20151008 REx Predictive presentation v 1 0 - distributed
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This document provides an overview of predictive modeling and how it can benefit businesses. It discusses: - The benefits of predictive modeling and the overall modeling process. - Common applications like customer intimacy, optimizing capital deployment, and detecting threats. - The modeling process including clarifying the business problem, understanding available data, developing models, and deploying results. - Examples of predictive modeling projects including customer segmentation for marketing offers, optimizing a website for visitor segments, and predicting sewer flooding risks to prioritize maintenance. - Techniques like cluster modeling, correlation analysis, and models incorporating multiple time periods are used to develop solutions. The document is intended to help business people understand how predictive
![Text enrichment
12/10/2015 37Copyright Red Olive 2015
Text data
sources
Text
enrichment
Subject
matching
Sentiment
classification
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Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap signal
but yet paying FULL monthly contract! Vodafone
sort it.
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap [----]
signal but yet paying FULL monthly contract!
Vodafone sort it.
Original Facebook Message Sentiment Amplifier
Why[WRB] not[RB] sort[VBG] your[PRP]
signal[VBP] issues [VBZ] out[IN] instead[RB]
of[IN] bringing[VBG] new[JJ]
phones[NNS]!!!![SYM] Wk[NNP] 3[CD] of[IN]
crap[NN] but[CC] yet[RB] paying[VBG]
FULL[NNP] monthly[RB] contract[NN] ![SYM]
Vodafone[NNP] sort[VBG] it[PRP] .[SYM]
Penn Treebank P.O.S. Tagger (English Messages)
sort[VBG] signal[VBP] issues [VBZ] instead[RB]
bringing[VBG] phones[NNS] Wk[NNP] 3[CD]
crap[NN] paying[VBG] monthly[RB] contract[NN]
Vodafone[NNP]
Removal of stop words and punctuation](https://image.slidesharecdn.com/d1ee25a8-f7c5-435a-8a5a-f7638446dc4e-151013204043-lva1-app6892/85/20151008-REx-Predictive-presentation-v-1-0-distributed-37-320.jpg)
![Subject matching
12/10/2015 38Copyright Red Olive 2015
Text data
sources
Text
enrichment
Subject
matching
Sentiment
classification
Information
delivery
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap signal
but yet paying FULL monthly contract! Vodafone
sort it.
Original Facebook Message
Subject Matching (Fuzzy Matching)
Why not sort your signal issues out instead of
bringing new phones out!!!! Wk 3 of crap signal
[NETWORK]but yet paying FULL monthly
contract! Vodafone sort it. [COMPLAINT]
BUSINESS TRANSACTION: Complaint
NETWORK: No Signal
PRODUCT: Samsung Galaxy S4](https://image.slidesharecdn.com/d1ee25a8-f7c5-435a-8a5a-f7638446dc4e-151013204043-lva1-app6892/85/20151008-REx-Predictive-presentation-v-1-0-distributed-38-320.jpg)
20151008 REx Predictive presentation v 1 0 - distributed
- 1. Information and Data Management What can Predictive Modelling do for your business? Jefferson Lynch, John McConnell 8th October 2015, Royal Exchange Analytics and Data Management
- 2. Intended audience and aims Who is this intended for? Business people who want to understand what practical difference predictive modelling can offer to their organisation, and what’s involved. By the time you leave you should… Understand the benefits and the overall process involved. Be familiar with some common problems where modelling is used, and some modelling approaches. Be able to assess your organisational gaps and so what help you may need. 12/10/2015 2Copyright Red Olive 2015
- 3. Why you should be interested: business context 12/10/2015 3Copyright Red Olive 2015 The business interest in using data to tackle business problems has changed: Not just structured data, reports and dashboards to guide solutions to defined performance problems… … but also discovery of new patterns in diverse data to address much bigger questions and problems.
- 4. What is predictive analytics? 12/10/2015 4Copyright Red Olive 2015 “Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behaviour patterns.” (Wikipedia.org) It can be applied to any type of unknown, whether past, present or future. The core idea is to capture relationships between predictor variables and known outcomes from past occurrences in a “model”, and then use those relationships to predict unknown outcomes. The accuracy depends greatly on the quality of both the assumptions made and the data available.
