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Technically, Google Analytics has nothing at all to do with SEO or organic rank, and here’s why:
Analytics is a piece of reporting software that is installed into your website. When a visitor arrives at your website, the software signals Google HQ (or wherever the data collection system for your Analytics account is stored) to say “here’s a visitor – we’ll update you with what this visitor does while here”, and obediently this software monitors the visitor’s every move on your site. For some visits, a visitor leaves soon after arriving. During other visits, a visitor navigates through a few of your pages, and then leaves. If your website has eCommerce, and Analytics is correctly configured to track eCommerce transactions, you may also get a report from Analytics as to what they bought, and how much it cost. Analytics is also fab at telling us how a visitor got to your site in the first place, because when a new visitor comes to the site a digital ‘handover’ occurs that tells Analytics some information on where the visitors came from. That also implies that if you sold something on your website you can attribute that sale to a particular source, entry point or type of visitor. Great information for making decisions about whether the business mechanics of your website is actually working.
None of this information tells us anything about how well your website ranked, or which page of search results the visitor found your website link on. Those are SEO features happening on Google’s website, and that information is not accessible by Analytics.
In days past, it was possible to connect a search keyword to a particular visitor. That meant you could see the result of your SEO work directly in Analytics and be able to tell if your website got business from organic search. For the last little while, Google has stopped sharing that information between their search and Analytics systems, so we now rely on some super sleuthing work to connect cause and effect. Did a particular search phrase in Google search result in a sale on your website? We don’t know for sure. Analytics can show the final result, but not connect that result with the exact cause. So now, we do SEO work, based on some fairly solid research outside of Analytics, and hope that it results in increased visitor rates and more conversions on your website. We can measure the cause through Google Search Console. We can measure the result through Google Analytics. We can’t absolutely prove the connection between the two.
So the boundary of where Search Engine Optimisation ends (at the entry to your website) is where Conversion Rate Optimisation starts.
What is Conversion Rate Optimisation?
Commonly referred to as “CRO”, conversion rate optimisation is the science of arranging your website content in a way that makes it more effective at turning visitors into customers. There are many different mechanisms you could use to conversion optimise your website. Examples are: well-placed calls to action, like a button with well-written label that tells the visitor what to do next, or perhaps a strong and clear benefit statement that tells the visitor how they could benefit from doing business with you. CRO is complex and sometimes completely unpredictable. There are specialist providers in the online marketing industry that make it their business to perform expert CRO work. They might not be the same person you hire to build your website, nor are they necessarily the same person you hire to execute your SEO or PPC campaigns. I suggest you always seek advice around this topic from at least a few sources, and hire the provider that tells you things that make complete sense to you. Like SEO, it’s not black magic, just an accumulation of good common sense and experience.
How does it work?
OK, I simplified a little when I said that Analytics is software in your website. Technically it doesn’t exist in your website at all, but what it does do is allow Google’s software to run on your site from remote, and send signals to and from Google’s database at certain triggers called ‘events’.
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What is an event?
Events occur on your website as a result of an action that the visitor takes, for example, when they click on a navigation menu and visit another page in your website. This triggers a new page to load and is seen as an event that gets reported to the Analytics database. Other examples of events may vary quite a lot. An event may be triggered when the user scrolls down the page – because as new resources for content later down the page are loaded, these are recorded as engagement events. Another example is clicking the play button on a video, or clicking the add to cart button on a product listing, or submitting a contact form. Events that cause a change in the URL of the page being viewed are easily tracked without much effort. Tracking events within a single page require a little more skill in implementation and may not be available in Analytics ‘out of the box’ so to speak. You may need help from a web developer to install certain kinds of event tracking mechanisms.
What to ‘Conversion Optimise’ for:
It’s simple really: Optimise for the events that result in business success. That might be many different things to different people, so take the time to make sure you have considered your goals and understand how they will affect your business.
Implemented well, Google Analytics can assist in determining the ultimate business measurement: Return On Investment. What did it cost you to get the visitors to your website? What did it cost you to convert those visitors into customers? The total cost of acquisition divided into the total revenue generated is your ROI.
As an SEO specialist, I often see clients shy away from investing in either SEO or CRO with the rationale that they ‘have a website and that cost them a pretty penny, so they will see what happens’. Well, a website without converting visitors is a wasted investment.
The real-time tab shows a sub menu for several segments being recorded in the last 30 minutes of activity. Use this to monitor live responses to breaking news, new posts, social media events, time-sensitive promotions or any other event that might cause a sudden change in user behaviour. There are limited observations you can make that can be connected to SEO-related activities in the Real Time section of Google Analytics, because the kinds of SEO that can create a response within this time might be far and few between. One example could be a video post with viral sharing.
