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Data Mining (Introduction for Business Students)
 
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This short revision video introduces the concept of data mining. Data mining is the process of analysing data from different perspectives and summarising it into useful information, including discovery of previously unknown interesting patterns, unusual records or dependencies. There are many potential business benefits from effective data mining, including: Identifying previously unseen relationships between business data sets Better predicting future trends & behaviours Extract commercial (e.g. performance insights) from big data sets Generating actionable strategies built on data insights (e.g. positioning and targeting for market segments) Data mining is a particularly powerful series of techniques to support marketing competitiveness. Examples include: Sales forecasting: analysing when customers bought to predict when they will buy again Database marketing: examining customer purchasing patterns and looking at the demographics and psychographics of customers to build predictive profiles Market segmentation: a classic use of data mining, using data to break down a market into meaningful segments like age, income, occupation or gender E-commerce basket analysis: using mined data to predict future customer behavior by past performance, including purchases and preferences
Views: 5182 tutor2u
Data Mining Presentation (Customer Segmentation)
 
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Views: 2683 Afiq Zaimi
Leila Etaati_Show Me Potential Customers: Data Mining Approach
 
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SQLRally Nordic recording from Leila Etaati’s presentation in Copenhagen, Denmark, March 2015
Views: 370 SQLugSWE
Customer Segmentation in Python - PyConSG 2016
 
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Speaker: Mao Ting Description By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries. Abstract I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques. Event Page: https://pycon.sg Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/P6SD/
Views: 18446 Engineers.SG
Application of Data Mining in Business Management | Basic Concepts of Data Mining
 
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There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration, etc. What is Data Mining? Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. The information or knowledge extracted so can be used for any of the following applications − Market Analysis Fraud Detection Customer Retention Production Control Science Exploration Data Mining Applications Data mining is highly useful in the following domains − Market Analysis and Management Corporate Analysis & Risk Management Fraud Detection Apart from these, data mining can also be used in the areas of production control, customer retention, science exploration, sports, astrology, and Internet Web Surf-Aid 🧐 What we are going to Cover in the Video: 🧐 0:00 - 4: 35 Introduction to Data Mining 4:36 - 7:09 What is Data / Data vs. Information 7:09 - 9:13 What is Data Mining 10:00 -11: 00 Data Mining Process 9:14 - 11:45 Why Data Mining 12:04 - 14: 00 Application of data mining
Views: 526 UpDegree
Excel at Data Mining - Your First Predictions
 
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In this video, Billy Decker of StatSlice Systems shows you how to start data mining in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a set of predictions for new customers using a Logistic Regression models based upon old customers. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server to which you have access rights.
Views: 22223 StatSlice Systems
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
 
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AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 20854 Growth Tribe
Customer Prolfiles Facebook & Data Mining. Your Questions Answered
 
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This week there has been a lot of criticism over a company called Cambridge Analytica and Facebook over something called Data Mining. I’ve had around 50 emails asking about surveys, data analysis and how this applies to Customer Profiles for new and existing brands. With some concerned about doing a Customer Profile, I break down what my company (and other ‘good’ companies) uses for data; How it’s used and how it’s applied to Customer Profiles. --------------------------------------------------------------------------- IF YOU WANT TO BUILD A SUCCESSFUL FASHION BRAND FOR YOUR FUTURE, IN JUST 42 HOURS, THEN COME CHECK OUT THE FBB! THE FASHION BRAND BOOT CAMP. 2 time zones. Once a year. So don't miss out. CLICK HERE TO FIND OUT MORE: http://www.fashionserviceshongkong.com/training/bootcamp/ ------------------------------------------------------------------------- DON'T FORGET TO JOIN MY EMAIL LIST FOR YOUR FREE 66 PAGE START UP GUIDE TO BUILDING A FASHION BRAND! You'll also get on my weekly email with special discounts for products and news. CLICK HERE TO JOIN http://www.createafashionbrand.com/ ------------------------------------------------------------------------- GIVE ME SOME FEEDBACK https://goo.gl/forms/VsY7vGb0OPnKJ12u1
Data Mining - Churn Analysis
 
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Data Mining Final Project: Churn analysis on a mobile company churn data set
Views: 2837 Ayame Shiba
Harvard i-lab | Low-Fidelity Data Mining for Customer Insights
 