- 5. How is predictive modelling carried out and where? 12/10/2015 5Copyright Red Olive 2015 Predictive modelling environments: Our tools of choice are SPSS Modeler and Statistics, another common general platform is SAS and there are several others. Open source modelling (e.g. R) is popular but needs more expert knowledge, there’s a productivity gain from modelling software. Some areas of usage: Customer intimacy Optimise capital deployment Detect and mitigate threats Many others…
- 6. Red Olive’s framework for predictive modelling 12/10/2015 6Copyright Red Olive 2015 Illustration of modelling process Business data for analytics 1 Clarify problem, create multiple solutions 2 Work out data needed to solve the problem 4 Prepare data for solution modelling 5 Develop solution models 6 Evaluate results 7 Deploy live model 3 Source and capture rich data (Refine) (Want to re-use?)
- 7. Understanding the problem and the data Clarify the business problem Does the data support the solution? 12/10/2015 7Copyright Red Olive 2015 Business data for analytics 1 Clarify problem, create multiple solutions 2 Work out data needed to solve the problem 4 Prepare data for solution modelling 5 Develop solution models 6 Evaluate results 7 Deploy live model 3 Source and capture rich data (Refine) (Want to re-use?)
- 8. Clarify the business problem Copyright Red Olive 2015 etc… Loan applications Person OMG Compare Moneysupermarket Websites 12/10/2015 8 What’s the big idea? More into the funnel? Overall volume? An optimised mix? Higher conversion of those who are there already?
- 9. Does the data support the solution? Copyright Red Olive 2015 Loan applications Person OMG Compare Loan Application A = Agreement Go Compare No behavioural data from web analytics was available. ? In future may be able to link with other in-house data to enable e.g. loan consolidation? 12/10/2015 9 Level 1 search Level 2 search Level 3 search Moredataavailabletouse formodelling… Morelikelytoapply(andbe successful?)
- 10. Modelling business solutions Where is predictive modelling typically applied and what are the benefits? What are some of the main techniques used? 12/10/2015 10Copyright Red Olive 2015 Business data for analytics 1 Clarify problem, create multiple solutions 2 Work out data needed to solve the problem 4 Prepare data for solution modelling 5 Develop solution models 6 Evaluate results 7 Deploy live model 3 Source and capture rich data (Refine) (Want to re-use?)
- 11. CUSTOMER INTIMACY EXAMPLE: NEW BUSINESS OFFERINGS, LOANS Copyright Red Olive 201512/10/2015 11
- 12. Can we profile applicants to find interesting segments (a “segment” means a group of people with certain things in common)? Could we then target certain segments with specific offers for them? Approach: used cluster modelling to identify some potentially interesting segments 23% 77% Apply Don't apply People who progress to apply Copyright Red Olive 201512/10/2015 12
- 13. 23% 53% 0% 10% 20% 30% 40% 50% 60% All visitors Profile 1 Profile – Segment 1 • 39 or younger • In a job for over 24 months and less than 10 years • Looking for a loan term between 12 and 35 months When a visitor fitting this profile comes to the site there is a 53% chance they will make an application Do the available lenders have products that match them? Example segment 1 Copyright Red Olive 201512/10/2015 13
- 14. What skills do you need to do this? Platform or coding? Copyright Red Olive 201512/10/2015 14 As we explore we generate many models, keep only a few: Easier to manage on a platform. Platform also easier to keep track of models, data sets, parameters… Also valuable when have a team of people working together, needing co-ordination.
