When I asked analytics expert Barry Mann about any good books on analytics, his advice was short and sweet: “simple, read Web Metrics by Jim Sterne“. Even though this book was published back in 2002, Barry recommended this as a great textbook on analytics. And so it proved to be. “Web Analytics – Proven methods for measuring web site” does a great job in distinguishing between the things one wants to measure (and why) and the tools one can use for measuring.

These are the areas of the book that I found most helpful:

1. Division of analytics tools – Sterne references a useful way of dividing tools into four levels. This breakdown comes from Gartner and offers a handy way of looking at available analytics tools (see Fig. 1 below).
2. The importance of log files – A great tool to start with is log files. Reading Sterne’s chapter titled “Sawing Logs” really helped me in asking the right questions before starting to look at the log files of a website (see Fig. 2 below).
3. Understanding your visitor data – The chapter in the book which talks about “valuing visitors” is great in helping you think about different type of visitors and their – expected or desired – behaviours (see Fig. 3 below).
4. Measuring stickiness – For commercial websites, the question of stickiness is one of branding and persuasion. First, how can we get people to stay longer with our brand? Second, when people engage with our brand, are we leaving the right impression? Sterne provides some useful formulas to calculate stickiness (see Fig. 4 below).
5. Calculating conversion – Sterne helpfully describes “conversion” as “whatever moment of commitment happens on your site”. He then goes to elaborate on a number of related metrics: navigation and search impact conversion, depth, recency and frequency, abandonment and checkout (see Fig. 5 below).

Main learning point: Web Analytics by Jim Sterne is a great book for anyone who is either new to the world of analytics or wants to build on a basic understanding. Sterne spends a great amount of time talking about the ‘why’ of certain online metrics and how to best measure them, which I found incredibly helpful.

Fig. 1 – The Gartner Levels of Ambition – Taken from “Web Metrics” by Jim Sterne, Chapter 5, pp. 67-68

• Level 1: Monitoring – The focus is on website optimisation. You look at server logs to figure out how to get people to stick around longer and give them more of what they want.
• Level 2: Feedback – Here you pay attention to the site visitor and try to improve the user experience. You look at the amount of time people spend on the site, and you use visitor segmentation to alter the content for different types of visitors.
• Level 3: Leverage – The focus shifts from the visitor to the customer, and the goal is to increase customer profitability. Customer profiling, dynamic up-selling and cross-selling, and customer satisfaction all come into play.
• Level 4: Strategic – Now the spotlight is turned around to shine on the company itself in order to optimise the business model: channeling low-margin customers to the competition, tracking lifetime value, and getting into some serious business intelligence analytics.

Fig. 2 – Things that one can learn from looking at log files – Taken from “Web Metrics” by Jim Sterne, Chapter 5, pp. 67-88

• Search terms used – Use referer log files of the search terms that a person has typed into Google. The URL of the page they were on is recorded, and that URL includes the long string of data the search engine used to produce the page, including the search term itself.
• Most used entry and exit pages on a site – Server logs show the most used entry and exit pages on a site. These are the pages most people use as a door into a website and the last page they looked at just before they left.
• Number of hits – Log analysis tools like WebTrends provide a good overview of the number of site hits: (1) entire site, (2) average per day and (3) home page.
• Number of page views – Looking at (1) the number of page views (impressions), (2) average number of page views per day and (3) document views.
• Visitor sessions – Looking at (1) the number of visitor session, (2) average number of visitors per day, (3) average visitor length, (4) number of unique visitors, (5) international visitor sessions, (5) visitors from the United Kingdom and (6) visitors from unknown origin.
• Visitors – Looking at (1) number of visitors, (2) visitors who visited once and (3) visitors who visited more than once.

