The key limitation, if I have understood things correctly, is that you currently can’t use segmentation within the API.  Which is fine when you are getting general numbers out but not when you need to create a segmented dashboard across a dozen metrics or so.  It also means you cannot get visits to groups of pages, e.g. visits which saw a product page.  However, there are a couple of workarounds.

For the first issue, you need to return to the old system of creating a profile per data segment.  So, as I have recently done, create a profile for New Visits, another for UK Visitors, one for Paid Search and so on.  Then, with your segments already created, you can easily extract top line numbers from each profile and combine to create that segmented automated report.

The second issue can be resolved through the use of goals.  Two key points to remember are goals can be created based on Head Match or Regular Expressions for page names and that they can only be triggered once per visit.  Given this, the number of goal conversions is suddenly equivalent to the number of visits in which a group of pages was viewed e.g. set up a goal for view a product page and the number of goal conversions is the number of visits in which a product page was viewed.

The API has not yet been updated with the upgrade to 20 goals so you can currently only use the API on the first 4 goals but hopefully that update won’t be far away.  Along of course, with the ability to access segmented data and also to extract the numbers from within funnel visualisations.  In the meantime, I hope these two tips are helpful.

Due to cookie deletion and multiple computer usage, it is difficult to get a true picture of the split between people who have never seen a website before and those who have.  However, recording whether the visitor had a cookie from this website previously does at least give an indication of this new/returning split.

What I like to be able to do is to segment out new visitors for a time period (week or month) and examine their behaviour on the website compared to visitors who had visited previously.  The new visitor segment should include all visits during that time period by these visitors, not just their initial visit.

Frustratingly, this information is usually not available as default in a web analytics tool unless you can segment at visitor level.  However, as long as you have one of the four metrics from New and Returning Visits or Visitors, you can calculate the other three.  And most tools will give at least one number.   As examples:

• Google Analytics gives New Visits and Return Visits
• SiteCatalyst provides Return Visits
• HBX contains Returning Visitors

The key to this is knowing that the first time a site is visited, that is both a new visit and a new visitor.  And as any subsequent visits by these people will be reported as a return visit, the number of new visits equals the number of new visitors.

With that logic in mind, it is simple to calculate all four metrics once you have a single one.  For example, assume that the tool available is SiteCatalyst (without access to visitor level segmentation via Data Warehouse or Discover):

• The number of Return Visits is available but none of the other three metrics
• Total Visits minus Return Visits gives New Visits
• New Visits equals New Visitors
• Total Unique Visitors minus New Visitors gives Return Visitors

And now it is easy to calculate the proportion of Visits that were New or Returning or to calculate the proportion of Visitors that were New or Returning.

The same principle can be applied to Google Analytics:

• New and Returning Visits is available (note that this metric is visits, not visitors as it is titled in the report)
• New Visits equals New Visitors
• Total Unique Visitors minus New Visitors gives Return Visitors

Of course, these numbers don’t mean that much on their own but do become more useful when trended over time or across different segments.

An interesting thing to look at can be the split in New and Returning Visitors for different time periods – day, week and month.  This can indicate the scale of the issue with cookie deletion, but more on that another time.

Most metrics, when viewed at daily level, contain a form of daily seasonality.  This is most clear in metrics such as visits, page views or sales which are absolute numbers.  There is a re-occuring pattern throughout the week with peaks and troughs on the same day/s each week.  An example of this pattern can be seen in Figure 1 below.

While this makes any chart pretty to look at, it makes it difficult to really identify trends or spikes in the data.  Is a data point high because there was a spike or because it was a Monday?  It is school holidays but should the number of visits on that Sat really be that low?  And of course, what day did we start to see traffic decline from and how much of a change is it really?

Figure 1

A common method used to remove daily seasonality is to smooth the line out using a moving average.  As it is a weekly pattern, a seven point moving average should lead to a nice smooth line.  Unfortunately, as can be seen in Figure 2, this means you get a nice smooth line, hiding most of those interesting spikes and step changes and general data trends.  You can see overall trends but you cannot pinpoint particular days when a change occurred.  It is also difficult to clearly identify a change immediately, as each day only contributes one seventh to each data point.

