Uploaded byBrendan Tierney
Predictive analytics: Mining gold and creating valuable product
The document discusses the importance and methodologies of predictive analytics in various industries, emphasizing the need for clear business objectives and the relevance of data in analytics projects. It illustrates real-world applications, such as fraud detection, customer churn management, and student retention strategies, highlighting the financial impact of effectively utilizing data analytics. Additionally, it conveys that advanced analytics can be performed without big data, encouraging businesses to leverage existing historical data for insights.
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Predictive analytics: Mining gold and creating valuable product
- 1. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Predic)ve Analy)cs in Oracle: Mining the Gold & Crea)ng Valuable Products Brendan Tierney
- 2. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com § Data Warehousing since 1997 § Data Mining since 1998 § Analy)cs since 1993
- 3. Big Data – Example Applica)ons Not all of these are using Hadoop or require Hadoop or ….. !
- 4. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com How to approach a Data Science Project Find me something interes.ng in my data is a ques.on from hell. Analy.cs should be guided by business goals Focus hard on Business Ques.on (and the relevant variables) that captures the essence of the ques.on. Before you can measure something you really need to lay down a very concrete defini.on of what you’re measuring
- 5. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Be Specific in Problem Statement Poorly Defined Be-er Data Mining Technique Predict employees that leave • Based on past employees that voluntarily leV: • Create New AWribute EmplTurnover à O/1 Predict customers that churn • Based on past customers that have churned: • Create New AWribute Churn ! YES/NO Target “best” customers • Recency, Frequency Monetary (RFM) Analysis • Specific Dollar Amount over Time Window: • Who has spent $500+ in most recent 18 months How can I make more $$? • What helps me sell soV drinks & coffee? Which customers are likely to buy? • How much is each customer likely to spend? Who are my “best customers”? • What descrip)ve “rules” describe “best customers”? How can I combat fraud? • Which transac)ons are the most anomalous? • Then roll-‐up to physician, claimant, employee, etc. How are you going to measure the results? What are the evalua)on metrics? How are you going to use models & results?
- 6. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Be Specific in Problem Statement Poorly Defined Be-er Data Mining Technique Predict employees that leave • Based on past employees that voluntarily leV: • Create New AWribute EmplTurnover à O/1 Predict customers that churn • Based on past customers that have churned: • Create New AWribute Churn ! YES/NO Target “best” customers • Recency, Frequency Monetary (RFM) Analysis • Specific Dollar Amount over Time Window: • Who has spent $500+ in most recent 18 months How can I make more $$? • What helps me sell soV drinks & coffee? Which customers are likely to buy? • How much is each customer likely to spend? Who are my “best customers”? • What descrip)ve “rules” describe “best customers”? How can I combat fraud? • Which transac)ons are the most anomalous? • Then roll-‐up to physician, claimant, employee, etc. How are you going to measure the results? What are the evalua)on metrics? I’ve got all this data; can you “mine” it and find useful insights?
- 7. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Define the Ques)on Why is this Topic Important (any sub topics/areas) What has been done before What are the evalua)on measures? What is the relevant data. (what data is accessible and what is not) Define techniques to use. How are you going to use the results. Out of lab and into Architecture How Frequently are you going to revisit/update Predic)ve Analy)cs Requirements Gathering (6-‐10 day exercise)
- 8. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Define the Ques)on Why is this Topic Important (any sub topics/areas) What has been done before What are the evalua)on measures? What is the relevant data. (what data is accessible and what is not) Define techniques to use. How are you going to use the results. Out of lab and into Architecture How Frequently are you going to revisit/update Predic)ve Analy)cs Requirements Gathering (6-‐10 day exercise)
- 9. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Define what data is relevant to the Ques)on/Specific Problem – What data is easily available now – What data is not easily available now – What data do you not have, not captured etc. What is the relevant data. (what data is accessible and what is not) Your Data hWps://hbr.org/2016/11/you-‐dont-‐need-‐big-‐data-‐you-‐need-‐the-‐right-‐data
- 10. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com All of the following are Real projects § on Real data § using Real products § on Real business problems § Are full cycle implementa)ons & in produc)on Most Data Science / Predic)ve Analy)cs stories you hear about are very limited § Many only exist on paper, in a test lab/environment, on a presenta)on, etc
- 11. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Fraud Detec)on § I’m not allowed to talk about what I did § But
- 12. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Insurance Fraud Insurers discovered a total 118,500 false claims were made, equivalent to 2,279 a week. § Using OAA to assess each Claim as it is received – Iden)fy possibility of it being a Claim – Iden)fy possible Claim Amount – Measure of Risk Exposure : Used to manage work flow and priority § Works in conjunc)on with other Fraud preven)on measures § Supports Claim Risk Exposure measures – Various regulatory, group and share holder requirements on Risk Exposure
- 13. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Retail Banking Fraud § Using OAA being used to monitor retail banking transac)ons – Iden)fy unusual paWerns in transac)ons – Iden)fy unusual paWerns on accounts – Iden)fy unusual paWerns between branches – Iden)fy unusual Staff behavior § Working with exis)ng Freud Detec)on methods to give – Addi)onal insights – Near real-‐)me monitoring – Working within their exis)ng Informa)on Architecture § Near Real-‐)me Fraud Preven)on measures – Previous/Current Fraud inves)ga)on is next day or next week – Now can iden)fy intra-‐day, Fraud teams acts quicker, etc
- 14. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com An Post § An Post has made innova)ve use of Oracle’s business intelligence and data warehousing systems to deliver efficiencies across a range of areas, including HR, mail processing and quality of service. § Oracle’s business intelligence suite founda)on Edi)on, which provides same-‐day view of cash flow through an easy-‐to-‐use dashboard • Near Real-‐)me Fraud Preven)on measures • Previous/Current Fraud inves)ga)on is next day or next week • Now can iden)fy intra-‐day, Fraud teams acts quicker, etc • Iden)fy unusual paWerns in transac)ons • Iden)fy unusual paWerns on accounts • Iden)fy unusual paWerns between branches • Iden)fy unusual Staff behavior
- 15. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Higher Educa)on Example : Higher EducaNon Student Reten)on Funding Model of Universi)es in the UK How can we maximise our Student Reten)on and increase our funding Can we manage our Student selec)on process beWer?