- 15. CUSTOMER INTIMACY EXAMPLE: DIGITAL CUSTOMER SEGMENTATION, ROYAL MAIL Copyright Red Olive 201512/10/2015 15
- 16. Behavioral data - Orders - Transactions - Payment history - Usage history Descriptive data - Attributes - Characteristics - Self-declared info - (Geo)demographics Attitudinal data - Opinions - Preferences - Needs & Desires Interaction data - E-Mail / chat transcripts - Call center notes - Web Click-streams - In person dialogues “Traditional” High-value, dynamic - source of competitive differentiation Who? What? Why?How? People/Customer data types 12/10/2015 Copyright Red Olive 2015 16 (*Source: IBM)
- 17. Modelling business solutions The client wants to understand core visitor segments: Their customer journeys Their value So the web site (and other channels) can be re- architected to better service those requirements The framework allow us to enrich the behavioural data with descriptive/attitudinal and other data In this example e-commerce data 12/10/2015 17Copyright Red Olive 2015
- 18. Why do they visit the site and what do they think of it? Who visits the site? What do they do on the site? 12/10/2015 Copyright Red Olive 2015 18 The framework in action
- 19. 12/10/2015 Copyright Red Olive 2015 19 Example segment: “Happy trackers” • Happy Trackers mainly use the site for Track and Trace and little else. • They tend to have a stronger business slant and be slightly older than the average. • They are not heavy users of the site and individual visits are relatively light and narrow. • However they are happy with what they do and they rate the site functionality the best out of all the segments.
- 20. 12/10/2015 Copyright Red Olive 2015 20 Overall segments derived 12 behavioural segments Cottage Industrialists, 2% Job Seekers, 2% Happy Trackers, 6% Price Finders, 10% Xmas Info Seekers, 4%One Hit Wonders, 56% Domestic PAFfers, 8% Regular Posters, 1% Hobbyists, 2% Virgin Posters, 2% Anxious Trackers, 3% Frequent Finders, 5%
- 21. 0 200 400 600 800 1,000 0 2 4 6 8 10 12 14 16 18 OHW Domestic PAFfers Regular posters Anxious trackers Hobbyists Frequent finders Cottage Industrialists Virgin posters Number of visits Average time on site per visit Size of bubble reflects size of segment 12/10/2015 Copyright Red Olive 2015 21 Different segments have different styles of engagement
- 22. UTILITY COMPANY EXAMPLE: OPTIMISE CAPITAL DEPLOYMENT Copyright Red Olive 201512/10/2015 22
- 23. Optimising capital: utility company example Aim: Identify from the data those business processes that most strongly influence customer satisfaction (CSAT, Net Promoter Score…). Use the results to influence decisions regarding capital investment. Approach: 1. Are the variations in CSAT over time significant? 2. Given limited resource for investigation, assess scale of opportunity in a number of process areas and focus investigation. 3. For the target processes, identify key driver variables and attempt to calculate linkage with CSAT scores. 12/10/2015 Copyright Red Olive 2015 23
- 24. Measuring CSAT: last 12 weeks and 95% confidence 12/10/2015 24Copyright Red Olive 2015 Message: short- term weekly movement is inconclusive Now12 weeks ago
- 25. Measuring CSAT: What happened between March and April 2014? 12/10/2015 25Copyright Red Olive 2015 Message: There has been a notable shift in overall CSAT since April 2014 – was there some significant event? Now40 weeks ago
- 26. 12/10/2015 26Copyright Red Olive 2015 Proxy variable example: using SLA compliance when time unavailable SLA current week SLA previous week SLA comp 2 weeks prior Mean CSAT 0.217 0.398 -0.039 Median CSAT 0.2 0.415 -0.031 1 Scores -0.266 -0.395 -0.002 5 Scores -0.013 0.161 -0.077 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 AxisTitle Correlations – last 40 weeks * Indicates that the correlation is statistically significant at the 95% level * * *
- 27. OPTIMISE CAPITAL DEPLOYMENT: DETECT AND MITIGATE FLOOD RISK Copyright Red Olive 201512/10/2015 27
- 28. Aims: Identify areas most at risk of sewer flooding, the underlying factors, and changing risk over time. Better prioritise investigations, sewer cleansing and repairs. Reduce the number of sewer flooding incidents in the most cost effective way. Increase confidence in the level of capital maintenance expenditure required. 28 Sewer Flooding Risk Model Copyright Red Olive 201512/10/2015
