Category: Research

Personality Traits?

paschmann

Did you know that heredity is the most dominant factor in your personality? An intriguing example of this is the “Jim Twins”:

https://mctfr.psych.umn.edu/research/UM%20research.html

One example of the amazing similarity of twins reared apart is the so-called “Jim twins”. These twins were adopted at the age of four weeks. Both of the adopting couples, unknown to each other, named their son James. Upon reunion of the twins when they were 39 years old, Jim and Jim have learned that:

  • Both twins are married to women named Betty and divorced from women named Linda.
  • One has named his first son James Alan while the other named his first son James Allan.
  • Both twins have an adopted brother whose name is Larry.
  • Both named their pet dog “Toy.”
  • Both had some law-enforcement training and had been a part-time deputy sheriff in Ohio.
  • Each did poorly in spelling and well in math.
  • Each did carpentry, mechanical drawing, and block lettering.
  • Each vacation in Florida in the same three-block-long beach area.
  • Both twins began suffering from tension headaches at eighteen, gained ten pounds at the same time, and are six feet tall and 180 pounds.
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    Hacking SAP HANA Websockets

    paschmann

    Disclaimer: This is not a production or documented feature – its also more of a hijack than a hack  🙂 

    I have been hoping for the inclusion of Websocket support on the HANA DB platform for a while now, and I was a little disappointed it was not packaged in the SPS08 release. My goal when building apps (or products) is to make use of the core platform its running on as much as possible, I firmly believe that when convincing an IT department, or company, to implement a product or app, the first question is: “How much infrastructure does this need?”. This can often be a deal breaker and why I am such a big proponent of the HANA’s DB + XS App Server integration – it consolidates the requirements into a single investment. Having a Websocket technology built directly in XS can be an additional selling point which developers are starting to expect these days. read more

    SAP Netweaver Gateway – A product powering innovation at SAP

    paschmann

    While SAP Netweaver Gateway is a product not often discussed at keynotes, events and in the media, it is a integral part of the innovative products being launched at SAP and is core component of SAP Fiori. If you are interested in taking advantage of some of the new free Fiori applications to drive value with your ERP investment, understanding SAP Netweaver Gateway is not optional, but rather a requirement.

    In a summary, Netweaver Gateway is a ABAP developed add-on powering applications like SAP Fiori, SAP Mobility Platform (SMP), and even Duet Enterprise. This small, but very important component gives your company the ability to turn your monolithic, big and heavy backend systems into lightweight, simple, consumable web services. read more

    Practical Big Data Use Cases

    paschmann

    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 read more