Site Optimization
Web Metrics Pt. 2 Tested, Section 2 (Analysis) ![]() |
| Web Metrics Pt. 2 Tested, Section 2 (Analysis) |
| Monday, 05 May 2003 | |
|
In this section, we move from data to analysis, from theory to practice. We focus on just two key spreadsheets, and two key lists. How can you cut through the confusion, simplify your analysis, and make the right pay-per-click marketing decisions?To help you analyze your pay-per-click returns, we have created a spreadsheet template that requires just three primary steps. This section of the report will make more sense if you download the PAY-PER-CLICK ANALYSIS TEMPLATE (For Retailers). (click here) Here is how to use this spreadsheet:
The spreadsheet comes with both a template and an example. You will need to use your metric tools to collect and enter the following six items:
If you have questions about how to use this template, just email: How can you cut through the confusion, simplify your analysis, and make the right content marketing decisions (for newsletters, white papers, etc.)?Seventy percent of the retailers who subscribe to our Journal also offer an email-based publication. (*4) Whether you are a publisher or retailer, you probably have some type of content offering. Conversely, if you are a publisher, you probably have some type of retail offering (books, subscriptions, etc.). This simple template should help you analyze the perceived quality of your content offering. (click here) Here is how to use this spreadsheet:
The spreadsheet comes with both a template and an example. You will need to use your metric tools to collect and enter the following ten items:
If you have questions about how to use this template, just email: mailto: How can you keep from miscalculating your ROI -- and seriously losing money? (6 Common Mistakes)Consider this an "intervention." It is all too easy to fall under the giddy influence of promising (but inflated) numbers, make incorrect marketing assumptions, and subsequently waste vulgar amounts of money. Here are six mistakes you do not want to make when calculating your ROI:
How can you protect yourself against reporting errors caused by your metrics program? (5 Common Problems)Users beware! No matter how much care you put into your analysis, sometimes the reporting tools skew your conclusions. Here are five potential dangers:
BONUS EDUCATION - LOG FILES 101 It seems appropriate to quote (again) the Marquis: "A Prince who will not undergo the difficulty of understanding must undergo the danger of trusting." If your whole concept of web metrics is based on vague understanding of log files, it may be worth your while to invest the next few minutes. We have endeavored to create a succinct, 688 word course... How do you understand your log files?Log files are big and ugly. A log file is a record of interaction between your web server and a client machine. Every time a client machine connects to your web server, the server writes a line to the log file. This is why log files are big: (1) If your server delivers one million pages, (2) if each page is made up of ten files, and (3) if each file is about 20 kilobytes -- your server will have to find, read and send 200 terabytes of data. One terabyte equals one trillion bytes. This is the equivalent of all the starts in our galaxy -- multiplied by ten. This is why log files are ugly: SAMPLE (ONE LINE) LOG ENTRY "2002-06-25 00:07:49 200.64.195.206 W3SVC201 GET/tabletools/ showprod.cfm DID=6&User_ID=1182745&st=8702&st2=-69508442&st3 =73707243&CATID=5&Object Group_ID=34 200 40930 747 80 Mozilla/4.0+(compatible;+MSIE+6.0;+Windows+NT+5.1) http://www.example.com/file.cfm" Do you really need to understand your raw log files? No. Still, you can learn the basics in the next 180 seconds, and this newfound knowledge could improve your social standing, your annual salary, and your conversational prowess at parties. Here is a sample extract from the above entry: 2002-06-25 00:07:49 - This is the date and time (Greenwich MeanTime) that the visitor requested a file from your server. 200.64.195.206 - This is the DNS of the person who asked for the file. You can use this number to find their domain name. GET /tabletools/showprod.cfm - This is the page (file) they requested from your server. 40930 - This tells you the server sent back 40,930 bytes. &DID through to ID=34 - This (painfully long segment) tells us the user ID, query, etc. 200 - This means the page request was successful. IF the server had been unable to find the file, this number might be at 404. Mozilla/4.0+ (compatible;+MSIE+6.0;+Windows+NT+5.1) - This shows us the page they were on when they made the file request. (It is the referrer.) If they had typed in the address, instead of clicking on a link, this URL would be replaced by a "-". In the Olden days, men of valor attacked these log files with ponderous, dull-edged spreadsheets. But today, there are sharp, efficient software programs to help you cut through the confusion. Why is it so difficult to interpret your log files?As you may have deduced from the previous section, it is tough to capture accurate metrics. But why? Why is it so difficult to get reliable numbers? There are several reasons. Here are just three:
ProLinkz Link Tracking Script: Ezine Promotion: Tracking the Results of Your Efforts: Ezine Readership Measurement Using ProLinkz: Does a Perfect Web Metrics Tool Exist?, Part 1 Does a Perfect Web Metrics Tool Exist?, Part 2: How to Interpret Web Metrics: One Metric Can Tell the Tale: Visitation Frequency: E-Commerce Metrics: Drowning In Your Own Data: Metrics Identify Problems, Not Solve Them: Click-Through Rate, R.I.P.: E-Newsletter Metrics: More E-Newsletter Metrics: Metrics Don't Replace Marketing Judgment: Monitoring Visitor Conversion Using WebTrends: Measuring the Value of Visitors with WebTrends:
(*1) Marquee of Halifax (1633-1695) "Of Princes," Political, Moral and Miscellaneous Reflections, 1750. (*2) This number is still somewhat arbitrary, as the true margin is yet to be fully discerned. (*3) If averaging your cost per click is dangerous, so is averaging your revenue per order. You cannot assume that the last period's average order will be the same for this period. The only safe way to calculate this number is to divide the total revenue by the total orders for the period you are measuring. (*4) This number is an estimate based on the last informal survey taken more than 12 months ago. Still, it is a useful indicator. |


