Segmentation

Segmentation is a key goal of many service providing companies and is a large component of both my research project and my work with Axtria. Segmentation is the process of dividing up clientele into subgroups based on available characteristics. It has uses that range from identifying prospective donors to targeting ads based on an individual's interests.

Groups can be segmented in a number of ways. Say for example you have a list of prospective donors and a list of past donors with a number of characteristics for each. You may know for both groups things like age, estimated income, connections with the organization, and many other variables. And you also have information on the donations of past donors such as amount given, frequency of gifts and things like this. Segmentation would work by trying to find a correlation between characteristics we care about (donation information) and other information about the donor base so we can know which potential donors would be more probable to donate so that more time can be focused on them.

Another example would be how Netflix uses segmentation. Netflix has an immense user base which they have a variety of information on from age, to categories of interest, to individual media that they found incredible or horrific. Thus, when new people sign up for Netflix and list their interests Netflix pattern matches them toward television shows and movies that they are most likely to enjoy. As customers watch and rate more Netflix has a larger data set and can make better and better suggestions.

Segmentation is but one small tool used in data analysis but it proves very useful when applied to the right circumstances.  

Excelling with Excel

The most prominent thought that has occurred to me many times since starting my internship is the fact that Microsoft Excel is a beautiful tool to work with. While far from perfect, the number of amazing, clever, and plain useful features it brings is astounding.

At its core Excel appears to be just a sophisticated spreadsheet: a tool for putting data in nice little rows for easy consulting with a lot of high-tech looking features that are too complex for actual use. But after a little instruction, the intricacies of Excel reduce labor and minimize pain.

Excel can do basic things like sum the items in a column or multiply the values in a row but it has much more power than this. In a table listing population of European countries it can color code the largest and smallest, tell you the average population, graph the distribution of population, and tell you how many country names include the letter 'o'. With a table of financial information it can tell you what attributes contribute the most toward revenue, which factors are nearly irrelevant and how to generate the most profit.

Excel is a wonderful tool to analyze data though it does have its limits. For exceedingly large data tables Excel begins to run very slowly. While its user-friendly graphics tend to help with understanding they also require computer resources based on the number of fields entered. But even with this drawback Excel is a highly useful tool for basic analysis and it erases much of the drudgery from data crunching.

The Wonderful World of Corporate

This week I discovered that for having two parents that have spent the majority of their lives working in the corporate sphere I am remarkably ignorant of the corporate world's functionality. And I guess that after a lifetime of limited exposure to the consumer aspect of business life that this should not be such a surprise.

Over the past week I've been exposed to a number of facets of the modern business. From lectures on employee efficiency to conferences calls with heads of IT I have learned much about the internal structure of modern corporations.

This is a key feature to understanding how businesses use data analytics. Many different departments work together to produce useful output. R and D must interpret data and identify significance, Engineering must figure out how to manufacture this change, Marketing must find a way to publicize this technology. All these independent sub-units must cooperate and listen to each other to develop a useful product.

Information sharing is crucial for cooperation and it brings many issues to the table. Even the simple communication of data is complicated. Data may be in Excel Spreadsheets while the analysts might want to look at it in R. Getting many people from many organizations on the same page is a difficult task and involves much communication so that services can be smoothly rendered. And all of these things coalesce to make the corporate network a spiderweb of interconnecting chaos.