Figure 2

What I advise doing instead is to remove the daily seasonality from each data point, resulting in a line that is unaffected by what day of the week it is.  Using this method means that it is clear to see if the performance each day was good or bad. For example, in Figure 3, it can be seen that the relatively worst day for visits was actually the 25th Aug, even though visits for that day were higher than for other days during the reported period.  The technique for removing daily seasonality can be applied each day, meaning that you can identify and react to a change in performance immediately.

Figure 3

The difficulty then is in calculating the daily seasonality across a week.  This can be done properly using SPSS or a similar tool but I use a quick hack workaround in Excel that, while not 100% accurate, gets the job done.  The steps to calculate daily seasonality for a metric (using the examples of visits) are as follows, with the example displayed in Figure 4:

1. Extract historical daily visits data.  You will need at least 6 weeks, more if the period includes a known number of factors that could impact on traffic e.g. school holidays, public holidays, product releases, marketing campaigns, etc.
2. Reorder the data so that each column contains a single week and each row contains only data for a particular day of the week.
3. Recreate this table so but replace the visits for each day with the % that visits for that day contributed to total visits for that week.
4. Add two more columns to calculate the mean and median for each row of data.
5. Delete all weeks which contain days which don’t reflect the general pattern.  In this example, weeks 5 and 6 were deleted.  At this point, the mean and the median should be relatively similar for each day of the week.
6. The daily seasonality pattern is achieved by multiplying the daily mean by 7.

Figure 4

This daily seasonality pattern can then be used for removing daily seasonality for that metric for any day.  Simply divide the value for each day by the relevant daily seasonality in order to remove it.  I generally do this using a vlookup against the day of the week for each date.

Going back to the reason for web analytics, you can use this technique to clean data so that you can instantly identify good and bad days, whether this is historical data or just for the preceding day.  If you are using this for historical data, you can identify the interesting days to investigate further (play with by segmenting).  If you are using on an on-going basis, you can see instantly what performance was like for the previous day and if need be, investigate and react to a change accordingly.

Currently, in order to be able to do this sort of analysis, you need to extract the data into Excel.  Hopefully one day, web analytics tools will allows you to upload a daily seasonality pattern for a metric so that you can display the daily data with this seasonality removed.  And my dream is of a tool that would incorporate the ability to automatically create the pattern for any selected metric (with manual over rides for tweaking of course).

The other key use that I have found for a daily seasonality pattern is it can be used in forecasting daily traffic levels.  If you are able to forecast what the week’s traffic should be, this can easily be multiplied out using the daily seasonality pattern to forecast traffic at a daily level.

A copy of the Excel file containing all the data, charts and formulae used in the examples above can be downloaded here – Daily Seasonality File.

This required me to think about how web pages can be grouped or categorised.  While I am sure there are many ways to categorise web pages, the method I came up with was based on the purpose of the web page.  This means the same method can be used to evaluate the performance of, and calculate a value for, all pages within each category.  I am sure I will evolve this categorisation and evaluation methodology over time but it has given me a nice place to start.

The different categories of web pages that I have identified are:

Information

These are destination pages containing information that may be of interest to visitors. As there is no action to take on these pages, it can be very difficult to evaluate whether the page is performing well or not. Given the objective for the page is for the visitor to read the content, success can be defined as the visitors who spend at least XX minutes on the page or who exit the website from that page (assuming this is because they have found and read the information they were after).

Navigation

These pages contain links through to other internet pages, whether within the site or through to 3^rd party websites. These pages do not contain any direct information and instead exist purely to direct visitors onwards. Success is defined based on the visitor clicking on one of the specific links contained within this page (not the general navigation links).

Transitional

These pages are just one in a series of pages. It may be a set of pages containing information or one stage in a checkout process. Either way, the intention is for the visitor to arrive on this page from the previous step and then to proceed to the next stage in the process. Success is defined by the visitor going through to the next stage in this process.

Transactional

These pages contain one or more actions that it is desired the visitor takes. These could be anything from downloading a file to submitting a form to adding a product to their basket. Success is defined by the visitor taking at least one of the desired actions.

Interactive

These pages allow the visitor to interact with them in some way. It could be to watch a video or to use a calculator. Success is defined by the visitor interacting with the element on the webpage. Note that this just defines whether the web page is good at getting the visitor to interact with the element, not how well the actual element is performing.