Overview: Shows the current traffic, top referrals, top social traffic, top keywords, top locations, current pageviews and hits per second. SEO work typically can’t make any sudden changes to a website’s traffic, so usually anything observed here that’s out of the ordinary is going to be due to other promotional activity.
Locations: Narrows down the view of the data to just the locations element, showing where in the world people are visiting your website from, in order of frequency. You can use this tab to figure out how much global reach your promotion has and in which areas you are getting the most traffic from.
Traffic Sources: This tab shows how people arrived at your website, which might be from Google search, referral from another website, referral from a social media website, direct entry, paid advertising etc.
Content: Shows the individual URIs for any pages that are currently being viewed by a user.
Events: If you have events loaded in your website to measure specific interactions, then any recorded events from the last 30 minutes will show here.
Conversions: Measures your conversions over the last 30 minutes. Conversions may be counted as either eCommerce sales or goal achievements that are either monetary or non monetary.
The audience section of the Google Analytics report shows most of the metrics that beginners to Google Analytics tend to be interested in, although much of the data here still requires some expertise to interpret in a meaningful way. Data can be divided by segment (traffic sources, traffic types, devices etc) and by metric (bounce rate, sessions, page-views, session duration etc). Changes in metrics can be seen as positive or negative depending on the purpose of the website, so it’s not possible to provide a useful guideline as to how you might want to change your metrics over time. For example: The bounce rate may be at 37% and the goal of your website is to quickly provide information from the first page a user lands on. In other words, if the user ends up having to search further though your website for what they want, it decreases the bounce rate but fails the goal. A decreased bounce is not the indicator of improvement in this instance. If you need help with determining what segments and metrics can be used as useful indicators, get in touch with our team. It’s often very complex and may not reveal useful data until you drill down to a very tight segment.
Demographics: when enabled, collects user data as users are engaged in the website. This only works if the user data sharing is enabled by the user and they are logged in to their Google account. There is no other way that user demographics can be collected if the user wishes to remain unknown. At no stage can you use data from the demographics report to identify any individual user, because the website owner doesn’t have any rights over that information.
Interests: this report, as with Demographics, provides data from the user. Both the Demographics and Interests data collection must be enabled on the domain for data to be collected here. When it comes to SEO and optimising your website for a specific demographic segment, this is something that might be achievable but is immensely complex and would rely in a tie-up between demographics data and keyword data.
Geo: provides data about the location of the user. The user’s permission is not needed for sharing of this data because the world’s internet connections are already mapped to a reasonable level of accuracy. It’s not flawless, but the user’s IP address can locate them to a point in the world and this data is reported in Google Analytics down to region and town / city level of accuracy. It means that you can examine specific Geo segments to analyse your metrics from that Geo zone and perhaps compare them with how other Geo zones perform.
Behaviour: this data provides metrics on whether your website’s users are new of if they are the same user returning many times, how often a user visits and how long it was since their last visit, plus how long they stay for when they are visiting. The metrics in these segments will help demonstrate how good your website content is in engaging users. If the website is designed to provide quick answers, your success metrics will be somewhat different to a website that provides video content or long-copy articles. You might even have pages designed for different purposes, in which case your success metrics might need to be examined on a page by page basis.
Technology: this data reports details of the types of browsers, operating systems and ISP networks being used to connect to your website. It may provide valuable data subject to promotions you run and target markets that are technology dependent in some way. An example might be when promoting an Application for iOS where traffic from your Adwords are leading to bounces from Windows devices. That’s probably predictable but it shows that too many Windows users are clicking your Adwords ads and you might need to try to reduce clicks from Windows devices.
Mobile: reports on the split between device types. The overview shows the split between desktop, mobile and tablet which is useful in identifying device-specific issues with your website’s metrics. The Devices section provides a breakdown of actual mobile device types being used and their associated metrics.
Custom: set up custom dimensions in admin mode to view data controlled the way you want it. For Advanced users of Google Analytics.
Benchmark: compare your website’s on-site performance against others in your industry. This view shares data from other Google Analytics users aggregated into a benchmark view. It allows you to quickly and easily compare your traffic patterns with similarly-sized websites in the same genre. Using the data table, you can determine how your organic search referrals compare – which assists in assessing your organic strength in the search market. You can use the same table to identify your comparative weaknesses. This is one of the most useful reports in Google Analytics to gauge how well your overall digital marketing campaign compares to others.
User Flow: track how your website users are navigating through your pages with this report, and filter by a variety of segments. This report is useful to gauge which pages in your website might be losing visitors more than others. Valuable data you can use to assess and trial new page layouts or content to keep engagement levels.