- 29. Variable risk increasing over time i.e. risk is greater as problems remain unattended over time Variable risk increasing over time = risk is becoming more recent Variable risk decreasing over time i.e. risk reduces as problems are fixed by the maintenance teams 29 Risk Model based on 365 days history Risk Model based on 90 days history Risk Model based on 30 days history High Risk Low Risk Tracking Sewer Flooding Risk Copyright Red Olive 201512/10/2015 29
- 30. FRAUD AND ANOMALY DETECTION Copyright Red Olive 201512/10/2015 30
- 31. Fraud detection Here we use the term “fraud” quite loosely, to include non- compliance and payment errors as well as abuse. Traditional detection techniques are based on a set of business rules that fraudsters learn and adapt to; using analytics is one way to combat that. Detecting fraud in a high-volume transactional setting is different from detecting fraud in a one-off, often very high value setting (e.g. insider trading). We’ll look at the former. 12/10/2015 Copyright Red Olive 2015 31
- 32. Confirmed Cases Investigation Predictive Modelling Scoring models Profiles Suspects Anomaly detection Normality/deviation modelling How do we go about identifying fraud? Data 12/10/2015 Copyright Red Olive 2015 32
- 33. Predict the expected value for a claim, compare that with the actual value. Those cases that fall far outside the expected range should be evaluated more closely. – Use decision trees: • income < $40K » job > 5 yrs then good risk » job < 5 yrs then bad risk • income > $40K » high debt then bad risk » low debt then good risk – Or Rule Sets: • Rule #1 for good risk: » if income > $40K » if low debt • Rule #2 for good risk: » if income < $40K » if job > 5 years Group behavior using a clustering algorithm Identify outliers and investigate Build a profile of the characteristics of fraudulent behavior. Pull out the cases that meet the characteristics of fraud. 33(*Source: IBM)
- 34. MORE ON CAPITAL DEPLOYMENT: TEXT MINING (NATURAL LANGUAGE PROCESSING) EXAMPLE Copyright Red Olive 201512/10/2015 34
- 35. Overview of text mining Why is text mining of interest? Example: Imagine you are a large telecoms company with hundreds of customer service agents and you want to classify all inbound customer communication quickly and direct it to the right people to deal with it best. 12/10/2015 35Copyright Red Olive 2015 Text data sources Text enrichment Subject matching Sentiment classification Information delivery
- 36. Text data sources Facebook LinkedIn Twitter e-mails … 12/10/2015 36Copyright Red Olive 2015 Text data sources Text enrichment Subject matching Sentiment classification Information delivery
- 37. Text enrichment 12/10/2015 37Copyright Red Olive 2015 Text data sources Text enrichment Subject matching Sentiment classification Information delivery Why not sort your signal issues out instead of bringing new phones out!!!! Wk 3 of crap signal but yet paying FULL monthly contract! Vodafone sort it. Why not sort your signal issues out instead of bringing new phones out!!!! Wk 3 of crap [----] signal but yet paying FULL monthly contract! Vodafone sort it. Original Facebook Message Sentiment Amplifier Why[WRB] not[RB] sort[VBG] your[PRP] signal[VBP] issues [VBZ] out[IN] instead[RB] of[IN] bringing[VBG] new[JJ] phones[NNS]!!!![SYM] Wk[NNP] 3[CD] of[IN] crap[NN] but[CC] yet[RB] paying[VBG] FULL[NNP] monthly[RB] contract[NN] ![SYM] Vodafone[NNP] sort[VBG] it[PRP] .[SYM] Penn Treebank P.O.S. Tagger (English Messages) sort[VBG] signal[VBP] issues [VBZ] instead[RB] bringing[VBG] phones[NNS] Wk[NNP] 3[CD] crap[NN] paying[VBG] monthly[RB] contract[NN] Vodafone[NNP] Removal of stop words and punctuation
- 38. Subject matching 12/10/2015 38Copyright Red Olive 2015 Text data sources Text enrichment Subject matching Sentiment classification Information delivery Why not sort your signal issues out instead of bringing new phones out!!!! Wk 3 of crap signal but yet paying FULL monthly contract! Vodafone sort it. Original Facebook Message Subject Matching (Fuzzy Matching) Why not sort your signal issues out instead of bringing new phones out!!!! Wk 3 of crap signal [NETWORK]but yet paying FULL monthly contract! Vodafone sort it. [COMPLAINT] BUSINESS TRANSACTION: Complaint NETWORK: No Signal PRODUCT: Samsung Galaxy S4
