Analytics is probably the most important tool a company has today to gain customer insights.This is why the Big Data space is set to reach over $273 Billion by … we'll take a look inside to see how a model works- 1. Infer, Infer, Infer. According to the renowned AT&T … Analytics driven cross-selling and up-selling campaigns provide remarkably higher returns. Ever since McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity, it has witnessed the rise and triumph of Machine Learning, especially in Predictive Analytics. As of this date, Scribd will manage your SlideShare account and any content you may have on SlideShare, and Scribd's General Terms of Use and Privacy Policy will apply. This analytics model has been of considerable benefit to the marketing function, and is hence widely used to improve marketing Return on Investment (ROD. [BIG] DATA ANALYTICS Take the “Predictive Analytics Plunge!” What business leaders need the most is forward-looking, predictive insight that will help them stay ahead of the curve. Predictive Models in Campaign analytics Based on historical data and customer profiles, it is possible to classify customers according to their likelihood of buying a product or a service through a campaign. ABOUT ME Currently work in Telkomsel as senior data analyst 8 years professional experience with 4 years in big data and predictive analytics field in telecommunication industry Bachelor from Computer Science, Gadjah Mada University & get master degree from Magister … Data Science and Predictive Analytics - Free ebook download as Powerpoint Presentation (.ppt / .pptx), PDF File (.pdf), Text File (.txt) or view presentation slides online. Now customize the name of a clipboard to store your clips. If you continue browsing the site, you agree to the use of cookies on this website. Predictive Analytics, Big Data, and How to Make Them Work for You. The model may employ a simple linear equation or a complex neural network, mapped out by sophisticated software. Its applications range from customer behaviour prediction, business forecasting, fraud detection, credit risk assessment and analysis of life sciences data. ENGAGE WITH YOUR CUSTOMER In my grocery store example, the metric we wanted to predict was the time spent waiting in line. Applications of Predictive Modelling Analytical customer relationship management (CRM) Health Care Collection Analytics Cross-cell Fraud detection Risk management Industry Applications Predictive modelling are used in insurance, banking, marketing, financial services, telecommunications, retail, travel, healthcare, oil & gas and other industries. At the first stage, segmentation helps reach out to prospects with higher predicted conversion rates, thereby increasing the campaign SUCCeSS rate as well as the ROI. Well truth be told, ‘big data’ has been a buzzword for over 100 years. Say you are going to the s… Prediction Impact. 1. How the model work In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Big Data Analytics 1. The PowerPoint PPT presentation: "Big Data and Predictive Analytics in Health Care" is the property of its rightful owner. After. Examples and use cases include pricing flexibility, customer preference management, credit risk analysis, fraud protection, and discount targeting. How do companies turn the promise of Big Data and advanced analytics into value? Predicting Modeling (also known as Predictive Analytics) is the process of automatically detecting patterns in data, then using those patterns to foretell some event. The big data concept started outside of HR, and most IT professionals would NOT consider the typical HR analytics project and data sets as “true” big data. There are other cases, where the question is not “how much,” but “which one”. It is based on advanced analytics and Predictive models are commonly built to predict: Customer Relationship Management the chance a prospect will respond to an ad Mail recipients likely to buy when a customer is likely to churn if a person is likely to get sick Portfolio or Product Prediction Risk Management & Pricing, Predictive Models Ideally, these techniques are 'widely Used: Linear regression Logistic regression Regression with regularization Neural networks Support vector machines Naive Bayes models K-nearest-neighbors classification Decision trees Ensembles of trees Gradient boosting. Tableau can provide specific views of small events or co-relate information to present trends and forecasts in real-time. Thus, every campaign can target the set of customers with better purchasing potential for that service/product. Using only PowerPoint or Keynote, you can easily make illustrations about Predictive Analytics, Data Mining, show statistics infographics quickly on a slide. Slide from general lecturing "Big Data Analytics: Engage with Your Customer" at Muhammadiyah Jakarta University. Azure Analysis Services is an enterprise grade analytics as a service that lets you govern, deploy, test, and deliver your BI solution with confidence. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. ... Predictive Analytics using Big Data is … Simulated scenarios can help evaluate the revenues at various price points. Shell analyses big data sets to detect events and abnormalities at downstream chemical plants using predictive analytics with MATLAB®. If you continue browsing the site, you agree to the use of cookies on this website. Scribd will begin operating the SlideShare business on December 1, 2020 Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Power BI is a suite of business analytics tools that deliver insights throughout your organization. Analytics and Big Data in the Oil Field Occidental Petroleum Corporation May 9, 2017 ... • Real time Data Historian • Predictive Analytics • Advanced Surveillance Technical Data Management ... Microsoft PowerPoint - Morgan Stanley Analytics and Big Data Keynote 050917 Website Final.pptx While these statistics-driven campaigns yield higher ROI, they also reduce the irritation caused by non-relevant communication, thereby indirectly reducing customer dissonance. Below is the list of points that describes the key difference between Big Data and Predictive Analytics : 