- 16. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com OBIEE Dashboard E-Learner Female Science“unknown” Poor data quality
- 17. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com What about the Money ££££ What was the original problem? We want to reduce the number of students who withdraw early from their courses? (student churn) and increase our funding (revenue) Did we achieve this ? Typical student churn of 2,300 per year x £10K = (£23,000,000) 82% success = £18,860,000 of poten.al revenue gain Implemented using a mixture of Provide beTer/addi.onal student support Be more selec.ve with making offers Restructure Courses Look at how courses are adver.sed and entry requirements You can imagine how much this would save across all the 133 Universi)es in UK > £1b
- 18. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com HCM Analy)cs § An Oxford economics report (Feb 2014) es)mates the average cost per employee is £30,614: – Lost “cost of lost output” whilst replacement employees get up to speed – The “logical lost” of recrui)ng and absorbing a new worker § Average employee turnover rate in the UK is approx 15%
- 19. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com HCM Analy)cs § Oracle HCM
- 20. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com HCM Analy)cs § Major World-‐Wide Financial Ins)tu)on § >300K employees § Employee Churn. It is all about the money? or promo)ons? Right? § Not what we discovered – Employee engagement, training, support, regulatory requirements, staff requirements, etc. – 93% accuracy – 68% of these had monetary reward indicators Some)mes we discover trends that are not expected. Un-‐biased trends Can be difficult to accept
- 21. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Merchandising Management of Outlets § Supply Chain Management § Ensuring your products are on the shelves in the outlets § Limited Staff to visit outlet : Who should they be targe)ng? § Learn from the Past § >85% accuracy 11th Feb, 2015 Out of stock considered supply chain problem. Problem is 'not on shelf'. 'Out back' no good, according to consumers @dunnhumby #BASummit2015 Holland Belgium Spain Eastern Canada
- 22. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Customer Churn Management § Mobile Phone Companies § Why is Customer Churn management is important? – It costs a lot more to recruit a new customer than to keep an exis)ng one. – You don not want to target all possible churner. – High value customers : How to you determine high value? – 69%-‐75% accuracy § Social Network Analysis – How big is your Social Network – How valuable is your Social Network
- 23. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com § Tracking customer Sen)ment – Call Centre & Customer reten)on – Part of Customer Churn management – Combined with other Predic)ve Analy)cs methods – Ensemble Data Mining/Predic)ve Analy)cs § Can we predict what )meframe they might churn? – Is this Big Data? • Most of this processing is done on a Laptop/Desktop Customer Sen)ment
- 24. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com
- 25. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com
- 26. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Is Advanced Analy)cs for you? § If you have Data then YES § You don’t need to have Big Data to do Advanced Analy)cs § You don’t need to hire PhDs or Data Scien)sts § You can do Advanced Analy)cs on the the data you have. § Do you have any historical data? § Use what data you have available – As new data becomes available you can add these in
- 27. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Everyone only talks up to this point Nobody talks about Deployment Or what happens aVer Deployment
- 28. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Create Valuate Products ✓ ✗
- 29. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com brendan.)erney@oraly)cs.com @brendan)erney www.oraly)cs.com ie.linkedin.com/in/brendan)erney
- 30. www.oraly)cs.com t : @brendan)erney e : brendan.)erney@oraly)cs.com Word Cloud of the Oracle Advanced Analy)cs web-‐pages hWp://www.oraly)cs.com/2015/01/crea)ng-‐word-‐cloud-‐of-‐oracle-‐oaa.html