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Below are the Use Cases across industries, by function:

  1. Digital Marketing: Predict virality, Influencer Marketing,PR & SEO(extract company mentions,Extract backlinks,Refine results by traffic rank,sentiment, spam score, thread participant count, or social filters), Targeted Advertising and re-targeting, web data coverage,Brand monitoring systems, reputation tracking platforms and Social Media Data Mining trends.
  2. Cybersecurity
  3. Recommender Systems
  4. Image Recognition
  5. Speech Recognition
  6. Pricing Comparison
  7. Dynamic Pricing
  8. Route Planning
  9. Fraud and Risk Detection
  10.  Predictive car maintenance
  11.  Server monitoring
  12. Aggregator systems – News, Poll, Reviews, Search, Social media, Video, Blogs, etc 
  13. Text categorization, summarization, topic detection
  14. Predicting adherence, risk default and churn.
  15. Market basket analysis
  16. Segmentation and Clustering
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. Up selling – Given a customer’s characteristics, what is the likelihood that they’ll upgrade in the future? usage: online algorithm and static report
  23. Channel optimization – what is the optimal way to reach a customer with cetain characteristics? usage: online algorithm and static report
  24. Discount targeting – What is the probability of inducing the desired behavior with a discount – usage: online algorithm and static report
  25. Reactivation likelihood –  What is the reactivation likelihood for a given customer , usage: online algorithm and static report
  26. Adwords optimization and ad buying  – calculating the right price for different keywords/ad slots
  27. Sales – Lead prioritization – What is a given lead’s likelihood of closing revenue impact: supports growth usage: online algorithm and static report
  28. 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
  29. Risk -Credit risk, Treasury or currency risk, How much capital do we need on hand to meet these requirements?
  30. Fraud detection -predicting whether or not a transaction should be blocked because it involves some kind of fraud (eg credit card fraud)
  31. Accounts Payable Recovery – Predicting the probably a liability can be recovered given the characteristics of the borrower and the loanAnti-money laundering – Using machine learning and fuzzy matching to detect transactions that contradict AML legislation (such as the OFAC list)
  32. Anti-money laundering : Using machine learning and fuzzy matching to detect transactions that contradict AML legislation (such as the OFAC list)
  33. 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.
  34. Revenue impact: supports growth and militates against revenue leakage usage: online algorithm and static report
  35. 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 Industries Vertical :

1.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

2. 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

3. 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

4.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

5.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

6.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

7. 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

8.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

9.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

10 .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

11. Education

Automated essay scoring

12 . Utilities

Optimise Distribution Network Cost Effectiveness (balance Capital 7 Operating Expenditure)

Predict Commodity Requirements

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