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How can we mine survey, sales and customer data to uncover new insights into the lives and needs of our users, without a strong background in statistical analysis or code development? In this session, Almighty CSO Ian Fitzpatrick will walk through approaches to finding anomalies, commonalities and outliers in data sets — with a focus on using these to drive better qualitative research that shapes a great brand, product or service experience. Particular emphasis will be placed on combining private and public data sets to uncover hidden patterns and opportunities. The workshop itself is designed to be highly-participatory and hands-on. Participants are encouraged to bring both a laptop or tablet computer and an eagerness to collaborate with others. Learn more about the Harvard Innovation Lab at http://i-lab.harvard.edu/ and follow us on Twitter at http://twitter.com/innovationlab and like us on Facebook athttps://www.facebook.com/harvardinnovationlab
Customer Segmentation
 
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By using advanced analytics to create your segmentation strategies, you can: - Identify your most proitable customers - Focus your marketing on segments most likely to purchase - Discover potential niche markets - Develop or improve products to meet customer needs For more information visit http://www.angoss.com/predictive-analytics-software/applications/customer-analytics/
Views: 26620 AngossSoftware
Improving the customer relationship management Using Data Mining
 
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Reach Us CONTACT US 1 Crore Projects 2017 Java IEEE Projects Door No: 68 & 70 ,Ground Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamil Nadu, INDIA - 600 026 Email id: [email protected] website:1croreprojects.com Phone : +91 97518 00789 / 7708150152
Views: 321 1 Crore Projects
Applications of data mining and machine learning in online customer care (KDD 2011)
 
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Applications of data mining and machine learning in online customer care KDD 2011 Ravi Vijayaraghavan P V Kannan With the coming of age of web as a mainstream customer service channel, B2C companies have invested substantial resources in enhancing their web presence. Today customers can interact with a company through channels such as phone, chat, email, social media or web self-service. With the availability of web logs, CRM data and text transcripts these online channels are rich with data and they track several aspects of customer behavior and intent. 24/7 Customer Innovation Labs has developed a series of data mining and statistics driven solutions to improve customer experience in each of these online channels. This talk will focus on solutions to enhance performance of web chat as a customer service channel. 2 stages of customer life-cycle will be considered -- new customer acquisition (or sales) and service of existing customers. In customer acquisition the key objective is to maximize incremental revenues via chat. While in customer service the objective is to drive up the quality of customer experience (measured by customer satisfaction surveys or mined customer sentiments) through chat. The solution based on machine learning methods involves: Real-time targeting of the right visitors to chat Predicting customer needs Routing customer to the right customer service agent Mining chat transcripts and Social Media Portals to identify key customer issues and customer sentiments Mining agents' responses for performance improvement Feeding back learning from 4 and 5 to 1 (better targeting) Real-life case studies will be presented to show how that this closed loop solution can quickly improve key metrics.
Customer Data Mining With Automated Email Sequences
 
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Local businesses who use email to gain the trust of their customers and prospects are using the best way to maximize potential. A large majority of businesses make the mistake of focusing on new business only. Doing so means you are ignoring the huge potential profits laying in wait, just under the surface. Nurturing your customers and prospects is the best way to understand their needs, sell them what they really want and continue selling to them, for years in many cases. Automated email sequences that communicate intelligently, help to identify when a prospect is ready to purchase. Keeping in touch with people lets them know you have not forgotten them. They will remember you when they are ready to buy your product or service. Virtual Anchor 246 5th St West North Vancouver BC V7M 1K1 http://virtualanchor.ca
Views: 26 Virtual Anchor
Quiterian Analytics, the fastest Data Mining for business users
 
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Get more value from your data. Give more value to your business. Quiterian, the fastest and advanced Analytical BI platform, incorporates advanced analytic techniques and predictive analytics that allow extracting the maximum value of data by the users, easily and instantly. No need for being an expert to know not only what is happening and why but also predict what will happen. Helping companies to be more efficient and competitive.
Increase Sales and Customer Retention by Data Mining
 
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Turn Key Solution for increasing sales, gross profits and owner retention
Views: 283 Scott Keller
Customer Insights Via Data Mining
 
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Statistical Consultants Ltd is a New Zealand based company that provides a wide range of statistical services to the private and public sectors, locally and internationally. http://www.statisticalconsultants.co.nz/
Association Rule Mining For Customer Basket Data Analysis | Basket Data Analysis Tutorial 2
 
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Association Rule Mining For Customer Basket Data Analysis | Basket Data Analysis Tutorial 2
Views: 569 Compile Guru
How To Analyze Your Customer Reviews with Text Analysis - Data Crunch
 