1. Connect to hundreds of data sources, simplify data prep, and drive ad hoc analysis. That is what statistics and DM algorithms do. On average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%. Monitoring What is happening Predictive Analytics What is going to Happen in future? Predictive Models in Retail industry Campaign Response Model - this model predicts the likelihood that a customer responds to a specific campaign by purchasing a products solicited in the campaign. Data without context and connection is meaningless. Customer segmentation Customers are segmented both at the pre-subscription and subscription phases. (Although, at SMD, we have analyzed many data sets with millions of data points.) ACTION @ predictiveanalyticstoday.com, Business process and on Predictive Modelling Business process on Predicting modelling Creating the model Testing the model Validating the model Evaluating the model Features in Predicting modeling Data analysis and manipulation Visualization Statistics Hypothesis testing, How the model work In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. Predictive analytics and data science are hot right now. Predictive analytics help forecast traffic patterns and peak period routing, and is thus of immense benefit in the smooth running of network operations. Clipping is a handy way to collect important slides you want to go back to later. This analytics model may be utilized across all the functions like marketing, credit Risk, customer service and so on. Hence, Big data and analytics connote competitive advantage. Predictive Analytics techniques are used to study and understand patterns in historical data and then apply these to make predictions about the future. Even the predictive analytics on large data is more accurate and help discover patterns. If you have your own PowerPoint Presentations which you think can benefit others, please upload on LearnPick. Do you have PowerPoint slides to share? Data Handling & Analytics - Department of Electronics & Telecommunication Engineering - A presentation on Data Handling & Analytics which includes topics like Types of Data, Rapid Growth of Unstructured Data, What is big data, Big Data Analytics, Big data challenges and more. Dbi 339: Predictive Analytics With Microsoft Big Data PPT Presentation Summary : Predictive analysis tools from Microsoft. Predictive analytics is a branch of business intelligence that goes beyond merely interpreting or contextualizing data. Analytics can ensure that network operations are run as pro-actively and scientifically, taking cognizance of changing traffic patterns. Customer lifetime value analytics The Customer Lifetime Value model provides the predicted yield from each customer over the customer life cycle. Predictive Maintenance Position Paper - Deloitte Analytics Institute 05 Introduction Knowing well ahead of time when an asset will fail avoids unplanned downtimes and broken assets. Getty. the focus of the model is usually on how to reach the markets efficiently, this model is used mainly for revenue and workload allocation activities. [BIG] DATA ANALYTICS ENGAGE WITH YOUR CUSTOMER PREPARED BY GHULAM I 2. Data analytics, of any size, is data analytics… Folks, I beg to argue the following: inductive analytics is a better denomination than predictive, for the seemingly obvious reason that algorithms induce values from known data. Introduction to Big Data Analytics and Data Science, Customer Code: Creating a Company Customers Love, Be A Great Product Leader (Amplify, Oct 2019), No public clipboards found for this slide, Full-time Traveller, Part-time Entrepreneur. The data mining and text analytics along with statistics , allows the business users to create predictive intelligence by uncovering patterns and relationships in both the structured and unstructured data. Data Analytics with “Big Data” Descriptive (past) Predictive (future) Prescriptive (do this) Descriptive (past) Predictive (future) Prescriptive (do this) Before. Predictive analytics and big data. Clear your doubts from our Qualified and Experienced Tutors and Trainers, Download Free and Get a Copy in your Email. If a computer could have done this prediction, we would have gotten back an exact time-value for each line. embedded analytics is a better denomination than prescriptive. Marketing spend optimization A Marketing Spend Optimization model helps marketing managers and product managers take decisions based on what works and what does not. a Sales territory optimization optimization of sales territories is necessary to align and balance workload and market potential. In Information Week’s Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive, Dr. Michael Wu, Chief Scientist of Lithium Technologies in San Francisco, describes Descriptive Analytics as the simplest form of Data Analytics, which captures Big Data in small nuggets of information. Regression models Customer Segmentation Cross-Sell and Upsell New Product Recommendation Customer Retention/Loyalty/Churn Inventory Management. Combined Predictors Means Smarter Rankings 3. Statistics, MBA Entrance, Management Subjects, BBA... Tableau An Advanced Business Intelligence Software. Architecture Big Data has to do with the quantity of data, typically in the range of .5 terabytes or more, where the capacity of relational database systems starts to degrade so the need of cloud-based pipelines like AWS and data warehousesare the needs of the hour. On the other hand, Predictive analytics has to do with the applicat… Risk Management: Bank anlyse transaction data to determine risk and exposures based on simulated market behavior, scoring customer and potential clients. A Simple Curve Shows How Well Your Model Works 5. Mohamed Chaouchi is a veteran software engineer who has conducted extensive research using data mining methods. During campaigns, subscribers are divided into segments to which specific campaigns are targeted. Cross-selling and up-selling A very real challenge in the telecom industry is how to increase yield from the current subscribers, or how to improve Average Revenue per User (ARPU).Cross- selling and up-selling activities can now be supported by predictive analytics, while drawing on association rules and transaction histories. This overview highlights 16 examples. Predictors Rank Your Customers to Guide Your Marketing 2. