Predictive Data Analytics to Help Your Customers
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The document discusses the application and importance of predictive analytics in business, highlighting its role in predicting future behaviors based on historical data. It emphasizes the need for data preparation, ongoing model updates, and the integration of community input for effective use. Key trends include the automation of analytics through machine learning and the challenges posed by data privacy and quality.
Predictive Data Analytics to Help Your Customers
- 1. #DataTalkPredictive Data Analytics to Help Your Customers
- 2. Join our #DataTalk on Thursdays at 5 p.m. ET This week, we tweeted with Michael Beygelman, Co-founder and CEO of Joberate, Berry Diepeveen, Partner and Enterprise Intelligence Leader at EY, and Chuck Robida, Chief Scientist for Experian Decision Analytics. Check out all tweets from this Twitter chat: ex.pn/predictive
- 3. What is predictive analytics?
- 4. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk Predictive analytics is extracting information from data sets to determine patterns, predict outcomes and trends.
- 5. Chuck Robida Chief Scientist, Experian @ExperianDA ex.pn/datatalk #DataTalk Predictive analytics is the ability to use data to predict future behavior based on past behavior.
- 6. Berry Diepeveen Partner, EY @Berry_Diepeveen ex.pn/datatalk #DataTalk I think it is as old as business. Nobody can perfectly predict the future, but you want to be more accurate about what is likely to happen.
- 7. Chuck Robida Chief Scientist, Experian @ExperianDA ex.pn/datatalk #DataTalk It’s the ability to analyze data in a way that can scale, be reproduced, and provide unbiased results.
- 8. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk Clever marketers are redefining predictive analytics into whatever suits them today, so we need to beware.
- 9. Chuck Robida Chief Scientist, Experian @ExperianDA ex.pn/datatalk #DataTalk Some techniques get more attention than others like machine learning, but all are used to solve business problems.
- 10. Berry Diepeveen Partner, EY @Berry_Diepeveen ex.pn/datatalk #DataTalk It is about being able to intervene. What is the point of finding out we lost a customer after he left? We need to prevent losing one before it happens.
- 11. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk I’ve always said that predictive analytics needs to be actionable like a brake system in a car. When you press, it does something.
- 12. Chuck Robida Chief Scientist, Experian @ExperianDA ex.pn/datatalk #DataTalk Predictive analytics isn’t a crystal ball, but the value comes in identifying the propensity of certain behaviors.
- 13. How trustworthy is predictive analytics?
- 14. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk Predictive analytics is very trustworthy, but not when used in pure isolation.
- 15. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Predictive analytics has a natural life and models need to be continually validated and aligned to changes in behavior.
- 16. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk It’s not just about the predictive modeling, statistics and the algorithms. It’s also about the play, experiments, intuition and innovation.
- 17. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Behaviors change so should your models. Economic change-inflation, housing, unemployment. Life change: marriage, kids, job ...
- 18. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate In terms of relevance, if associated with some decisions of value or have meaning, predictive analytics can be very relevant.
- 19. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk And you must deal with in a sensible way, especially around sensitive use cases such as fraud detection.
- 20. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Simply put, predictive analytics still comes down to a cost/benefit decision. Use it as your compass.
- 21. What type of data do companies use for predictive analytics?
- 22. Depends on the business goal. Generally a mix of fit-for-purpose internal and external data types, structured or unstructured data. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 23. Sometimes companies start by using internal data. In my world, payroll data, promotions, performance reviews, etc. #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk
- 24. Depending on how much success they have with internal data, and how quickly, they’ll usually broaden out to third-party data. #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk
- 25. For lenders: asset evaluations for loans, address change for collections -- all good data, in compliance with regulation. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 26. For marketing: social, contact history, profile data, all good data. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 27. If we go back to the fraud detection use case; you’d have to rely on internal, external, structured and unstructured data. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk
- 28. The beauty is that there is no limit about what data sources you want to tap into. It’s always driven by the business and use case, not the other way around. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk
- 29. Only limitations are legal, compliance and your imaginations. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 30. How much data preparation needs to be done before executing predictive analytics?
- 31. It requires a very tight collaboration between business and data science in order to determine the iterations. Berry Diepeveen Partner, EY @Berry_Diepeveen ex.pn/datatalk #DataTalk
- 32. Data preparation is arguably as important as the rest of the process. ex.pn/datatalk #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 33. Garbage in, garbage out. Data preparation is the most important step. Incorrect or insufficient data equals bad business decisions ex.pn/datatalk #DataTalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 34. We see three phases in any predictive analytics program: 1) strict data management, 2) building and applying advanced analytics models, and 3) using data visualization to bring the insights back to the end user. Berry Diepeveen Partner, EY @Berry_Diepeveen ex.pn/datatalk #DataTalk
- 35. If the sample size is massive, it might be more practical to sample the data; else you can use the whole sample. ex.pn/datatalk #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 36. Without strict and rigorous data management, you should question your investments in data science. Berry Diepeveen Partner, EY @Berry_Diepeveen ex.pn/datatalk #DataTalk
- 37. Decide what to do with incomplete data, discard it or take guesses at missing data points by looking at other data in the sample. ex.pn/datatalk #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 38. Be careful before tossing any data. Bias! ex.pn/datatalk #DataTalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 39. Many activities like selecting, combining, and aggregating data are important, especially when defining the form for training. ex.pn/datatalk #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 40. How often should models get updated?