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Add in-demand data science skills to your resume: https://www.thelead.io/data-science-360/ If you run a business or organizations, it's important to know what your customers are saying about you - whether if its a form of a blog, social media post, review or comment. This is where text mining and text analysis comes into play. Data scientists build text mining algorithms to mine texts from customers and map out a word cloud to understand their customers. Dr. Lau shows us how to do text analysis in this Data Crunch episode. Text analysis data files for this episode: https://goo.gl/Y5YwRH Google Alerts: https://www.google.com.my/alerts Brandwatch: https://www.brandwatch.com/ =============== Where to follow and learn more from LEAD: Website: https://www.thelead.io Facebook: https://www.facebook.com/thelead.io/ Instagram: https://www.instagram.com/theleadio/ ================ LEAD is an institute in Malaysia, where we provide courses in Data Science, Full Stack Web Development, Digital Marketing & Business, for individuals and corporates — so they can find better careers or to build successful businesses. We teach career-ready skills that our students can use right away in their jobs or find a job. Rather than taking years to learn and master a subject, we have designed our courses to shortcut our students to be competent in the workspace. So we gathered a group of experts in their fields, to teach and mentor our students. Collectively, our 15+ years in technology mentoring means you’ll get real insights & strategies from the best developers, digital marketers, and data scientists.
Views: 516 LEAD
CRM for Success - Data Mining for Pinpoint Targeting or Analysis
 
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http://blog.infogrowcorp.com/ http://www.infogrowcorp.com As part of InfoGrow's CRM for Success Series - May's installment focused on Data Mining for Pinpoint Targeting or Analysis.
Views: 1093 InfoGrow
Predictive Analytics in Insurance
 
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If you have questions or comments on the contents of this video, please email us at [email protected] There has been considerable change in the relationships between customers and companies. Customers are in control of the relationships with their vendors and are not afraid to switch to a new provider if they do not feel they are receiving the service they deserve. Companies now have the ability to know their customers and market to them on a personalized basis using data mining and predictive analytics technologies. Predictive Analytics unlock insights that enable companies to add new customers and grow their existing business by improving their understanding of what their customers want. It uncovers hidden insights in customer data to create more personalized customer experiences that win more business while reducing costs and increasing customer loyalty. Predictive Analytics enable the very sharpest competitive edge. They deliver powerful, unique, qualitative differentiation by providing your enterprise a proprietary source of business intelligence with which to compete in Operations, Customer or Threat & Fraud applications in your organization. A predictive model generated from your data taps into experience to which only your company is privy, since it is unique to your prospect list and to the product and marketing message to which your customers respond (both positively and negatively). Therefore, the model's intelligence and insights are outside the reaches of common knowledge, and the top prospects it flags compose a customized, proprietary contact list. View this informative webinar to learn more about how Predictive Analytics are making a difference in the insurance industry through focused target marketing, and more efficient fraudulent claim detection. We discuss a detailed use-case for a real-world insurance company examining how specific customer attributes were used as indicators for fraud prediction.
Views: 13495 LPA Software Solutions
Charter Cable, Data Mining there Customers !
 
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If ANY cable company sends you a message like this. Saying you need a special box to receive there signals that is how they data mine your household. They then take this information ans store all your habits and sell them to advertisers. The records can also be subpoenaed by the government to show motive when building a case ageist you for any made up reason. So get your free big brother spy box now!!!
Views: 2508 David S
Analysis and prediction of Ε customers' behavior by mining clickstream data
 
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Analysis and prediction of Ε-customers' behavior by mining clickstream data Abstract: In a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion
Views: 678 1 Crore Projects
How Companies Use Customers Data?
 
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Datamining
Views: 53 Mahsa Mortazavi
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 470460 Brandon Weinberg
Ⓘ EN. Data mining techniques & customer relationship management through existing Wi-Fi.
 
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Data mining techniques & customer relationship management through existing wireless access networks (Wi-Fi)
Views: 195 spotyy.social.WiFi
Customer Behaviour Prediction Using Web Usage Mining
 
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Get more details on this system with details at http://nevonprojects.com/customer-behavior-prediction-using-web-usage-mining/ System monitors users web usage data and provides appropriate reporting to admin
Views: 6606 Nevon Projects
AutoAlert | Automotive Industry's Leading Data Mining and Trade-Cycle Customer Experience Platform
 
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AutoAlert.com - Built for the Future AutoAlert is the creator of the automotive industry's leading data-mining and trade-cycle management platform and other revolutionary software solutions. Backed by patented algorithms, our technology bridges the communication gap between a dealership's management, employees, and customers, creating high-quality sales opportunities, increased gross margin, and improved customer retention.
Views: 695 AutoAlert
Data Mining  How To Transform Data From Multiple Touchpoints Into Customer Insights Which You Can R
 
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Retail data mining can help identify customer behaviour, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, enhance goods consumption ratios design more effective goods transportation and distribution policies and reduce the cost of business. Learn from real life, practical guidance and examples about how it can help you in forecasting and planning and help your business grow.
Views: 95 Indiaretailing.com
Lecture 7 Customer Churn Prediction  (Business Data Mining 2/2561)
 