This section is useful for determining the level of success you are achieving from your entire digital marketing campaign from a more holistic viewpoint. Measure where your visitors are coming from, and which channels are performing well against any individual dimension. If you have Goals enabled or are tracking eCommerce transactions, you can also determine which of your marketing channels are providing return on investment.
Channels: provides a breakdown of your successful lead source campaigns with metrics in a range of dimensions. The main table shows acquisition metrics, behaviour metrics and conversion metrics for each lead source.
Treemaps: generate a heat map style display to plot any two metrics for any channel group.
Source/Medium: compare the performance of your website in respect to any source and medium subset. For example, compare your organic traffic and paid traffic between two different sources, like Facebook and Google, or compare referral traffic from traffic sources like Yellow Pages and other business directories. This is useful for making budget decisions around ad spend on any given platform because you can drill down to which channel is providing return on investment. It can also help you pin-point opportunities for growth in underperforming channels.
Referrals: lists performance data based on traffic that is referred from 3rd party websites, not search engines. Technically, there could be some referrers noted here that do have search engines in them, but are usually for searching within their own content. A referral is any visit that came from such a site.
Link your Google Analytics property with your Adwords account to start measuring the success of your Adwords campaigns. Works equally for search or display network referrals from Adwords and feeds through valuable data.
Campaigns: determine which of your Adwords campaigns are referring the best visits and resulting in the most conversions per campaign spend. For eCommerce stores you will need your eCommerce tracking enabled and have the right tracking snippet installed on your website. Sales data is fed back to Analytics and can be shared with Adwords to refine bidding strategies or set automatic bidding for maximising conversion. For non-eCommerce you will need to set Goals and Conversions and can assign arbitrary or actual values to each to begin estimating profitability of your Adwords campaigns.
Treemaps: generate a heat map style display to plot any two metrics for any campaign, ad group or ad.
Keywords: refers to the keyword set being used in Google Adwords and doesn’t connect with your SEO work in any way, but you can start using the conversion data you collect from this dimension to determine which keywords are better converters than others and apply them into your organic campaigns. That’s data you can’t really derive from Google Search Console because the keyword specific lead source as an identifiable referrer in Analytics is not available from there. Depending on how much traffic you’ve captured, you may need a month or two’s data to make some informed decisions around how well some keywords convert.
Search Queries: differs to the Keywords section because it reflects the actual search phrases being searched in Google that triggered the specific ad in your campaigns. You may be able to build valuable info around longer-tail keyword phrases that convert and this would allow you to target the best performing long-tail searches with your organic SEO campaign.
Hour of Day: shows when your ads have been triggered and can help you asses which times provide you with the best conversion rates. For example, mornings might be associated with lower conversion rates, but traffic referred from your campaigns after midday might convert better. That would help you make decisions around day parts and when you should boost or reduce your bids.
Final URLs: Denotes the URL of the landing pages for any given Adwords ad. Use this data to determine which landing pages deliver the best results. You might want to use this for split testing of landing pages to work out how you can apply layout to your website on other pages too, for improved conversion rates.
Search Engine Optimization:
Connect your Google Search Console property with your Google Analytics property to see GSC data shown in this section. This section doesn’t reveal anything more than what is already available in either platform, but it just makes viewing of the data easier and useful as a comparison. Using dashboards, or custom displays, you can show correlations between organic referral and conversion metrics.
Landing Pages: View impressions, clicks, average position and CTR data sourced from Google Search Console.
Geographical Summary: View impressions, clicks and CTR data from Google Search Console, sorted by country.
Drill down into social referral data to see how well your social media lead source campaigns are working for you. This is great for working out which platform is most likely to convert, and differences occur because some types of product or service are better suited for sharing on some platforms than others. For example, LinkedIn may provide excellent conversion rates for articles shared there promoting educational material or business management, when Pinterest performs poorly for the same articles. This may relate to user demographic, user expectations and platform purpose or a variety of other reasons.
Network Referrals: See which social networks are referring traffic to your website and measure engagement goals or conversions.
Data Hub Activity: provides useful information on which elements from your social channels are referring traffic. There is a limited set of social data hub partners that share this information with Google Analytics.
Landing Pages: provides a breakdown of performance based on the landing pages that traffic was referred to from your social channels.
Trackbacks: allows conversion data to be connected with a source of traffic where the traffic source isn’t the last referrer.
Conversions: set up goals to measure the value of your social channels for conversion.
All Campaigns: Compares paid lead source campaigns by type, useful for determining where you should make greater effort or investment in generating traffic.
Paid Keywords: Compares keywords from paid lead source campaigns from all channels.
