A previous series of blog posts on Marketing Analytics offered an extensive overview of the available analytical techniques for marketing and their added value. Some examples of these techniques included market basket analysis, customer segmentation and churn prediction. A conclusion reached in these blog posts was that data analytics are the ideal extension to traditional marketing: based on data, we gain insights into (potential) customers and their behaviour, so that we can target them in an even more personalised manner.
The first of a two-part blog post zooms in on an important category of marketing analytics: Geospatial analytics or Geographic analysis. What can geographic analysis signify for your business? What is the added value of using this analysis? In a second blog post, we will explain the more technical aspects, show you how you can start up this analysis with the help of the open-source software R (the R Project for Statistical Computing) and provide a complete step-by-step plan of our own workflow.
Curious on how you can integrate interactive Visio diagrams in your Power BI reports? Check out this blog post for a detailed walk-through. To top it off, we'll also include the brand new what-if-analysis feature in the mix.
In a big data world, data-driven decisions and Internet of Things, Analytics is often needed to acquire data insights. However, when data scientists forget to use visualizations to communicate or explore information they are missing out on a valuable tool.
We live in a talent economy and now more than ever, companies are realizing that with great people comes great business success. With the rise of 'Great Place to Work' contests and the popularity of LinkedIn, HR departments have gained significant strategic importance over the past few decades. Companies have recognized that when they have a well-balanced and happy employee base, this reflects in every other part of the business.
Organizations often possess a lot of data that’s being stored in unstructured formats. Most of the time, this involves data that people were able to enter as free text, such as e-mails, call center logs, presentations, manuals etc. In these instances, analytics can help to access the value hidden within this data.