Optimisation analysis. Know your visitor
If you had to sum up in a few words how to analyse for optimisation it would be this: know your visitor. Knowing nothing in an anonymous visitor environment would equal a spray and pray optimisation approach. Observing visitors and how they differ will facilitate the formulation of hypothesises for experimentation or recommendation for change. There's always an array of analysis that's possible, even in an environment that lacks user login data. So let's look at examples that are instantly available in your digital analytics system.
A good way to start making observations about visitor differences is to break down main KPIs and compare against dimension values. As an example if you had an online wine store, some key metrics would be: order rate, average order value, average order quantity and bounce rate. Some worthwhile dimensions for comparison are: geo, browser type, device, time to purchase, visitor frequency, checkout, traffic source and products purchased. Our wine store investigation would involve a thorough review of each KPI, so starting with order rate, we'd iterate through each dimension one level deep. When analysing ratios such as order/conversion rate, I would also look at absolute order numbers for giving context and gauging popularity, so you could think of this as an order rate and orders investigation. If we took 5 dimensions to analyse this would output 10 investigations:
|Dimension combination||Potential questions|
|Geo-> traffic source||How do these vary? Does natural or paid search convert better in the UK? Is traffic distribution across channels even or skewed towards a poorer performing traffic source? Maybe email is The Channel and some countries have fewer email addresses?|
|Geo-> checkout||If there's order rate differences across location, why could that be? How about: a popular payment method missing in a particular country or suspect translations?|
|Geo-> product||What's the look-to-book ratio for the top 10 selling wines and how does it compare across geo? What do these differences mean? Does homepage content properly represent what's most popular in each country?|
|Geo-> browser type||How does the user's browser impact order rate? Are Chrome users more likely to purchase? Could this be useful for personalisation?|
|Traffic source-> checkout||There will most likely be differences here, is there anyway to make changes to the checkout experience per traffic source?|
|Traffic source-> product||Are certain traffic sources more likely to purchase particular wines and can this be used for personalisation?|
|Traffic source-> browser type||How does browser conversion rate vary across geo? Are Firefox visitors in Germany more likely to purchase compared to Internet Explorer visitors in the US?|
|Checkout-> product||If the checkout order rate varies based on the product, why could that be? Do the descriptions or thumbnail images need a review in the cart?|
|Checkout-> browser type||Are there browser incompatibility issues? IE 8 causing problems again?!|
|Product-> browser type||Are visitors of certain browsers more likely to purchase certain wines? If so can this be used for personalisation?|
Once the above investigations are complete we do the same for the other KPIs and when exhausted we can go another dimension deep (when it makes sense), for example: Traffic source-> checkout-> geo. I find it helpful to export the data to Excel where you can apply conditional formatting, the average order value analysis below asks questions straight away; why are the French so financially frugal? Don't they like our fine British wine?!
You will have more dimensions depending on your business which all play a role in better understanding who your visitors are and how they behave. I find this systematic approach helpful otherwise it can seem overwhelming on where to start.
I hope this was useful and thanks for reading. If you have any comments, questions or feedback please leave them below. And you can follow new posts on Twitter, Email or RSS.