The ultimate guide to sentiment and emotion analysis
Sentiment and emotion analysis enables businesses to keep track of how customers feel about products and services. This blog shares how these insights...
The Team at CallMiner
October 01, 2019
Data mining is the process of collecting, assimilating and utilizing information for anomalies and/or benefits. The data is typically collected from large databases and processed to determine patterns and other correlations. These patterns can be statistical; an example is that the unemployment rate can be derived and predicted using data mining. Correlations can also be used in the realm of machine learning. For instance, businesses sometimes use data mining to construct machine learning programs to predict customer behavior.
The uses of data mining are vast. While it’s not an extensive list, here are some broad business-centered benefits of data mining:
There are two main types of data mining: predictive and descriptive. There are a couple of main techniques for each of these mining operations.
Predictive data analysis, as its name suggests, aims to forecast outcomes based on a set of circumstances. The most common predictive data mining techniques include regression and classification:
(Source: Credit Karma)
Descriptive data analysis relies on historical data to understand trends and evaluate changes over time. The most common descriptive data mining techniques include association rule and clustering:
Data mining tools run the gamut from simple to complex, open source tools to comprehensive enterprise-grade platforms capable of complex analysis. To capture the most relevant data needed to drive informed decision-making, many companies turn to sophisticated data mining and analysis tools. A SaaS-based engagement and speech analytics platform, CallMiner Eureka offers multi-channel text and speech analytics, enabling you to capture data from every customer interaction, regardless of channel – that means phone, email, chat, social media, surveys, and more.
A robust platform like CallMiner Eureka enables the capture of both structured and unstructured data, allowing for the capture and integration of customer dialog, customer sentiment, and agent performance with other data gleaned from sources such as chats and email for data mining and analysis. Powered by the Eureka data mining engine, its comprehensive, AI-driven platform offering a complete range of customer intelligence solutions from real-time to post-contact analysis to meet the demands of modern enterprises.
To get the highest-quality data and make the most of it, follow these expert data mining best practices.
“If you don’t deploy your model into the frontline and use it to affect your business’s performance in some way then you have spent a lot of time and expertise on an interesting research project that’s had no practical impact whatsoever. Make sure that you have clear deployment routes in mind right from the start. You need to ensure that Marketing can use your cross-sell model, that Contact Centre staff can see your churn risk scores, that your acquisition modelling is being applied to new prospect campaigns. If you don’t ensure your models are deployed then you’ll never be able to demonstrate the power of your work.” – Rachel Clinton, 9 tips for effective data mining, Data Science Central; Twitter: @DataScienceCtrl
“A holdout sample is used as a reference sample to judge whether the model you are working upon has the ability to predict future scores. This is based upon a sample of observations withheld from estimation to yield a predictive model. Preparing a handout sample ensures that a model just for point-of-sale is not built which is based upon a defined set of data only. Hence, it provides a robust way of building up a model.” – 6 tips on successful Data Mining, New Gen Apps
“It’s always a mistake to skip over the data preparation step in the CRISP-DM model. Even well-tended data warehouses are likely to have fields with missing data, duplicate records or other errors. And these days, many data miners are accessing raw and unstructured data from data lakes or other repositories. Cleaning the data and getting it into a usable state is an absolute must. In this step, it’s also vitally important to think through what the data is saying and apply common sense rather than just accepting the data as is. For example, if your data includes records for pregnant men or people who are listed as parents but have zero children, you need to go back and figure out where things went wrong.” – Cynthia Harvey, Big Data Mining: 9 User Tips, Datamation; Twitter: @Datamation
What are your most important data mining techniques and best practices?
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