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Machine Learning Tech You Could and Should Use Tomorrow
This document discusses how machine learning technologies can be used in various business applications such as marketing, customer analytics, and image recognition. It provides examples of companies like Netflix and Amazon that are successfully using machine learning. It also outlines a layered approach for implementing these technologies and discusses some limitations and cautions when adopting machine learning.
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Machine Learning Tech You Could and Should Use Tomorrow
- 1. Machine Learning Tech youcould and should use tomorrow Stephen Kenwright Marketing Director
- 2. Sheffield Hallam UniversityConfidential
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- 4. This guy wasone of my lecturers Ray was using machine learning to identify authorship @stekenwright
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- 9. How we usetext mining First-party data like internal search queries, help desk questions, chat logs, phone logs Social media data, reviews and online coverage/links Search queries Confidential@stekenwright
- 10. What can wedo with this data? PredictVisualise trends Connect data to customer records Compile text data in one place Confidential@stekenwright
- 11. Possible use cases Whichkeywords generate the most profitable customers? Does survey data match up to sales? Which customer complaints lead to churn? Confidential@stekenwright
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- 15. A quick history Confidential Early 1900s 1970s 1990s Now IntuitionStatistical programming languages Automated machine learning Manual analysis Visual statistical software Using experience and judgement to predict outcomes Writing code to construct statistical models The software knows how to analyze your data and does it for you Manual calculations to predict outcomes Drag and drop workflows with menu driven commands to set up and statistical analysis @stekenwright
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- 17. ConfidentialConfidentialConfidential By 2020, 85%of interactions will be handled without a human. Gartner
- 18. Confidential Layered approach toimplementation 4. One-to-one communications 3. Life style centred 2. Customer life cycle centred 1. Basic Increasingsophistication (Data,Audienceinsight, Technology) Example types of data:Example communications: Customer recognition Personalisation to drive relevancy and CTA Segmentation centred on lifestyle approach Fully personalised Basic information at purchase Customer life cycle position, purchase history Demographic: Age, affluence, geo- location, motivation Full benefits of Single Customer View @stekenwright
- 19. Netflix In the wild:Netflix.
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- 21. “Machine learning isa core, transformative way by which we’re re-thinking how we’re doing everything.”
- 22. Machine learning inthe Google stack In-Market Audiences Smart Display Campaigns Smart Bidding Confidential@stekenwright
- 23. Limitations You can create morecustomised audiences manually Brand safety has made marketers want more control over creative Seasonal campaigns are still manual Confidential@stekenwright
- 24. Choosing tech, accordingto Forrester Confidential@stekenwright
- 25. Everyone’s machine learningis, err…learning Pre 2010 2015 2017+ Confidential@stekenwright
- 26. Where we arenow.
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- 28. Analytics maturity Market sizing Marketsensing NBA modelling Proposition development Price modellingCampaign evaluation Data visualisation A/B testing PREDICTIVE Profiling / segmentation Customer lifetime value PRESCRIPTIVE Web analytics Propensity modelling Machine learning PRE-EMPTIVE KPI reporting Research analysis Upsell modelling DIAGNOSTIC DESCRIPTIVE COMMERCIAL VALUE COMPLEXITY Attribution / marketing mix Confidential@stekenwright
- 29. Developing personas Demographics • Theyoungest group • x% are families • Long distance travel Behaviour • Summer time travel • Book 4-6 months in advance • Less likely to visit in 12 months Lifestyle • Into music • Follow current affairs • Shop online • Lower income % of Base – a% % Value – b% Spend per visit - £c HH 3 year value - £d @stekenwright
- 30. Marketing mix modelling saves£2.4m PPC spend. Confidential
- 31. ConfidentialConfidentialConfidential Use email openrates to inform ad copy.
- 32. Phrasee personalises email usingmachine learning. Confidential
- 33. Self-serve data cleanse– puradata.co.uk 3. Dedupe
- 34. Olduvai gorge, TanzaniaConfidential
- 35. Reducing response times forhumanitarian aid. Confidential
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- 38. Edit Classifier. • EditBuildings_1406043538 •"custom_classes": 5, “ • "class": "HomeComplex", • "class": "HomeSmall", • "class": "TinComplex“, • "class": "TinLarge", • "class": "TinSmall", Uses reinforcement learning to train the Watson image recognition and classification algorithm Confidential@stekenwright
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- 40. ConfidentialConfidentialConfidential 74% of organisations implementingAI who believe it’s making them more creative. Capgemini 2017
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- 42. Confidential The Great Hallwas filled with incredible moaning chandeliers and a large librarian had decorated the sinks with books about masonry. Mountains of mice exploded. Several long pumpkins fell out of McGonagall. Dumbledore’s hair scooted next to Hermione as Dumbledore arrived at School. The pig of Hufflepuff pulsed like a large bullfrog. Dumbledore smiled at it, and placed his hand on its head: “You are Hagrid now.” http://botnik.org/content/harry-potter.html
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