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Anasse Bari, Ph.D. is data science expert and a university professor who has many years of predictive modeling and data analytics experience. Connection to Analytics: Underlying all the big data talk is the concept of analytics. In this case the question was“how much (time)” and the answer was a numeric value (the fancy word for that: continuous target variable). PowerPoint slide on Predictive Analytics compiled by Rajib Kumar De. Data Science graphics library for creating presentation on data, analytics and Big Data topics. See our User Agreement and Privacy Policy. Churn modeling The customers leaving the current company and moving to another telecom company are called churn and it can be reduced by analyzing the past history of the potential customers systematically. Multivariate statistical models running on MATLAB Production Server™ are used to do real-time batch and process monitoring, enabling real-time interventions when abnormalities are detected. If you wish to opt out, please close your SlideShare account. •Analytics on non-relational, multi-structured, machine-generated data •Analytics that need to scale to big data sizes •Analytics that require reorganization of data into new data structures – graph, time & path analysis •Analytics that require fast, adaptive iteration The full Report discusses Machine Learning use … ¥Big Data: Wide and Long ... Predictive Analytics Technology 40 ¥Data preparation: An intensive bottleneck, critical to success. It's also extensively used for allocation of territory for managing operations, among channel intermediaries in pre-pay business units. Fraud analytics Data synthesis can help telecom service providers (TSPs) navigate their complex organizational structures and target and collect relevant fraud data 'when the need arises. Sisense for Cloud Data Teams formerly Periscope Data is an end-to … Pillars of Predictive Analysis. Tommy Jung is a software engineer with expertise in enterprise web applications and analytics. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. If so, share your PPT presentation slides online with PowerShow.com. Prediction Impact. Presentation PDF Available. Please enter the OTP sent to your mobile number: Post an enquiry and get instant responses from qualified and experienced tutors. The Computer Makes Your Model from Your Customer Data 4. Sisense for Cloud Data Teams. Predictive Analytics Process Defi ne Project Data Collection Data Analysis Statistics Modeling Deployment O predictiveanalyticstoday.com, How the model work(cont,) Here you will learn what a predictive model is, and how, by actively guiding marketing campaigns, it constitutes a key form of business intelligence. What is Predictive Modelling Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown fUtUre events. By successfully applying predictive analytics the businesses can effectively interpret big data for their benefit. Predictive DATA Analytics Process Predictive Analytics TIME Reporting/ Analysis What happened Why that happened . Price optimization Price optimization contributes significantly to revenue development and profitability and is especially important in the corporate sales segment, where awareness of the impact of the various pricing options offered is critical. Predictive Analytics Software SAS Analytics STATISTICA IBM Predictive Analytics MATLAB Minitab. Business analytics - Big Data Benefits Of Big Data Analytics in Banking Sector Fraud Detection: It help Bank to detect, prevent and eliminate internal and external fraud as well as reduce the associated cost. The main components of Predictive Analysis are the monitoring at any given moment of Big Data, the understanding of data analytics and effective utilization of data across the enterprise. You can change your ad preferences anytime. January 2020; DOI: 10.13140/RG.2.2.24222.48967. These models are widely used by product managers and finance teams. PREPARED BY GHULAM I. For each approved PPT you will get 25 Credit Points and 25 Activity Score which will increase your profile visibility. In case of sampling a subject of interest, the more samples one has; the better is the result. Rich library of data mining algorithms for diagnostic, predictive analytics—clustering, time series, neural nets, Perhaps the most promising and productive way to do just that is through the fast growing and rapidly evolving practice of predictive analytics. In the past few years, predictive analytics has gone from an exotic technique practiced in just a few niches, to a competitive weapon with a rapidly expanding range of uses. Understand that not all Big Data is useful data. The model also predicts the amount of the purchase given response. Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. big data analytics found in: Big Data Analytics Applications Ppt PowerPoint Presentation Pictures Professional Cpb, What Is Big Data Ppt PowerPoint Presentation Styles Background, Big Data Analytics Tools And Techniques Ppt.. Learn more. Predictive analytics Uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. High priority customers can be given loyalty bonuses, preferential treatment through personalized service, better credit norms for contract subscribers etc. Prediction Impact. See our Privacy Policy and User Agreement for details. Looks like you’ve clipped this slide to already. Big Data holds the answer," he simply corroborated companies' dependency on Big data. Conclusions Customer Data Predictive Analytics with Data Mining: Predictive Model, Why Predictive Modelling Nearly every business in markets will eventually need to do predictive modeling to remain ahead of the curve. Provide future-proof detection techniques Guard against habitual offenders Ensure that pre-paid service is truly risk free Launch profitable IP-based services Network optimization Network management is possibly be the most complex operation in a telecom company, the size of the investment decisions and the cost of a failure in terms of customer perception.