Predictive Simulation & Big Data Analytics ISD Analytics - PowerPoint PPT Presentation

Predictive Simulation & Big Data Analytics ISD Analytics “Predict a better future”

Overview Simulation can play a vital role in the emerging $billion field of Big Data analytics to support Government policy and business strategy decisions Overview  How simulation plays a key part in the Big Data Predictive Analytics process  Introduce Simulait simulation-based consumer analytics platform  Introduce Simulait simulation-based consumer analytics platform  Case studies: water, energy, emergence response, retail, transport  Simulait Online – simulation in the cloud for on-demand access and large scale simulations “Predict a better future”

Data Analytics & Decision Process Past Future Observe Predict Influence Descriptive Analytics Predictive Analytics Prescriptive Analytics Business Questions: Business Questions: Business Questions: What happened? What happened? What should I do about it? What should I do about it? What is likely to happen? What is likely to happen? Why did it happen? How do I influence the future? Solutions: What is happening? What are the consequences? Why is it happening? Simulation Solutions: Statistics & linear regression Predictive data-mining Simulation Solutions: Forecasting & trend reporting Optimisation Data mining & forensics Real-time analytics & mining Market segmentation Reporting & dashboards Ad-hoc database queries Less data, greater insight, greater value “Predict a better future” * Based on Gartner’s model of analytics

Projection vs Prediction Traditional statistical approaches project future behaviour by extrapolating past behaviour  Observe and forecast what people do but not “why” they do it  Unable to effectively represent complex consumer behavior  Limited functionality – unable to address a broad range of business problems  Past demand is not always a good predictor of the future 10 000 Influence future sales by testing strategies with Simulait 1000 Total Sales Changing population & consumer trends 100 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 “Predict a better future”

SimulAIt – An Analogy SimulAIt is a real life SimCity application where businesses or Government can predict and test strategies to influence the behaviour of large populations  Diverse domains: water, energy, emergency response, retail, transport, ...  Diverse applications: strategy, policy, pricing, demand forecasting, marketing, community behaviour and social planning, new product uptake, etc....  Global applicability: Australia, Europe, USA  Cloud solution: SimulAIt Online can be accessed on-demand using a web browser “Predict a better future”

Simulait: A Truly Predictive Approach  Accurate: proven approach, demonstrated over 95% accuracy  Model not built on past demand data – demand data used to validate the model  Accuracy due to greater representation of a broad range of consumer factors  Benefits are more than accuracy – it’s the scenarios that you can test with it!! “Predict a better future”

Simulait Architecture “Predict a better future”

Case Study 1: Victorian Water Utilities Objectives  Isolate and quantify the effectiveness of past water conservation strategies – economic, regulatory, social (communications) & environmental  Forecast bounce-back in water demand from easing restrictions & price increases  Assess impact of product uptake on demand and revenue  Assess impact of product uptake on demand and revenue  Build a business case to industry regulators – pricing review  Build demographic demand profiles  Blind validation : Used 4 yrs of demand data to calibrate outdoor water use and then forecast next 6 years of demand without access to actual demand data “Predict a better future”

Case Study 1: Victorian Water Utilities Blind validation results Average monthly household water consumption 35 30 umption 25 Water consump 20 15 10 Simulated Actual-calibration data 5 Actual - blind validation data 0 Jul-00 Jul-01 Jul-02 Jul-03 Jul-04 Jul-05 Jul-06 Jul-07 Jul-08 Jul-09 Jul-10 “Predict a better future”

Case Study 1: Victorian Water Utilities Key outcomes and benefits  Informed capital expenditure, corporate plans, water restriction schedules  Rigorous business case to industry regulators to maximise product price and revenue  Isolated and quantified the effectiveness of past & future strategies (campaign analysis)  Informed & increased ROI on future strategies “Predict a better future”

Case Study 2: Water in USA & France Key outcomes and benefits  Model transferable to different countries  Better for long term forecasting – tendering, strategic & financial planning, design future cities, etc...  Support water conservation, regulation, new water rates, impact of recession, etc... Calibration Calibration point >90% Accuracy “Predict a better future”

Case Study 3: Rebates/Retail Objective  Identify a mix of products and prices for the water rebates program that maximises efficiency and keeps within the program budget  Three projects, and now a 3 year license to 2015 Approach  Simulated 2 million households, 4.5 million consumers   Incorporated consumer preference and affordability, and product age, failure and Incorporated consumer preference and affordability, and product age, failure and price  Simulated product uptake and efficiency with different prices Key outcomes and benefits  Accurate predictions of product up-take and budget spend  Prevented budgets blow-outs  Cost/benefit (triple bottom line) analysis of different strategies  Forecast the ROI of different demographics and regions, and to assist with targeted (micro)-marketing of the rebate program “Predict a better future”

Case Study 4: Energy Customer Personalization Using 1% of CRM data in the first 6 months, Simulait was able to accurately predict what each specific customer will do, and why, for the next 2 years!!! Energy load forecasting accuracy Total Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2008 99.0% 99.2% 97.9% 98.8% 98.0% 95.0% 98.5% 99.6% 97.0% 99.6% 98.7% 96.5% 85.0% 2009 2009 99.8% 99.8% 96.7% 96.7% 99.3% 99.3% 99.3% 99.3% 99.0% 99.0% 98.9% 98.9% 98.4% 98.4% 98.8% 98.8% 95.1% 95.1% 97.3% 97.3% 93.1% 93.1% 98.6% 98.6% 98.3% 98.3% 2010 98.3% 91.9% 97.9% 97.1% 97.6% 98.6% 98.1% 99.1% 97.1% 87.8% Calibration 00 Prediction 50 Actual Forecast 00 50 00 50 00 50 “Predict a better future”

Case Study 5: Energy - EV Uptake & Transport Objective  Predict the uptake of Electric Vehicles over time to 2040  Predict usage and charging behaviour of electric vehicles  Impact on the electricity network (extra peak load) to support reliability and quality risk management “Predict a better future”

Case Study 5: Energy - EV Uptake & Transport Approach  EV Uptake consumer decision model  Simulated the new and used vehicle market across Australia  Considers many dynamic factors: consumer type, petrol and elec price, car range, charge times, charge infrastructure, upfront price, ongoing costs, dwelling suitability, battery replacement, depreciation, market penetration, etc...  EV usage: transport/activity model  Model each consumer’s daily activities and transport/vehicle use  Factors include: consumer type (e.g. occupation, family structure), day of week, number of vehicles in the household, activity types (work, school, shopping, entertainment, family/social visits, etc...)  Other factors: passenger trips, infant trips to carers if both parents working, separate household activities for independents, vacation from work (e.g. for parents during school holidays), etc...  EV charging and increase in peak demand  Charge times and location: home, work, fuel station, shopping centre, etc..  Other complex factors: power point upgrades, vehicle-to-grid system “Predict a better future”

Case Study 6: Emergency response - bushfire Following the 2009 bushfires that claimed 173 lives, the Victorian Royal Commission identified that “...strategies must reflect how people actually behave... Timely and accurate warnings can provide triggers, but the content and delivery must be carefully developed to elicit the right response” “Predict a better future”

Case Study 6: Emergency response - bushfire Objective  Model community behaviour to bushfires and warnings to support bushfire strategy and policy, and ultimately save lives  The model predicts:  What people do and when: Stay, leave or “wait and see”  Where will people go: neighbours, designated shelter, leave region, open area “Predict a better future”