“Truth is a Pathless Land”

...but finding an effective solution to your business problem does not have to be. Business analytics landscape does actually appear so, with a myriad techniques and vendor tools in the market.

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The Analytics Compass Blog is aimed at two types of readers:

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Time series forecasting: understanding trend and seasonality

timeseries forecasting profiles resized 600

This article was contributed by Vaibhav Waghmare.

How Predictive Analytics Can Boost Your Social Media Campaigns

sentiment analysis and predictive analytics

This article was contributed by Jessica Davis.

10 mistakes to avoid while building time series forecasting models

top 10 mistakes in applying predictive analytics models resized 600

This article was contributed by Vaibhav Waghmare

The importance of translating analytics to business speak

translating analytics speak to business speak

Multiple linear regression analysis is one of the most versatile tools in the data scientist's arsenal. It is very easy to apply - any one with a basic comfort level in using MS Excel can do it - and by the same token very easy to overuse and/or abuse. Abuse comes from incorrectly applying the regression model as well as incorrectly interpreting the results from a correctly applied model. 

6 trend analyses to consider prior to time series forecasting

revenue trend lines time series forecasting data resized 600

(This article was contributed by Vaibhav Waghmare)

Analytics for manufacturing overhead: accurate labor cost tracking

labor cost tracking system for manufacturing overhead analytics resized 600

In an earlier article we described why it is very important for many manufacturing businesses to accurately track their labor costs. A large portion of the work in small contract manufacturing companies depends on tasks such as assembly and inspection. For small companies, automation such as robots is not really a viable option and they still depend upon human effort for such mundane tasks.

How to improve customer segmentation with text mining in 5 steps

customer segmentation using survey data

In a previous article we discussed how text mining can help with customer segmentation when used in conjunction with traditional survey analysis data. In this article we will demonstrate that doing so can significantly improve the predictability of models.

Time series forecasting in 4 simple terms for business users

time series forecasting basics resized 600

This article was contributed by Vaibhav Waghmare.

Combining power of R and RapidMiner for time series forecasting

time series input example resized 600

Handling time series analysis in a tool like RapidMiner requires advanced skills. Basically, one has to become very conversant with the Windowing operator and other "Series" extension tools, about 80+ different ones. While basic time series forecasting tools, such as exponential smoothing are available as built-in operators, handling advanced techniques like ARIMA, requires some extensive workarounds. There are certain aspects of RapidMiner which are "non-conventional", particularly for time series. For people who do not want to give up the traidtional way of doing time series, have no fear, RapidMiner will allow you to keep your conventional methods by allowing you to fully integrate with standard methods.

How text mining survey data can help with customer segmentation

customer segmentation and customer lifetime value slice

Understanding the needs of your customers is a critical aspect of business. This requires proper customer segmentation. There are many different approaches to segmenting customers: based on their behavior, based on their business structures (e.g. small, medium, large) or even based on the amount of revenues they generate for you, as performed in an 80-20 customer analysis.

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