- 41. It’s more of a business decision. If your data is updated quarterly, no point in updating a model more often than that. ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate #DataTalk
- 42. Frequent model evaluation or validation is critical + results should be taken in context of other solutions and external factors. ex.pn/datatalk #DataTalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 43. Building good models is the science. It involves experimentation, sufficient quality data and is time consuming. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk
- 44. If data is updated daily, and you choose to update the model quarterly, you might have to live with some bad assumptions. ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate #DataTalk
- 45. Expect a model to naturally deteriorate over time. Predictive analytics needs to be continually validated for fit for purpose. ex.pn/datatalk #DataTalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 46. Regardless of the use case, you need to update models regularly and structurally, but additional ad hoc updates depend on use case. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk
- 47. Models are fit-for-purpose and consider things like economy, home values... Tests + benchmarks exist to ensure models are robuts. ex.pn/datatalk #DataTalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 48. What is often forgotten is that new models have to be retrained with the updated data sets - and results verified. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk
- 49. What are the best ways to test the effectiveness of predictive analytics?
- 50. There are many scientific ways to test, but the real question is did the analytics provide you with actionable insights, at the right time. #DataTalk ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen
- 51. Splitting data at the outset could be a good idea so you’re not accidentally creating a super model that only works on one set. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 52. Deploy them in a manner where their impact can be measured in a controlled environment like champion-challenger testing. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 53. Use a majority of the data (say 65% or so) for the build of the model, and use the 35% of the data for the test of the model. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 54. There are numerous ways to test models, and some people swear by some approaches almost like religion. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 55. Test models by using data not used during development. Validation won’t yield same results, so benchmarking plays a big role. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 56. One can use lift charts, decile tables, some people like to use target shuffling. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate
- 57. What are ways companies can use predictive analytics in new ways?
- 58. Possibilities are endless, but business focus is key. ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk
- 59. Predictive analytics used to scientifically predict anything from the future state economy + weather to spread + cures for disease. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA
- 60. Newest technologies allow you to quite efficiently translate unstructured into structured such that it can be included in models. ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk
- 61. #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk I spoke to a gentleman at Goldman Sachs. They were using predictive analytics in the hiring process.
- 62. #DataTalk Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk Goldman Sachs used analysis of incoming CVs to compare to top performers and those who had a cultural fit.
- 63. What are the challenges when working in predictive analytics?
- 64. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk Challenges? Too many :) But making sure you have ample relevant data is important, and making sure you have tested models
- 65. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Challenges working with predictive analytics: data availability, quality, volume, and statistically robust sample size.
- 66. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk We need to seriously consider data ownership and data privacy in every single engagement.
- 67. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Compliance and control over permissible purposes present challenges, especially when rich data can’t be used.
- 68. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk Too many examples of not treating data as a real asset that ultimately belongs to the customer.
- 69. Berry Diepeveen Partner, EY @Berry_Diepeveen #DataTalk ex.pn/datatalk Another challenge is resources and finding professionals who have skills around technology, techniques, modeling and business acumen.
- 70. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk Globalization of predictive analytics is another challenge.
- 71. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk In more mature markets, the uptake is “simpler” while in other markets less so, which creates challenges for global organizations.
- 72. What trends are happening in predictive analytics?
- 73. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk In terms of trends, machine learning to automate the analytics process itself is certainly one of the bigger trends.
- 74. ex.pn/datatalk #DataTalk Berry Diepeveen Partner, EY @Berry_Diepeveen We are predicting the future of predictive analytics now. We need an algorithm and a model. :)
- 75. ex.pn/datatalk #DataTalk Chuck Robida Chief Scientist, Experian @ExperianDA Available data + advanced statistics + new processing tech = businesses + can build more meaningful + relationships with consumers.
- 76. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk Another trend hard to ignore is the datafication of our lives; basketballs to tennis rackets, and Lumo Lift to help you stop slouching
- 77. Michael Beygelman CEO, Joberate @beygelman @joberate ex.pn/datatalk #DataTalk Along the datafication continuum, data privacy laws are severely lagging and will need attention.
- 78. ex.pn/datatalk #DataTalk Berry Diepeveen Partner, EY @Berry_Diepeveen Look at fantastic innovations from enterprise technology providers and new ventures that revolutionize the markets
- 79. Any final tips for companies working in predictive analytics?
- 80. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate Become more community based rather than managed by centralized IT at big companies, or siloed in some underfunded organization. :)
- 81. #DataTalk ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen Absolutely! The internet of things is creating great opportunities where we have seen completely new business models.
- 82. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Business new to predictive analytics maintain robust model validation methodology. Using a broken model will cost you money.
- 83. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate The input of community into the evolution of predictive analytics can have profound open-source like benefits.
- 84. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Sophisticated users of predictive analytics, remember to start with the business problem and work backwards.
- 85. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate My best tip for working in predictive analytics is walk, don’t run. Make sure you take very deliberate first steps.
- 86. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Predictive analytics serves to exist for solving complex business problems. Start with end in mind.
- 87. #DataTalk ex.pn/datatalk Michael Beygelman CEO, Joberate @beygelman @joberate Decide that you will have a culture of analytics and then move into that area. Don’t “test” analytics to see if they’re “for you.”
- 88. #DataTalk ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen Do not underestimate how important the data visualization is to end user adoption of predictive analytics.
- 89. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA The world of analytics and data is exploding. It’s critical to prioritize analytical opportunities to stay ahead of competition.
- 90. #DataTalk ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen And do not underestimate how important the data visualization is to end user adoption of predictive analytics.
- 91. #DataTalk ex.pn/datatalk Berry Diepeveen Partner, EY @Berry_Diepeveen The business needs to work with the insights and it is not about developing the most accurate and complex model.
- 92. #DataTalk ex.pn/datatalk Chuck Robida Chief Scientist, Experian @ExperianDA Simply put, predictive analytics still comes down to a cost/benefit decision. Use it as your compass.
- 93. Join our #DataTalk on Twitter on Thursdays at 5 p.m. ET. experian.com/datatalk