After Identifying Site Bottlenecks, Delivering Solutions, and understanding Measurement tools, we can now begin the deeper data analysis and provide tangible meaning to the data presented. Benchmarks also ensure that we are comparing apples to apples when looking at data. For our purposes, as we are looking for performance improvements for a site element, webpage, or the site entirely. Also, we are looking for key business KPIs, such as page views, clicks, average session duration, conversions, etc. Subsequently, we need to look over a period of time to create an average or a median metric benchmark that will reflect a business’s prior success.
For businesses that have seen significant growth in a short period, it may be difficult to look at, for example, a year-long web report and take a rough average.
Long-run Historical Benchmarking
Long-run historical benchmarking is a method of benchmarking that looks at a lot of past web performance data to create benchmarks. This method works with websites that have existed with web data capabilities for a reasonable period, websites that have seen steady web performance, or sites that do not directly drive most business growth/sales. To create an annual performance benchmark, a company can compare the number of years’ data and create an average/median benchmark for web metrics. It is key for an annual benchmark to be compared against other years to account for any external variables that affect web performance.
This method is the easiest method to create benchmarks for web performance because they do not require intensive calculations or models, and these benchmarks can easily be created even just by looking at graphs. If a business is focused on e-commerce or it is a major channel of business growth, then the business will obviously make constant efforts to increase web performance through marketing efforts. The second caveat for this model is that if the first round of solutions delivers a positive change in web performance, then the first version of the benchmark will no longer be relevant, and another benchmark will need to be developed. But, because the “new performance” values are relatively new and there is not enough data to measure through an entire year or possibly even for a month.
Thus, if there is a significant change in performance after the creation of the first benchmark, new data will need to be analyzed to create a new benchmark.
Forecasted Growth Rate Benchmarking
Forecasted growth rate benchmarking is another method to create benchmarks for web performance. For example, companies with constant site performance growth, B2C businesses, and sites that drive most business growth/sales will benefit more from using this model. The specific KPI that will show a high correlation with changes in website performance depends on business intelligence analysis, and businesses will need to invest their own time into finding the most relevant KPI.
Insert a trendline with the following conditions
Now, with a trendline that now closely mirrors the empirical data, we can use the trendline equation to compute a KPI growth rate. By taking the function’s derivative, we can find the KPI growth rate. For functions that have an order greater than two, you will notice that the independent variable will still exist in the KPI growth rate, and that is completely normal. With this KPI growth rate, we have developed a benchmark that your solution deliveries should outperform.
First, creating an assumption that a business KPI and web performance is a very large assumption, and there is very little research to prove this assumption can be made soundly. Second, this model only works for companies that are B2C and depend on online sales to drive growth. Now that we have understood the two foremost methods of creating benchmarks, we can now try to assess meaning from the solution delivery data. If we can see that, after the time that the solutions were implemented, that there was a clear positive change in performance, then it can be concluded that the solution delivery works.
But most times, there are 2 drawbacks to trying to analyze website performance data. First, it is very rare to see an obvious change in site performance simply by optimizing a website’s front and back end. Second, it is very difficult to see obvious changes in performance if the website has very low page views. Arguably, that is a very significant increase in performance.
The key to understanding the data is to ensure that any changes in performance are sustained and consistent over a reasonable period most times