- 39. Sentiment classification Many further factors help determine sentiment: Emoticons, “Likes” on social media channels, … Further text classification using e.g. Decision Trees. Result: a sentiment classification. 12/10/2015 39Copyright Red Olive 2015 Text data sources Text enrichment Subject matching Sentiment classification Information delivery
- 40. TEXT MINING – POLITICS Copyright Red Olive 201512/10/2015 40
- 41. Analysis undertaken so far Two samples of data from Hansard (the transcriptions of proceedings in the Houses of Parliament) have been downloaded, relating to: Nicholas Soames, Conservative MP and former Defence Secretary. Dennis Skinner, longstanding Labour MP. The various files were loaded into SPSS Modeler’s text mining platform. The data was parsed using Natural Language Processing (NLP) to identify prominent “concepts” and then some basic analysis of these concepts was carried out. 12/10/2015 41Copyright Red Olive 2015
- 42. Findings: Nicholas Soames’ concepts The most commonly repeating concepts identified are listed below with “country” the most frequent, occurring 72 times. “Immigration” occurred 40 times and was expanded further. 12/10/2015 42Copyright Red Olive 2015
- 43. Findings: Nicholas Soames, immigration A concept map was created centred on “immigration”. This shows the strength of association between two concepts. In the case of “immigration”, the strongest concept associations are with “defence”, “society” and “social”. 12/10/2015 43Copyright Red Olive 2015
- 44. Findings: Dennis Skinner, immigration In stark contrast, Dennis Skinner says virtually nothing on the issue of immigration. 12/10/2015 44Copyright Red Olive 2015
- 45. Findings: Dennis Skinner’s concepts One of the top concepts in Dennis Skinner’s comments is “pits”, occurring 54 times. 12/10/2015 45Copyright Red Olive 2015
- 46. Findings: Dennis Skinner, pits Below is a concept map centred on “pits”. The strongest associations are with “tories”, “help” and so on. 12/10/2015 46Copyright Red Olive 2015
- 47. Findings: Nicholas Soames concept categories In the “military” context, there seem to be particularly strong links between the categories “human resources”, “finance” and “geographical location”… 12/10/2015 47Copyright Red Olive 2015
- 48. Findings: Nicholas Soames concept categories … so if we go back to relevant original texts, linked below, we may expect to find the cost of having people in certain locations as a prominent theme. 12/10/2015 48Copyright Red Olive 2015
- 49. Findings: Dennis Skinner concept categories A similar analysis of Dennis Skinner’s concept categories based on “natural resources”. 12/10/2015 49Copyright Red Olive 2015
- 50. Learn more… Has this morning whet your appetite? We’d love to talk with you further about analytics for your own organisation. To arrange to do that please leave your contact details on one of the sheets near the door or just have a word with Jefferson, John or Mark. 12/10/2015 50Copyright Red Olive 2015
- 51. Preparing to try it out for real? Ready to try this out for real? We can help you build your business case and prove the benefits to your business on your data. Please have a chat with us at the end. If you’re already further along, we run more in- depth training courses: Solving business problems using data analytics. Statistical thinking. Data mining principles and techniques. Hands-on skills in data mining and predictive analytics. 12/10/2015 51Copyright Red Olive 2015
- 52. Quick recap What we’ve covered: Business context, modelling process, addressing the right problem(s). Customer intimacy: new business offerings (internet loans), skills you’ll need; customer development and retention (Royal Mail). Predictive asset management: customer satisfaction and internal processes; flood prediction. Fraud and anomalies: process for detection. Text mining: telecoms complaints, political analysis. 12/10/2015 52Copyright Red Olive 2015
- 53. Information and Data Management Contact details: jefferson.lynch@red-olive.co.uk Office: 01256 83 11 00 Mobile: 07860 353027 Analytics and Data Management