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CDO Advisors Quick Start Packages

Power BI, Analytics and Data Science Quick Start CDO Advisors Quick Start packages offer businesses a low cost option to explore Power BI, Analytics or data science.  Executives know they need better information from their data and are often confused on what technologies are available to transform their data.  You know your management team is frustrated by their ability to make data driven decisions based on data science and predictive analytics.  Our quick start packages are designed to get you a working proof of concept in as little as 5 or 10 days.  All of our packages leverage your data to provide better reporting, analytics, or data science.  As a result of these packages, see exactly what your business could gain by investing in these technologies with minimal cost. Quick Start Packages: Analytics Quick Start Customer Segmentation Quick Start Power BI Quick Start RapidMiner Quick Start Text Mining Quick Start  

By |2019-05-12T15:24:18+00:00May 12th, 2019|Data Mining, Power BI, RapidMiner|0 Comments

RapidMiner Auto Model for Online Data Mining

Get started with Data Mining using RapidMiner Auto Model RapidMiner recently released their online data modeling tool called Auto Model.  This product allows you to upload data and have RapidMiner build initial data models for you.  Currently there are limited algorithms that are available, however, the tool is a great way to get started with RapidMiner and learning data science. Available algorithms: Decision Tree Naive Bayes Generalized Linear Model Logistical Regression Watch the our youtube video - Auto Model Titanic Example Try out the product - automodel.rapidminer.com        

By |2019-04-22T02:06:52+00:00October 21st, 2018|Data Mining, RapidMiner|0 Comments

RapidMiner Wisdom Conference Sponsor

RapidMiner Conference Sponsor Come join me at RapidMiner Wisdom Conference on October 10-12, 2018 in New Orleans.  CDO Advisors is proud to be a RapidMiner sponsor for this event.  It should be a great few days of learning how to get the most from RapidMiner and apply it to business problems.    

By |2018-10-09T07:39:28+00:00October 9th, 2018|RapidMiner|0 Comments

Increase Membership and Revenue with Predictive Analytics

Credit Union Membership Increased by Data Mining I am speaking on Friday May 18th, 2018 in Savannah, GA at the National Credit Union Conference for Directors and Senior Management.  My session topic in on how to use predictive analytics to leverage your data to increase membership and revenue.   For financial institutions, the ability to determine cross selling and targeted marketing campaigns can be achieved by leveraging the data you already have.  In this session I leverage multiple technologies to put together a complete picture of member behavior, location and impact.  The image below (for my fictional credit union) shows how many members live in each postal code along with the opportunity of households in postal code.  This type of report can help you focus on areas to increase or decrease marketing to gain membership. Credit Union Member Penetration by Zipcode Session Abstract: Predictive Analytics can provide credit unions [...]

By |2018-05-18T08:46:10+00:00May 18th, 2018|Power BI, RapidMiner|0 Comments

2018 CIO Review Business Intelligence Solution Provider

CDO Advisors included in the Top 20 Business Intelligence Solution Providers We were just included as a 2018 CIO Review Business Intelligence Solution Provider. While data analytics solutions such as BI and predictive analytics have already emerged as an integral factor in bolstering the value chain of big businesses, the Small and Medium Businesses (SMB) are yet far from leveraging their impeccable benefits. The existing rumor that these services are highly expensive, and the challenge of poor data quality and sheer lack of awareness on progressive technology, have become the biggest blocks in preventing SMBs from availing data analytical services. Drawn by the situation, Derek Wilson, an astute entrepreneur endeavored on a journey to aid SMBs to extract maximum value from their data sets. Thus, saw the creation of CDO Advisors, the IT advisory and consultant firm, providing end-to-end data services ranging from data management, business intelligence, predictive analytics, to data [...]

By |2018-05-06T10:36:45+00:00May 6th, 2018|Data Mining, Power BI, RapidMiner|0 Comments

Marketing Customer Information File Overview

  Marketing Customer Information File (MCIF) Many financial organizations already have a custom customer view called a marketing customer information file.  Other industries have these as well, called by different name such as master patient index in the healthcare space.  These files are created through existing software or internal jobs to merge data from various sources into a master file or database.  This file contains relevant information on each customer from all systems to enable a holistic view of your customers interactions with your organization.  A sample MCIF data set would include information (one row per customer) such as: Customer ID Member Tenure Checking Account Indicator Saving Account Indicator ATM Card Indicator Number of Monthly Transactions by Type Auto Loan Indicator Home Loan Indicator Last Contact Date Number of Calls to the Contact Center In addition, any other information that can be used for marketing, sales, or product development can [...]

By |2018-03-27T08:05:03+00:00March 27th, 2018|Power BI, RapidMiner|0 Comments

Text Mining and Sentiment Analysis of SuperBowl LII

Sentiment Analysis of Tweets from SuperBowl LII Do you own a business and wonder what people aka customers are saying about your company?  Using text mining and sentiment analysis enables you to uncover the words your customers are using and the positive or negative sentiment of those words.  Text mining involves taking text from any source and breaking it down into the core words.   Sentiment analysis then applies the score to the words from -1 (the worst sentiment) to 0 (neutral sentiment) to 1 (highest positive sentiment).  The scores of all the words found create the overall sentiment for that encounter. To show this in action, I ran sentiment analysis on Tweets the day after SuperBowl LII (2/5/2018).  In one example, someone tweeted "Congratulations Philadelphia SuperBowl champions".  The word champion has a positive sentiment and has a positive sentiment score of  .70.  When you take all of the words in [...]

By |2018-02-06T12:55:17+00:00February 6th, 2018|RapidMiner|0 Comments

RapidMiner Market Basket Sample

RapidMiner Market Basket One method to create a targeted cross selling model is to use historical customer information and apply the prediction to existing customers.  In this example, I will be using fictitious customer account data for a financial institution such as a bank or credit union to create a RapidMiner Market Basket predictive model.  The Marketing department wants to send out targeted mailing and phone call campaigns to customers most likely to need an auto loan.  The available data includes information on each unique account along with flags that indicate use (1 used, 0 not used).  Other fields count the number of inbound calls, late payments, or number of months as a customer. Sample Financial Data Fields Account ID                                       10. Holiday Club Indicator Account Origin Indicator            [...]

By |2017-10-09T07:01:42+00:00October 9th, 2017|RapidMiner|0 Comments

RapidMiner Text Mining Example for Loch Ness Monster Sightings

RapidMiner Text Mining Text mining is a process to pull words from free form text fields, word documents, or other data sources.  Business use cases for text mining include scrubbing text from survey notes, claim notes, project notes or call center notes.  It is applicable in any situation that requires parsing text to find words and the number of times they appear.  These include uses in various business functions such as Risk Management, Customer Service, Project Management, or Knowledge Management.  RapidMiner text mining enables you gain insight from your business information and transform that into knowledge and actionable insights.  In this example, I will use data collected from the Loch Ness Monster sighting website.  Each sighting is posted with a date and free form text field.  The goal of this example is parse the sighting and determine the most common words by frequency from the information provided.         [...]

By |2017-09-26T07:05:29+00:00September 26th, 2017|RapidMiner|0 Comments