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Customer Churn Prediction using Python
Views: 100 Phayung Meesad
Data Science Demo - Customer Churn Analysis
 
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This introduction to Data Science provides a demonstration of analyzing customer data to predict churn using the R programming language. MetaScale walks through the stops necessary to train and test multiple algorithms in order to provide the most accurate model for predicting when a customer will leave the company.
Views: 28499 MetaScale
TN TRB Computer Science Syllabus - Business Computing #5 Data Mining
 
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Tamil Nadu TRB Computer Science Instructor GRADE1 Exam Syllabus Business Computing - Data Mining ------ Data Mining • It's the process to find patterns or relationship of Data using algorithms. • It's a process of analysing data from different perspectives and summarising it into useful information. • It gives answer which Data Base cannot give Data • Raw fact Eg: • Petrol price is Rs.75 per litter on 1st April • Petrol price is Rs.76.25 PL on 2nd April This data (price and date) stored in DB Information • We can get some information from Data Eg: • Price of the petrol is between Rs70 to Rs80 per litter in April Knowledge • From the useful Data and useful Information we can get some knowledge Eg: • When Petrol price is increasing by Rs5, the inflation rating is increased by 2% Data Mining: Extract the useful Decision / Answer from the Data Data • You can trust this Data, which is always correct based on current status that is stored in DB Information • The information gathered from Data is dynamic, which is getting changed based on Data • May be different for different time / place / person • Information is collected from Data. Data Mining Process / Life cycle 1. Data • Raw data from DB 2. Target Data • Split the necessary data • Selection of Data 3. Pre-processed Data • To remove unnecessary data • Deduct missing data 4. Transformed Data • Save the data in different form which can be mined • Normalized the data 5. Mining the Data • Extract the decisions by Patterns / Templates • By mathematical rules and algorithms • Also called machine-learning algorithms 6. Knowledge • Interpret the patterns to knowledge by user • From the Mined data template / pattern we can get the knowledge Extracting the knowledge from the Data is Data Mining EG of Machine-Learning algorithms • Classification learning • Numeric estimation • Association learning • and more Classification learning • Classify the characteristics of Objects/Entities • EG: A consumer will buy a new car in next year = Yes / No • Train the Machine using training data from the data what we have (Transformed data and Patterns) Appling the decision tree, showroom member can predict the new customer ability.
Webinar: Public Sector Data Mining in Higher Education
 
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If you have questions or comments on the contents of this video, please email us at [email protected] One of the biggest assets an organization or institution has is its data. That data contains patterns and relationships not readily identifiable. Enter Predictive Analytics. With IBM SPSS Modeler software, historical data is automatically mined detecting patterns and indicators which can be used to predict future outcomes, allowing you to prioritize efforts on those events which are most likely to occur. SPSS Modeler is a comprehensive analytics platform designed to bring predictive intelligence to decision making across your entire organization. Acquire customers more efficiently Grow value of existing customers Retain profitable customers Manage assets Maintain physical infrastructure Maximize capital Monitor your environment Detect suspicious behavior Control outcomes IBM SPSS Modeler connects data to effective action by drawing reliable conclusions about current conditions and future events. Attend this free webinar to hear how Predictive Analytics can make a difference in the public sector, specifically in the area of higher education. Listen to our SPSS expert discuss and demonstrate how SPSS Modeler software can help predict: Which students are most likely to graduate? Who are the most promising applicants for admission? Which alumni will donate and how much? How can the educational institution more reliably plan for future development? How can tuition and donor forecasts be driven by data and made more accurate?
Advanced Customer Segmentation for Consumer Marketing - MindEcology.com
 
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http://www.mindecology.com This video shows you how to use advanced market (customer) segmentation techniques that go beyond using traditional demographic data alone. Video uses data-oriented examples so that you can see how it really works. The result is better-targeted advertising and marketing for better return on investment (ROI). http://www.mindecology.com
Views: 23825 Jed Jones
Lead Generation from Data Mining - How to get new customers!
 
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Does your company use lists for your marketing? Are you paying over $100 and not even getting much detail on the businesses? Contact us TODAY to get your targeted list of businesses for as little as $15! Each list comes with the following columns of information: Business Name Number of Employees Phone Number Location & Address Website Email Addresses Easy to import into CRMs and data is always FRESH! Call us at (517) 295-0479, Email us at [email protected], Respond to this post or Message us!
Views: 105 Coders Farm
The Russ Belville Show #535 Data Mining - Stoners Are Your Best Customers
 
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Preview of Russ Belville's presentation "How To Not Piss Off Your Cannabusiness Clientele" for HIGH TIMES SoCal Cannabis Cup
Views: 36 Russ Belville