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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
  • 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

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

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
  • 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
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