While this post is aimed at Web performance, the curse of the single metric affects our everyday lives in ways that we have become oblivious to.

When you listen to a business report, the stock market indices are an aggregated metric used to represent the performance of a set group of stocks.

When you read about economic indicators, these values are the aggregated representations of complex populations of data, collected from around the country, or the world.

Sport scores are the final tally of an event, but they may not always represent how well each team performed during the match.

The problem with single metrics lies in their simplicity. When a single metric is created, it usually attempts to factor in all of the possible and relevant data to produce an aggregated value that can represent a whole population of results.

These single metrics are then portrayed as a complete representation of this complex calculation. The presentation of this single metric is usually done in such a way that their compelling simplicity is accepted as the truth, rather than as a representation of a truth.

In the area of Web performance, organizations have fallen prey to this need for the compelling single metric. The need to represent a very complex process in terms that can be quickly absorbed and understand by as large a group of people as possible.

The single metrics most commonly found in the Web performance management field are performance (end-to-end response time of the tested business process) and availability (success rate of the tested business process). These numbers are then merged and transformed by data from a number of sources (external measurements, hit counts, conversions, internal server metrics, packet loss), and this information is bubbled up in an organization. By the time senior management and decision-makers receive the Web performance results, that are likely several steps removed from the raw measurement data.

An executive will tell you that information is a blessing, but only when it speeds, rather than hinders, the decision-making process. A Web performance consultant (such as myself) will tell that basing your decisions on a single metric that has been created out of a complex population of data is madness.

So, where does the middle-ground lie between the data wonks and the senior leaders? The rest of this post is dedicated to introducing a few of the metrics that will, in a small subset of metrics, give a senior leaders better information to work from when deciding what to do next.

A great place to start this process is to examine the percentile distribution of measurement results. Percentiles are known to anyone who has children. After a visit to the pediatrician, someone will likely state that “My son/daughter is in the XXth percentile of his/her age group for height/weight/tantrums/etc”. This means that XX% of the population of children that age, as recorded by pediatricians, report values at or below the same value for this same metric.

Percentiles are great for a population of results like Web performance measurement data. Using only a small set of values, anyone can quickly see how many visitors to a site could be experiencing poor performance.

If at the median (50th percentile), the measured business process is 3.0 seconds, this means that 50% of all of the measurements looked at are being completed in 3.0 seconds or less.

If the executive then looks up to the 90th percentile and sees that it’s at 16.0 seconds, it can be quickly determined that something very bad has happened to affect the response times collected for the 40% of the population between these two points. Immediately, everyone knows that for some reason, an unacceptable number of visitors are likely experiencing degraded and unpredictable performance when they visit the site.

A suggestion for enhancing averages with percentiles is to use the 90th percentile value as a trim ceiling for the average. Then side-by-side comparisons of the untrimmed and trimmed averages can be compared. For sites with a larger number of response time outliers, the average will decrease dramatically when it is trimmed, while sites with more consistent measurement results will find their average response time is similar with and without the trimmed data.

It is also critical to examine the application’s response times and success rates throughout defined business cycles. A single response time or success rate value eliminates

• variations by time of day
• variations by day of week
• variations by month
• variations caused by advertising and marketing

An average is just an average. If at peak buiness hours, response times are 5.0 seconds slower than the average, then the average is meaningless, as business is being lost to poor performance which has been lost in the focus on the single metric.

All of these items have also fallen prey to their own curse of the single metric. All of the items discussed above aggregate the response time of the business process into a single metric. The process of purchasing items online is broken down into discrete steps, and different parts of this process likely take longer than others. And one step beyond the discrete steps are the objects and data that appear to the customer during these steps.

It is critical to isolate the performance for each step of the process to find the bottlenecks to performance. Then the components in those steps that cause the greatest response time or success rate degradations must be identified and targeted for performance improvement initiatives. If there are one or two poorly performing steps in a business process, focusing performance improvement efforts on these is critical, otherwise precious resources are being wasted in trying to fix parts of the application that are working well.

