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.
I’m not telling you something new if I say the demands on IT from business are ever increasing. Certainly the last few years, business is looking more and more towards IT as a big part of the solution to their business problems.
The amount of API traffic increases every day. But it’s no longer just the known players such as Google, Amazon, Twitter, Facebook and Salesforce (the API Billionaires Club) that will produce the related massive amount of data. Cisco estimates that by 2020 37 billion smart ‘things’ will be capable to connect with an API. The Internet of Things is here! But how should API-providers handle all these clients?