Web Analytics: Lecture Presentation. All Content on BIG DATA, AI AND MACHINE LEARNING (76 Pages)

Big Data, Ai and Machine Learning he role of Ai and Machine Learning – can we automate and improve our approaches? Data science uses automated methods to analyse vast amounts of data and extract knowledge Google ...

Big Data, Ai and Machine Learning he role of Ai and Machine Learning – can we automate and improve our approaches? Data science uses automated methods to analyse vast amounts of data and extract knowledge Google Analytics Customers Characteristics of Big Data Predictive Analytics for Marketers Managing Churn Predicting component failure Establishing Segments Examples in Retail Banking Mobile Telecoms Customer Analysis Public Sector etc It also discusses Pricing and Social Network analysis issues and what software is available. Predictive vs Descriptive Analysis and Modelling An introduction All models are wrong but some are useful Become familiar with the tools that help you Identify populations Predict behaviour Recommend activities to improve performance Track these Principles of data management Leventhal’s examples of data Examples of Data Sources Leventhal’s take on the role of the analyst She has to discuss with the business experts what the issues are Understand the nature of the data that supports the business processes Brief IT so it can be collected and prepared which will take 70% of the time Decide on the modelling techniques, build and test the mode Data quality Audit ◦ Values Analysis – is each variable well represented ◦ Statistical analysis – what is envelope of values represented by the different means and SDs ◦ What does a decile histogram tell us about the shape of the data Data preparation and cleansing – about 60-70% of the work An example Inspecting the Dataset The analytic modelling toolkit The same analysis from a machine learning perspective looks like this Classification Tools Decision Trees- Supervised learning Turn a classification into questions Types of Decision Trees When to use them Rule induction trees CHAID uses chi square techniques to decide on the splits They can form a random forest Iterative averaging of best fit There are decision trees in Google Analytics PCA (principle components analysis) is about reducing the variables to something manageable – start by removing highly correlated variables ◦ Highlight the key values that describe what’s going on ◦ It requires business acumen to see which things can safely be ignored Cluster analysis enables you to see which customers fall into natural segments. The self organising map is basically an unsupervised neural net Affinity analysis delivers the beer and nappies correlation Cluster Analysis Surface Vector Machines – a Supervised classification routine Neural Networks let the modelling software take the strain Neural nets are black boxes. they learn by refining the factors in the hidden layers to deliver a more accurate output – but there’s no model TPredicting Lifetimes – churn and in machines Cumulative hazard function is the mirror image of the survival function Acquisition and churn require a model of customer lifecycle Regression for Prediction Straightforward but consuming Who is going to buy what under which circumstances and how do we track it? Churn Prediction with Regression Spotting individuals that will cancel a subscription based on behaviour - It’s a binary classification task An example of using regression to predict churn Comparison of Approaches Software Solutions - Datamining assumes too much data to fit on one computer How targeting Models are built and deployed Precision and Recall Data Measures Machine Learning Libraries There are many libraries available if you wish to write machine learning code. Amongst the most popular general purpose libraries are: Weka (Links to an external site.)Links to an external site. is a Java library developed at the University of Waikato in New Zealand. It has a GUI, which is very useful, but can also be called from within Java code. These characteristics make it an ideal starting point for machine learning. R programming language (Links to an external site.)Links to an external site. is very popular for machine learning. There is no one library, but rather hundreds provided for free in a decentralised manner such that anyone can use them. Scikit Learn (Links to an external site.)Links to an external site. is a Python library for machine learning. It is a popular choice in industry, and hides a lot of the mathematics. Tensorflow (Links to an external site.)Links to an external site. (advanced) is an open source library initially developed by the Google Brain team, which can be used in a variety of programming languages including Python, C++, Java, Haskell and Go. It is used for general machine learning, and deep neural networks in particular. Pytorch (Links to an external site.)Links to an external site. (advanced) is a high performance Python library, optimised to take advantage of advances in the use of GPU processing Choosing your tool http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html What Leventhal says about testing Building Customer Segmentation Relevant / Identifiable / Viable / Distinctive / Complete / Exhaustive What level of complexity can your staff manage ◦ Are the segments meaningful AND doable – how often does the model need updating Assess business needs and available data. Define criteria 1. Demographic / psychographic / behavioural What do they need vs what do they Classification is about Segmentation Analysis by Segmentation 1) Understand GDPR 2) Data mining is separate from modelling – try and keep it as simple as possible 3) Don’t lose sight of the principles of statistics 4) The most valuable data is from your own customers 5) Modelled data is not the same as actual or raw data 6) you have to manage a combination of what data you have and what techniques you can use 7) Try and use predictive techniques to manage stock 8) Robust is better than sophisticated 9)Try and simplify and make usable the social graph 10) Testing is important but is about deciding where to bet your fiver 11) Get on with it – look for quick wins and follow the money Marketing with Smart Machines Alexander Borek and Joerg Reinold The Marketing Managers day – quite soon. Algorithms and Data Real time bidding Automated multivariate site testing Monitoring of most promising sales leads. Personalised offers like in Amazon Threads personal shopping adviser Every company is a media company Autonomous logistics Pay as you go vs ownership Kit as a service – Rolls Royce aero engines Fully automated supply chains COPYRIGHT DR ALAN RAE 2018 Recognition of individuals by face and voice across channels and partners Business content will be authored by machines Our challenge is to keep up with all this. Its cloud and API driven and probably on Amazon Web Services Machines deliver statistical probability actions statistics will be a core competency for marketers