In summary, a single metric provides a sense of false confidence, the sense that the application can be counted on to deliver response times and success rates that are nearly the same as those simple, single metrics.

The average provides a middle ground, a line that says that is the approximate mid-point of the measurement population. There are measurements above and below this average, and you have to plan around the peaks and valleys, not the open plains. It is critical never to fall victim to the attractive charms that come with the curse of the single metric.

In the realm of Web performance measurement and monitoring, one of the eternal and ever-present questions remains “What is fast?”. The simple fact is that there is no single answer for this question, as it it isn’t a question with one quantitative answer that encompasses all the varied scenarios that are presented to the Web performance professional.

The answer that the people who ask the “What is fast?” question most often hear is “It depends”. And in most cases, it depends on the results of three distinct areas of analysis.

1. Baselining
2. Competitve Analysis
3. Comparative Analysis

Baselining

Baselining is the process of examining Web performance results over a period of time to determine the inherent patterns that exist in the measurement data. It is critical that this process occur over a minimum period of 14 days, as there are a number of key patterns that will only appear within a period at least that long.

Baselining also provides some idea of what normal performance of a Web site or Web business process is. While this will provide some insight into the what can be expected from the site, in isolation it provides only a tiny glimpse into the complexity of how fast a Web site should be.

Baselining can identify the slow pages in a business process, or identify objects that may be causing noticeable performance degradation, its inherent isolation from the rest of the world it exists is its biggest failing. Companies that rely only on the performance data from their own sites to provide the context of what is fast are left with a very narrow view of the real world.

Competitive Analysis

All companies have competition. There is always a firm or organization whose sole purpose is to carve a niche out of your base of customers. It flows both ways, as your firm is trying to do exactly the same thing to other firms.

When you consider the performance of your online presence, which is likely accounting for a large (and growing) component of your revenue, why would you leave the effects of poor Web site performance your competitive analysis? And how do you know how your site is fairing against the other firms you are competing against on a daily basis?

Competitive analysis has been a key component of the Web performance measurement field since it appeared in the mid-1990s. Firms want to understand how they are doing against other firms in the same competitive space. They need to know if their Web site is at a quantitative advantage or disadvantage with these other firms.

Web sites are almost always different in their presentation and design, but they all serve the same purpose: To convert visitors to buyers. Measuring this process in a structured way allows companies to cut through the differences that exist in design and presentation and cut directly to heart of the matter: Show me the money.

Competitive measurements allow you to determine where your firm is strong, where it is weak, and how it should prioritize its efforts to make it a better site that more effectively serves the needs of the customers, and the needs of the business.

Comparative Analysis

Most astute readers will be wondering how comparative analysis differs from competitive analysis. The differences are, in fact, fundamental to the way they are used. Where competitive analysis provides insight into the unique business challenges faced by a group of firms serving the needs of similar customers, comparative analysis forces your organization to look at performance more broadly.

Your customers and visitors do not just visit your site. I know this may come as a surprise, but it’s true. As a result, they carry with them very clear ideas of how fast a fast site is. And while your organization may have overcome many challenges to become the performance leader in your sector, you can only say that you understand the true meaning of performance once you have stepped outside your comfort zone and compared yourself to the true leaders in performance online.

On a daily basis, your customers compare your search functionality to firms who do nothing but provide search results to millions of people each day. They compare how long it takes to autheticate and get a personalized landing page on your site to the experiences they have at their bank, their favourite retailers. The compare the speed with which specific product pages load.

They may not do this consciously. But these consumers carry with them an expectation of performance, and they know when your site is or is not delivering it.

So, how do you define fast? Fast is what you make it. As a firm with a Web site that is serving the needs of customers or visitors, you have to be ready to accept that there are others out there who have solved many of the problems you may be facing. Broaden your perspective and put your site in the harsh light of these three spotlights, and your organization will be on its way to evolving its Web performance perspective.