Fig. 3 – Different visitor types and behaviours – Taken from “Web Metrics” by Jim Sterne, Chapter 7, pp. 141-146

• Unique visitor – The easiest way to track unique visitors is to look at their IP addresses. However, what do you do when your server logs show that two visitors came from two IP addresses, but if they come from the same online gateway or corporate firewall, how do you distinguish between them? Cookies are the best way to work out unique visitors in this scenario.
• Return visitor – Placing a cookie on a visitor’s computer is the best way so far of telling one visitor from another and knowing if the visitor has been to your site before. Two big drawbacks to this approach though: (1) lots of people are annoyed by cookies and will disable them and (2) many corporate firewalls won’t allow cookies to go through the corporate firewall.
• Qualified visitor – A suspect is somebody who shares characteristics with your current customers. A prospect is somebody who has expressed interest in your products – perhaps by responding to a promotion. A qualified prospect is one who has the need, the desire, and the means to make the buy. WebTrends defines a qualified visit this way: “Visits by customers who are considered qualified as revenue generators. To qualify, a visitor must access specific pages on a web site that are specified by the system administrator.”
• Stale visitor – Qualified visitors eventually lose their qualifications when they don’t come back for a spell. The length of this spell is likely to depend on the type of product or service that one is selling.
• User – A visitor is visitor. They come, they look, they may event become qualified if they stay long enough and dig deep enough. But a user comes to your site repeatedly and for a reason.
• Churn – Churn measures how much of your customer base rolls over during a given period of time. Divide the number of users who fail to return in a given time period by the total number of users at the end of the time period and you’ve got your baseline.

Fig. 4 – Ways to calculate stickiness – Taken from “Web Metrics” by Jim Sterne, Chapter 9, pp. 169-171

• You can easily calculate stickiness by multiplying frequency (F) by duration (D) and reach (R) and come up with a benchmark for your content. You choose whether frequency is measured per day, per week, or per month. Duration can either be calculated in minutes or pages. Reach is a percentage of your total potentially interested universe.
• For example, Your site has acquired a total of 200,000 unique users. Over the past month, 50,000 unique users went to your site. These 50,000 users accounted  for a total of 250,000 visits (average frequency of 5 visits per unique user for the month), and during these visits the users spent a total of 1,000,000 minutes viewing pages on your site. Therefore:
• Monthly Stickiness = (250,000 visits/50,000 active users) x (1,000,000 minutes/250,000 visits) x (50,000 active users/200,000 total users)
• Stickiness = Total Amount of Time Spent Viewing All Pages / Total Number of Unique Users

Fig. 5 – Relevant conversion to consider – Taken from “Web Metrics” by Jim Sterne, Chapter 11, pp. 214-248

• With navigation and search impact conversion, it’s useful to look at the “first purchase momentum”. This will provide you with insights into the clarity of your navigation; what us the actual number of clicks-to-purchase and how does this compare to the minimum required clicks to first purchase?
• First purchase momentum = Required clicks to first purchase / Actual clicks to first purchase
• It can be helpful to look at depth – How many pages of your website did people look at? And at what level of detail? Did people look at any specific product detail?
• Recency and frequency are about looking at the relationship between visits and purchases. As Sterne points out, “Not all buyers are first-time visitors, and not all first-time visitors are buyers. What’s the relationship? What is the pattern of visits for an individual user.” Marketing professors Wendy Moe and Peter Fader wrote a paper in 2001, which looks at the ability to predict purchasing probabilities for a given visit.
• Abandonment – Sterne provides some very useful metrics in relation to shopping cart abandonment: (1) the ratio of abandoned carts to completed purchases per day (2) the number of items per abandoned cart versus completed transactions (3) the profile of items abandoned versus purchased and (4) the profile of a shopper versus a buyer. The overall abandonment rate is the number of people who commence but do not complete the buying process.
  
• Apart from talking about the aforementioned shop-to-purchase ratio, Sterns also look at yield which determines the effectiveness of a multi-step process where incremental costs aren’t available, such as creative / banner testing or the comparison of two paths leading to the same path.
• Net yield = Total Promotion Cost / Total Promotion Result
• Cost per conversion = Advertising and Promotional Costs / Number of Sales

Related links for further learning:

1. http://www.webtrends.com/products-solutions/analytics/
2. http://www.kaushik.net/avinash/excellent-analytics-tip6-measure-days-visits-to-purchase/ 
3. http://www.brucehardie.com/talks/ho_cba_tut_art_09.pdf
4. http://www.moblized.com/blog/simple-ways-to-fix-cart-abandonment