Multiple Categories

It is quite possible for a web page to fall into multiple categories. An example of this would be the Travelex UK homepage. This is a navigational page that visitors can interact with and (ideally for Travelex) click on a button to add the currency exchange to their basket. The performance of this page can be measured in different ways to understand how it is performing as a navigation page, as a transactional page and as an interactive page. It is possible for a page to perform well in one category but not so well in a different category.

I have thought of an alternative view of a report which I believe would be a useful tool in evaluating the effectiveness of a page. This reporting view does not exist in Sitestat or Google Analytics and I suspect it probably does not exist within other web analytics packages either (please let me know if I am wrong). On the assumption it does not, can I please request that someone designs this reporting view for me.

Quickly first, some current methods of approximating the effectiveness of a page are:

• rating of the page by a sample of visitors (there can be issues with sample size and potential bias?)
• actions taken on a page (but what if there was no way to interact with the page?)
• time on page (only valid if didn’t exit from page but even then, was the visitor actually viewing the page the whole time?)
• actions taken by visitor during rest of visit or within specified time period (did viewing that page actually influence future actions?)

Another method of judging the effectiveness of a page is to base it on what the visitor did next. This can be done using a website navigation report (Navigation Summary in GA or Clickpath Explorer in Sitestat) or a Site Overlay. However it is currently only really practical and useful within a multi-page process e.g. the 3rd page of a 5 step checkout process. At this point, you know where the visitor should have come from and what they should be doing next – therefore can see if visitors are behaving as you expect.

For most other pages on the website, what is happening looks something like this diagram. There are a range of pages viewed immediately before and after the current page with one option in each category being ‘Entered Site’ or ‘Exited Site’. What can’t be easily seen is which of the previous pages link up to which of the next pages. And it is this kind of detail which could be useful in evaluating the performance of a webpage.

Imagine that your (small) site had been visited 9 times with the following being the clickpaths on your site.

1. A -> B -> X
2. A -> B -> X -> C
3. A -> B -> X -> B
4. A -> B -> X -> A
5. A -> B -> X -> X
6. A -> C -> X -> C
7. A -> C -> X -> B
8. A -> X -> A
9. X

Using the current reports for the page X, you would be able to see the following breakdowns for the previous and next page viewed:

Previous pages

• B – 50%
• C – 20%
• Entry – 20%
• A – 10%
• X – 10%

Next pages

• Exit -30%
• A – 20%
• B – 20%
• C – 20%
• X – 10%

This report is nice but doesn’t really tell you that much. Visitors came from a variety of pages and left to a variety of pages. However, if the next pages could be grouped, an alternative view of the ‘Next pages’ could be shown as follows:

Next Pages

• Exit – 30%
• Previous page – 30%
• Previously viewed page – 10%
• Current page – 10%
• New page – 20%
• Specified page – 0% (no page specified)
• Internal search results – 0%

This report now shows that the visitor went on the view a new page only 20% of the time, information that was not readily available in the current reports. The visitor returned to their previous page 30% of the time, possibly they are clicking on the back button due to unappealing content or because there is no clear indication of where to navigate to next on that page. For expected breakdown would vary for different types of web pages but this report could be useful in evaluating the performance of pretty much any page on a website.

Of course, for any report to be really useful, it needs to be segmented. Besides the usual segmentation options (traffic source, etc), I can imagine this report would be improved by first of all eliminating bounces visits for that page and then segmenting on whether the page was the landing page or not. Also, assuming there was an option for specifying a page or set of pages viewed next, this could be useful in understanding if the visitor clicked through to either target pages or the homepage from the page being evaluated.

While it is hard to definitely say without the report being in front of me, I believe a report of this nature would be useful in evaluating the performance of a webpage. Most importantly, I believe the report would help in making business decisions that lead to a company achieving its business objectives. And given that is (or at least should be) the requirement for a web analytics report existing, I think this would be a useful addition to the reporting suite for any web analytics vendor.

There are a lot of different factors that impact on traffic levels for a website.  Common big factors are public holidays and school holidays with traffic always affected on these days.  Typically there would be a significant decline and, while it is possible that these days were actually positive for certain websites, there is a clear correlation between these days and traffic levels.  What this means is that it is not traffic for that website that is being impacted but the number of people using the internet (and their behaviour) is different for these days of the year.

