7 Web Analytics Metrics Mistakes B2B Web Analytics Mistakes and Pitfalls

Many times, statistics from web analysis can be misleading. It is all too easy to end up doing the wrong thing based on analysis of website statistics. Here are mistakes we have discovered for the following metrics:

1. Number of Leads by Keyword
   It is very important to measure conversions by keyword. This lets you know where to focus your marketing efforts. However, many B2B sites and especially those with high cost items have a relatively small number of conversions. In addition, the conversion may occur:
   • During the 2nd, or later visits when the original keyword used is now lost (due to cookie erasing, subsequent search using product name, etc)
   • By a coworker of the original searcher who lands directly on site since the URL is now known
   Since the numbers are small and may not be traceable to the original search, in many cases it is statistically invalid to decide on actionable items based on the number of leads by keyword. In those cases it is best to find proxies for conversions. Possible proxy candidates are time-on-site or engaging actions.


2. Percentage of Leads
   The percentage of leads from total visitors or any other segment is not always relevant. In many cases the profit from a B2B sale is big enough so that a good lead can justify the cost of a campaign even though the percentage of leads is small. The absolute number of leads is more important in this case. If you see that the percentage of leads from traffic is going down but the absolute number is going up--you can should go out and celebrate your success.


3. Absolute Number of Leads
   Measuring the absolute number of leads can mislead you. Yes, I know I just contradicted the previous point. Unfortunately you may sometimes notice the number of leads is decreasing. Before you panic, it is useful to measure the percentage of conversions. This is because there are many times when seasonal or other time-based factors impact total traffic. If we just measure absolute numbers and we see a 50% drop in leads we start to panic. But if you see that the percentage of leads is consistent over the last few months you know that the reduction in number of leads is due to a drop in traffic. You should then analyze to see if the reason is seasonal or other factors we have no control over. If it is seasonal, we can relax--although we should still try and improve the percentages. If it is a factor we do have control over, we can then start to panic and work to rectify the situation.
   
   It is useful to measure percentage of conversions to use as an early warning sign, however our main goal should be to increase absolute conversions until the expense of increasing them outweighs the profit.


4. ROI
   This important metric has great PR but it is undeserved. As long as you are profiting from a campaign, the return on investment should not be use to eliminate ad campaigns. You can use it:
   • If you need to reduce your advertising budget this metric then becomes necessary in order to guide you to which areas it is best to reduce the budget.
   • To measure your optimization efforts
   
   
5. Number of Downloads
   Not all downloads are created equal. Whitepapers are typically downloaded earlier in the sales cycle. In addition, you may get many non qualified people downloading the white paper who are interested in the subject. Data sheets, on the other hand are usually downloaded by people later in the sales process and who want to see detailed specifications of your product.
   By measuring downloads you are lumping these and other different segments together. Best to measure whitepaper downloads separately from data sheet downloads. An upsurge in white paper downloads in October may be because students are studying the subject described in your white paper. On the other hand, an upsurge in data sheet downloads is usually great news--unless you find out that it is your competitors doing all the downloading.


6. Using Numbers that are Statistically Valid
   In many cases metrics do not have enough information to be statistically valid. Unfortunately there is a tendency to want to come to conclusions fast. This could be because:
   • You want to prove something and are over eager to bring the testing (with the results you wanted) to a conclusion
   • There is pressure to present actionable items to others
   Avoid the pressure. I have seen many tests where the results flip flop once or twice before the numbers are valid.


7. Experience and Knowledge.
   Numbers are great and no one loves them more than me. However, they are just numbers and have many disadvantages:
   • There is still a lot of information they don't include. For example they don't explain why people do things
   • There may be mistakes in the data
   • The conclusions may not make sense and by being stubborn and digging deeper you can usually find the reason
   • In addition, they can be manipulated to prove preconceived ideas-sometimes unconsciously. As my seventh grade math teacher said: Figures don't lie, but liars figure
   
   
   These are some of the web analytics pitfalls and mistakes we have come across. I am sure there are many more. If you have any, I would love to hear from you.