower and the loan
- Anti-money laundering
- Using machine learning and fuzzy matching to detect transactions that contradict AML legislation (such as the OFAC list)
Customer support
- Call centers
- Call routing (ie determining wait times) based on caller id history, time of day, call volumes, products owned, churn risk, LTV, etc.
- Call center message optimization
- Putting the right data on the operator’s screen
- Call center volume forecasting
- predicting call volume for the purposes of staff rostering
Human Resources
- Resume screening
- scores resumes based on the outcomes of past job interviews and hires
- Employee churn
- predicts which employees are most likely to leave
- Training recommendation
- recommends specific training based of performance review data
- Talent management
- looking at objective measures of employee success
Use Cases By Vertical
Healthcare
- Claims review prioritization
- payers picking which claims should be reviewed by manual auditors
- Medicare/medicaid fraud
- Tackled at the claims processors, EDS is the biggest & uses proprietary tech
- Medical resources allocation
- Hospital operations management
- Optimize/predict operating theatre & bed occupancy based on initial patient visits
- Alerting and diagnostics from real-time patient data
- Embedded devices (productized algos)
- Exogenous data from devices to create diagnostic reports for doctors
- Prescription compliance
- Predicting who won’t comply with their prescriptions
- Physician attrition
- Hospitals want to retain Drs who have admitting privileges in multiple hospitals
- Survival analysis
- Analyse survival statistics for different patient attributes (age, blood type, gender, etc) and treatments
- Medication (dosage) effectiveness
- Analyse effects of admitting different types and dosage of medication for a disease
- Readmission risk
- Predict risk of re-admittance based on patient attributes, medical history, diagnose & treatment
Consumer Financial
- Credit card fraud
- Banks need to prevent, and vendors need to prevent
Retail (FMCG – Fast-moving consumer goods)
- Pricing
- Optimize per time period, per item, per store
- Was dominated by Retek, but got purchased by Oracle in 2005. Now Oracle Retail.
- JDA is also a player (supply chain software)
- Location of new stores
- Pioneerd by Tesco
- Dominated by Buxton
- Product layout in stores
- This is called “plan-o-gramming”
- Merchandizing
- when to start stocking & discontinuing product lines
- Inventory Management (how many units)
- In particular, perishable goods
- Shrinkage analytics
- Theft analytics/prevention (http://www.internetretailer.com/2004/12/17/retailers-cutting-inventory-shrink-with-spss-predictive-analytic)
- Warranty Analytics
- Rates of failure for different components And what are the drivers or parts?
- What types of customers buying what types of products are likely to actually redeem a warranty?
- Market Basket Analysis
- Cannibalization Analysis
- Next Best Offer Analysis
- In store traffic patterns (fairly virgin territory)
Insurance
- Claims prediction
- Might have telemetry data
- Claims handling (accept/deny/audit), managing repairer network (auto body, doctors)
- Price sensitivity
- Investments
- Agent & branch performance
- DM, product mix
Construction
- Contractor performance
- Identifying contractors who are regularly involved in poor performing products
- Design issue prediction
- Predicting that a construction project is likely to have issues as early as possible
Life Sciences
- Identifying biomarkers for boxed warnings on marketed products
- Drug/chemical discovery & analysis
- Crunching study results
- Identifying negative responses (monitor social networks for early problems with drugs)
- Diagnostic test development
- Hardware devices
- Software
- Diagnostic targeting (CRM)
- Predicting drug demand in different geographies for different products
- Predicting prescription adherence with different approaches to reminding patients
- Putative safety signals
- Social media marketing on competitors, patient perceptions, KOL feedback
- Image analysis or GCMS analysis in a high throughput manner
- Analysis of clinical outcomes to adapt clinical trial design
- COGS optimization
- Leveraging molecule database with metabolic stability data to elucidate new stable structures
Hospitality/Service
- Inventory management/dynamic pricing
- Promos/upgrades/offers
- Table management & reservations
- Workforce management (also applies to lots of verticals)
Electrical grid distribution
- Keep AC frequency as constant as possible
- Seems like a very “online” algorithm
Manufacturing
- Sensor data to look at failures
- Quality management
- Identifying out-of-bounds manufacturing Visual inspection/computer vision
- Optimal run speeds
- Demand forecasting/inventory management
- Warranty/pricing
Travel
- Aircraft scheduling
- Seat mgmt, gate mgmt
- Air crew scheduling
- Dynamic pricing
- Customer complain resolution (give points in exchange)
- Call center stuff
- Maintenance optimization
- Tourism forecasting
Agriculture
- Yield management (taking sensor data on soil quality – common in newer John Deere et al truck models and determining what seed varieties, seed spacing to use etc
Mall Operators
- Predicting tenants capacity to pay based on their sales figures, their industry
- Predicting the best tenant for an open vacancy to maximise over all sales at a mall
Education
Automated essay scoring
Other
- Sentiment analysis
- Loyalty programs
- Sensor data
- Alerting
- What’s going to fail?
- De duplication
- Procurement
Use Cases That Need Fleshing Out
Procurement
- Negotiation & vendor selection
- Are we buying from the best producer
Marketing
- Direct Marketing
- Response rates
- Segmentations for mailings
- Reactivation likelihood
- RFM
- Discount targeting
- FinServ
- Phone marketing Generally as a follow-up to a DM or a churn predictor
- Email Marketing
- Offline
- Call to action w/ unique promotion
- Why are people responding- How do I adjust my buy (where, when, how)?
- “I’m sure we are wasting half our money here, but the problem is we don’t know which ad”
- Media Mix Optimization
- Kantar Group and Nielson are dominant
- Hard part of this is getting to the data (good samples & response vars)
Healthcare
- CRM & utilization optimization
- Claims coding
- Forumlary determination and pricing
- How do I get you to use my card for auto-pay? Paypal? etc. Unsolved.
- Finance
- Risk analysis
- Automating Excel stuff/summary reports