A factor that leads to a similar change in user behaviour but that is harder to understand, difficult to explain and impossible to predict is the weather.  I have had numerous discussion trying to explain to management that the reason that traffic was down for a day or weekend was simply because the weather was nice.  People sometimes want a complicated explanation when the real answer can be quite simple.  My theory behind this change in behaviour is that on warm sunny days, more people are going to be outside enjoying the day and doing so for a longer period.  A lengthy and expensive piece of market research should prove this theory – or you try could looking outside on a sunny day.

There are two really tricky aspects to defining the exact impact from the weather, these being the varying weather conditions across a country (let alone the world) and an inconsistent level of change to traffic from similar changes in the weather.

For the first, it is not a significant issue in the UK or similar sized countries as the weather can be judged to be relatively consistent across the whole country.  Yes there is a large variation across the UK but simply taking London temperature will give an idea of hot, warm, mild or cold for the country.  For larger countries like the US or Australia it is more difficult as a no single population centre cannot be used to represent the whole country.  In these situations, I would recommend taking a weighted average across 3 to 5 locations to get a feel for overall weather conditions for that day (or segment the traffic for different regional areas).

The second inconsistency relates to an inability to create a formula that relates temperate and traffic.  Similar weather conditions can result in different levels of traffic, all other factors being equal.  This can be explained using logical thinking to explain the variations in website performance and how it is impacted by the weather.  What I have seen is that the decline from a hot weekend is always higher for the first hot weekend of spring or a late sunny weekend (as it was in London this weekend).  The simple explanation for this is that more people are going to spend time outdoors and away from their computers when it is the first hot weekend after the winter or potentially the last hot weekend of the year.

If you are doing weekly or monthly performance reporting, keep in mind the option of including the temperature when reporting on the warmer months.  The best site I found for historical weather data is the Weather Underground website (given current media reporting, the name is a little unfortunate) – an example here is historical data for Sept for London.  The only issue is that temperatures are in Fahrenheit but this can easily be adjusted in Excel.

I generally have an excel sheet set up that contains all the historical data for these metrics. Additional metrics can be created by applying calculations to the original metrics with simple Excel formulae.The new data can be added as a new line in this spreadsheet with charts updated accordingly.

The report items that are required for this report is as follows (in my preferred viewing order):

• Unique visitors
• Total visits
• Total page views
• Average duration per visit
• Page views per visit
• New vs returning visitors
• Unique visitors per entry type

These report items have different default time intervals. As we are only looking here at single time periods, the time interval for each report item should be set to “Full period”.

If the data is going to be transferred into Excel, there is no need for charts, additional statistics or descriptions and as such, these options can be deselected. There is no harm in leaving them in the report but I generally like having the report simpler and cleaner.

The report item “Page views per visit” is used to calculate the bounce rate for the website. It is a frequency table containing the number of visits during that time period per each number of page views e.g. number of visits with 1 page view, number of visits with 2 page views and so on. As a bounce can be defined as a visit with only a single page view, the bounce rate is the percentage of visits where only a single page is viewed. Therefore only the top line of data is required and the “Maximum number of lines” can be reduced to 1.

This report can then be set up for either a weekly or monthly report (or daily although I would question how valuable this is) using the time periods “Last week” and “Last month”. At this stage, you should now have a nice report containing all the relevant report items to get a quick overview of the performance of your website.

If this data is being captured and trended in an Excel file, some basic calculations need to be performed to get all my basic site performance metrics. Frequency (visits per unique visitor) and page views per visit can be calculated by dividing the appropriate metrics. The bounce rate calculation is single page visits divided by total visits.

I would recommend calculating the proportions of new, returning and unknown visitors and the proportions of the different traffic sources (entry types). An understanding of the reason for a change in the traffic levels for a website can be gained from looking at both the absolute numbers and the proportions for each of these metrics.

So that’s the basics, I feel everyone and every website can benefit from looking at this data. For real insights, more detailed reports will be required but this should tell you if you need to look further and where you should look first.

