Week 4 HW #6 - Martha's COIN 72
Book #3 Web Analytics
Chapters 1-6 Option #A - Reports vs. Analytics
INSTRUCTIONS: Option A Option B Option C
Option A - What's the Difference between Web reporting and Web analytics?
Web Reference: http://www.kaushik.net/avinash/difference-web-reporting-web-analysis/Our textbook, on page 279, delineates five basic metrics that provide the information we need to analyze the performance of a commercial Website. This data includes sessions (visits), unique visitors, pageviews, time on site, and referrers. The next few chapters describe what this information is, and why it's so important. The authors also introduce us to the tools of analytics, described on pp. 267-68, which use 2 ways of collecting data - log file reporting, or Web bugs and JavaScript. However, knowing WHAT to do, and HOW to do it is not enough. A good Web marketer needs to ANALYZE the data that's collected.
Occam's Razor provides good examples of the difference between web reports and web analysis. What's even more important, it gives good visual examples to illustrate the difference. Let's start with the definition of Web analysis:
If you see words in English outlining actions that need to be taken, and below the fold you see relevant supporting data, then you are looking at the result of web data analysis...The job of web analysis mandates a good understanding of the business priorities, creation of the right custom reports, application of hyper-relevant advanced segments to that data and, finally and most importantly, presentation of your insights and recommended action using the locally spoken language.
The author of Occam's Razor, Avinash A. Kaushik, makes some interesting observations about the typical "dashboard" presentation style of Web reports (see http://www.kaushik.net/avinash/the-action-dashboard-an-alternative-to-crappy-dashboards/). Kaushik asserts most analytics reports are not useful to most business executives for the following reasons:
- Most reports leave all of the interpretation of the data to the executive, who is unfamiliar with the meaning of the information;
- Most executives want insight from, but don't trust, the number crunchers, the data providers;
- Most data analysts work in isolation; they do not have a wide-ranging view of the needs of the business or the nature of the culture;
- Most creators of analytical reports are "outsiders" - they do not have the understanding of the deeper needs of the system, nor are they able to influence it.
Summarizing: "If you want your Executives / Customers to take action, you have to give them information and not data."
How to do that? The solution proposed is two-fold:
- Limit what you give executives to 3-5 pieces of analytical data; and/or,
- Structure the data in such a way that they can "see" what is actionable, and what needs to be done.
To achieve this later solution, Kaushik constructed a 4-part template comprised of a box with 4 quadrants. This techique "visualizes" the data, but it also creates a structure of meaning to it. It provides the executive with an idea of what's happening, what this means, what is being done about it, and what the results are. Specifically, the 4 quadrants are laid out like this:
- the data in the top left,
- key trends and insights in the top right,
- actions/steps being taken in the lower right, and
- impact on the company on the lower left.
The crux of the matter is this - data is meaningless without an understanding of its meaning! That sounds like a tautology, but it does convey the essence - data needs to be interpreted, and placed in context, before one can act on it. It is not enough to know something; one must also know what it means. Most of all, one needs to know what to DO with it; how it affects one's operations; how others understand it. Above all, the executive decision-maker wants data that gives him/her a solid basis upon which to decide. And this entails a lot more than simply data. It requires interpretation. The analyst presents the data, the analyst interprets the data, but unless the analyst also illustrates the meaning of the data - and provides authoritative, analytical application of the data, the analyst is failing at her/his job. What they have provided the executive is worthless. It may look good on paper, but if it doesn't lead the company in the right direction, at the right time, in the right way, it is indeed "just a bunch of numbers". top