Interpreting Google Analytics
In this post I will assume that you have already set up Google Analytics and added at least one goal to your website, (read this post if you haven’t). Realistically you will need to wait a little while in order to collect enough data to be able to make meaningful interpretations.
Once you feel like you have reached this stage, the aim of this post is to show you how to get the most out of your analytics account by effectively interpreting this data to guide decisions regarding digital marketing and website optimisation.
Although if you hover over each of the metrics a box with a definition will pop up, I’m firstly going to explain the significance of these. I’m then going to go into more detail about conversion funnels and how you can use these to highlight the areas of your ecommerce business that can be improved.
Uses for Analytics
When you first bring up a report, Analytics will give you an overview for the time period selected. The most useful screen to begin looking at in my opinion is the “audience” overview. This will show you amongst other things, the overall number of sessions, users, page views and your bounce rate.
Whilst this information is a great starting point, ultimately you want to be able to break this information down to find out possible reasons for changes in the performance of your website. Clicking the “add segment” box allows you to build a chart and select up to 4 sources that you wish to compare.
At this point, if you are using Google AdWords then it is very useful to link your Adwords and Analytics accounts to help evaluate your paid search strategy. By monitoring traffic sources on a regular basis, you will get a clearer understanding of which actions that you are taking for your website are working and which aren’t.
For example, if there is a significant increase in paid traffic then you will want to see whether this correlates with an increase in conversions. If it does then you will probably have the confidence to spend more on AdWords, however if it is not then you may want to either consider making changes to your PPC strategy or to focus on organic customer acquisition methods.
In the fairly common scenario described above, Analytics will show you who your most profitable and engaged visitors are. The best thing to do at this stage is to explore the “audience” and “behaviour” metrics a bit more in order to build a picture who you should be targeting.
Likewise, if blogging is a significant part of your digital strategy then it is important to be able to evaluate its ROI. To do this, go to “behaviour”, “site content” and have a look at which landing pages have the highest number of new users entering through them and which ones are leading to conversions. This should be a useful guide when it comes to formulating your content strategy for the future.
(Please note that whilst conversion funnels and sales funnels are defined separately, conversions being defined as a non-transactional goal and sales defined as a transactional goal, because the principles are the same I will use the terms interchangeably).
Whilst just a brief overview of your Analytics reports is worth doing to highlight the general performance of your website and to keep an eye out for changes, If you are getting high levels of traffic and wish to delve deeper then you should find that conversion funnels are one of the most useful tools available when it comes to optimizing the performance of your website.
Firstly, in order to view your funnel visualisation reports, in the reporting section of your UA account, go to “conversions” and then “goals”.
You will see a funnel for every stage (page) that you have set up for a given goal. The number and percentage on each funnel is very useful for seeing which pages on your website have the highest drop off rate.
A common error that some people make at this stage is to confuse the number of unique page views at each stage of the funnel as the number of visitors who made it to that stage.
Just as a reminder, a unique page view is defined as the number of unique visits during which a page was viewed. The reason why these are different is that one visitor can view the same page multiple times during multiple sessions, thus generating multiple unique page views.
How useful the funnel will be depends on how predictable the conversion completion routes are for your website. For the example, I am going to use a typical sales funnel which is likely to be similar to the first goal that most merchants set up.
Home page > Catalogue > Product page > Add to cart > Checkout > Order complete
Analysing the Funnel
If you set your home page as the entry to the funnel then your drop off rate at this stage is likely to be reflected in your overall bounce rate (the % of users who leave the website without interacting with it). What constitutes an acceptable bounce rate will depend on the merchant to some extent, however around 50% is generally considered good.
If you have a high bounce rate for your paid traffic then this signals that your AdWords campaign needs urgent attention to increase the relevance of your keywords and copy.
Generally, the recommendation to improve the drop off rate at this stage is to ensure that your homepage looks fresh and has appealing content, as well as making it instantly clear to visitors that your website is easy to navigate.
In terms of improving the performance of Catalogue pages, the same navigation and appearance recommendations apply as with the home page, however some of the drop off will simply be down to a visitor not seeing anything they want. Promotional banners can be a useful tool to encourage interest in certain products and should be experimented with.
When it gets to individual product pages, because you are allowed 20 funnels, merchants with large product databases will possibly want to leave specific product pages out as there are far to many possible routes to conversion for this to be helpful, e.g.
Home page > Catalogue > Add to cart > Checkout > Order complete
This way you can still compare the performance of different category pages, however you may want to look at page value in Analytics and create funnels for some of your best and worst performing pages. Diagnosing low value pages may highlight some of your product pages that are not keyword optimized which would explain them not ranking highly in search engines. For on page optimisation of product pages, having customer reviews for the product is great for reassuaring visitors as they approach the checkout stage.
Cart abandonment is unfortunately common and can be highly frustrating for merchants. 29 different studies found an average rate of 68.07%.
You don’t need analytics data to conclude that friendly cart abandonment emails, perhaps with a 10% discount code are a great idea for getting visitors to return. If this rate becomes significantly higher than this then it could point to issues such as hidden or excessive costs for the visitor to complete the purchase.
This is why it is always recommended to be as transparent as possible with total costs, even if you are unable to offer subsidised shipping. The most common reason however is that the checkout process is too long winded, in which case ensuring that only essential details are required for the visitor to complete the purchase is a no brainer.
Hopefully this post has been insightful in terms of beginning to use Analytics as a decision making tool for your website. In my opinion, for small ecommerce businesses it is definitely worth monitoring regularly and continuing to learn from, even if you don’t find it to be a revolutionary marketing tool straight away.
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