Background

For each type of analysis think about:

• What problem does it solve, and for whom?
• How is it being solved today?
• How can it beneficially affect business?
• What are the data inputs and where do they come from?
• What are the outputs and how are they consumed- (online algorithm, a 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
  • competitions :
• 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
  • competitions: Springleaf Marketing Response (evaluation: area under the ROC curve )
• Channel optimization
  • what is the optimal way to reach a customer with certain 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
  • competitions : Acquire Valued Shoppers Challenge (evaluation: area under the ROC curve) Predicting Red Hat Business Value (evaluation: area under the ROC curve)
• Adwords optimization and ad buying
  • calculating the right price for different keywords/ad slots
• Target market
  • Understanding the target helps you determine exactly what your products or services will be, and what kind of customer service tactics work best
  • usage: static report
  • competitions: TalkingData Mobile User Demographics (evaluation: multi-class logarithmic loss)

Sales

• Lead prioritization
  • What is a given lead’s likelihood of closing
  • revenue impact: supports growth
  • usage: online algorithm and static report
  • competition: Predicting Red Hat Business Value (evaluation: area under the ROC curve)
• Demand forecasting
  • competitions: Rossmann Store Sales (evaluation: Root Mean Square Percentage Error (RMSPE) )

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

Operation

• Detecting employee
  • tracking work hours or detecting employee thieves
  • competitions: (related)State Farm Distracted Driver Detection (evaluation: multi-class logarithmic loss)
    

    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
  • Site Selection in the Restaurant Industry is Widely Performed via Pitney Bowes AnySite
• 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
  • http://www.analyticbridge.com/xn/detail/2004291:Comment:219197
• 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  Case Study on Manufacturing
• 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

Utilities

• Optimise Distribution Network Cost Effectiveness (balance Capital 7 Operating Expenditure)
• Predict Commodity Requirements

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