As a quick summary, here are the metrics you should have if you have transferred the data to an Excel spreadsheet:

• Unique visitors
• Visits
• Page views
• Frequency
• Page views per visit
• Duration per visit
• Bounce rate
• New, returning and unknown visitors (the number of visitors and proportions of each)
• Entry type – Clickin, Search engine, External referrers, Direct Entry (the number of unique visitors and the proportions of each)

For the sake of this example, let’s assume that it is the number of visits to your site which has declined over the previous week. And knowing you would have quickly scanned your other key metrics for more information, let’s assume that they don’t provide helpful information (unique visitors and pageviews also dropped 10%, no change in the proportion of traffic from different traffic sources).

Given this, a good place to look for an explanation of a change in weekly data is at your daily data. This may quite quickly show the exact day that values changed (whether this is up or down) providing insight into the cause of the change.

I am an analyst with Excel consistly open. Whenever I go to manipulate data, I transfer it from whatever data source it can be found in (Sitestat these days of course) into Excel where I can easily play with it. I have created some basic examples in Excel using simple numbers to highlight alternative situations.

Your Basic Data

The first step is to run your visits data at daily level so that they can be examined. By themselves, the numbers don’t mean much, they need to have some context added to them. I have added additional data showing the week on week change for each day during the last week as well as charting the two weeks data. If nothing had changed, you would get data and a chart as per below, with this sample data following a fairly standard internet day of the week pattern.

Change 1 – Trending Down (or Up)

But for these examples, visits had dropped, down by 10% against the previous week. You need to know why visits have dropped from 877,000 two weeks ago to 789,300 last week, a decline of 87,700 visits or (in useful terms) a decline of 10.0%. In this first example, there has been a consistent decline during the course of the week. Every day was down 10% against the equivalent day in the previous week. In reality of course, there will be day to day variations but you can always see a fairly similar level of change during the week.

Change 2 – The Step Change

A trend like that above won’t often be seen for a big change, say > 5%. It is more common when you are looking at a 1% to 3% change. While these are small numbers, over a number of weeks they do add it. I have seen that trend most often leading into Summer or Winter as days get longer/shorter and hotter/cooler, with these conditions impacting on overall internet usage.

Instead what you will see is a sudden step change where a metric suddenly changes to a new level. If you are looking at absolute numbers, there will be a six to eight consecutive days where each day has a similar week on week change until the numbers settle down at their new level. If you are looking at a metric that is a ratio or percentage with a less obvious day of week pattern, the change can be seen even more clearly, for example the frequency may change from being constantly around 2.50 to being constantly around 2.85.

Note: I will show in a future post a simple way to remove the day of week seasonality from metrics like visits so that trends can be more easily seen.

In the example on the left, there was a clear step change on the Thurs (day 11) with visits dropping by 20% every day from there. There is commonly only a partial change on the first day of a step change with the factor that caused the change commencing from part way through a day (change to website or online marketing).

The beauty of a step change is that it can allow you to pinpoint the exact day (and sometimes the approximate time of day) that something happened that is impacting the performance of your website. Through this, it is easier to identify what was the cause of the change. Additionally, if a business case is required, it is not difficult to calculate the impact of this change.

It should be kept in mind though that if a permanant step change occurs part way through a week, the weekly numbers will decline (or improve) further the next week when they have a complete week at this new level. I used the term permanant step change as the most common step change is temporary, occuring every school holidays.

Change 3 – The Spike

Another common cause of a weekly change in a metric is a sudden one to three day spike in the metric, either up or down. In the example on the right, there was a one day negative spike in visits on the Thursday which resulted in total visits being down 10% week on week.

In a way though, despite their impact, spikes are less important. As be seen, every other day of the week had exactly the same visits as the previous week. There is no reason to believe that the big drop in visits will be repeated. The cause of this drop in visits should be investigated but as it has already corrected itself, there is no reason to believe it will happen again.

Potential causes of spikes are servers crashes where the website either goes down or everything is fine but data wasn’t being captured, spam producing fake traffic, online marketing dropping off possibly due to budget temporarily running out, public holidays, etc.

Summary

A vital part of understanding what could have caused a change in a metric for a week (or a month) is looking at the data on a daily basis. By comparing each day against the same day in the previous week (or if need be, an alternative baseline week) and by looking at the data visually, it is possible to get a feel for when the change occured. This aids in identifying the cause of the change as well as understanding whether it is something to be concerned for the future or not.