Organic Keywords: provides data that is traceable back to keywords where the lead source is unpaid. Google does not share most of the keyword data so it’s usually not possible to connect conversion with a specific organic referring keyword.
Cost Analysis: Import cost data from your non-Google lead source campaigns to associate costs with conversion goals and calculate return on investment.
What are your website’s users doing while they are on your site? It’s a common question and this section helps answer that as much as possible. Factors determining success of failure relating to User Interface (UI) and User Experience (UX).
Behaviour Flow: A schematic showing which pages your users landed on and where they went after that. Shows drop-offs and can highlight down to specific referring pages to see where traffic went.
All Pages: a simple list of all pages in your website that received traffic from any source within the given reporting timeframe with individual pageviews and engagement metrics, and if Goals or eCommerce is enabled, a calculation of the value of the page based on conversion value divided across pages in conversion pathway.
Landing Pages: a list of landing pages (pages with external referral) and metrics showing acquisition, behaviour and conversion metrics. Use this data to help refine the first step of your conversion funnel.
Exit Pages: a list of pages that were the last interaction by the user. Use this to help refine conversion funnels and identify where they might be failing.
Gives feedback from browsers on the load timings for pages in your website. Unlike the Google PageSpeed Insights tool, this reports timings that the user experiences, not Googlebot. It may be helpful with determining issues with code, bandwidth use or hosting services that can negatively affect the user experience on your website. Combine this with data on bounce rates or exits to determine if there are issues with specific pages only.
Page Timings: compare the timing data from all of the pages that were reported in the given reporting timeframe to easily highlight pages with load speed issues.
Speed Suggestions: a connection point between Google Analytics and the Google PageSpeed Insights tool providing page speed scores and guide on what points should be resolved to speed up each page.
Get data reported by searches performed on your website.
Usage: feedback on the usage rate of site search and how they play a part in acquisition, behaviour and conversion. Use this data to determine the value of site search and the extent to which you might want to leverage that to improve conversion. Often ideal for eCommerce websites with large product inventories.
Search Terms: feedback on the search terms being used within your site when site search is used. This data can be used to direct organic SEO campaigns because it can show a direct link between a user’s query and a conversion even if the user arrived from referral sources.
Pages: Get data regarding the page on which the search occurred and which page became the destination once search was made. Use this data to refine product filters or navigation on your site, or provide better data for your internal search engine to shortcut conversion pathways.
Install extra triggers in your website to measure events that the standard Google Analytics snippet can’t measure. Events might be simple actions like in-page clicks, ‘play’ buttons, form submissions etc. and are any action that doesn’t result in a page-load or that cause a page-load but are not a navigation click.
Top Events: a list of the events in order of performance, with associated value if value is assigned.
Pages: the pages on which the event was triggered with total events and their associated value if assigned.
Events Flow: the order in which events are triggered by a user or users.
Collect data on any ad content published in your website by AdSense or Ad Exchange.
Create custom experiments with acquisition, behaviour and goal conversions to compare specific criteria or structural changes in your website.
View your website in the Google Analytics in-page window mode to view data relating to navigation points in your website.
Set up goal tracking in the Google Analytics View settings to determine if you’re website is doing what you want it to do. Goals can vary from simple destination page-loads like a contact page, to custom eCommerce sales with upsells or upgrades.
Goal URLs: for a list of page URLs on which goals are being achieved.
Reverse Goal Path: track back from your Goal URL to the point of entry to determine if your conversion funnel is working as expected. This is helpful in figuring out why a particular goal gets better conversions than another and if you can assign responsibility for the reduced achievement rate on a specific page or path.
Funnel Visualization: a graphic representing you goal conversion pathways for easier conceptualisation.
Enable eCommerce tracking on your website if you have an eCommerce system that transacts the sale on your website. If your website acts as a lead source for another eCommerce system, you will need to have cross-domain tracking active to connect conversion with lead source.
Product Performance: measure which products are performing well (sales units) and turning a profit by connecting lead source cost data such as CPC costs from Adwords campaigns.
Sales Performance: get overall sales performance data for your eCommerce system and connect this with load source costs to determine margins.
Not all eCommerce transactions on your website are conducted from an introduction by a single lead source campaign. A user might visit your website as a result of an Adwords ad, sign up to your email marketing campaign and purchase nothing. That same user might return days or weeks later after seeing a special on your email marketing direct mail, clicks through on the email and lands at the specials page and makes a purchase. In this instance, it’s hard to say for sure if the Adwords ad or the email are responsible for the sale.
Assisted Conversions: provides data on conversions that had a historic lead source. In other words, these track back to the first recorded interaction from the user so that all conversion steps are credited with assisting the conversion. This can provide valuable data on other key dimensions in your conversion pathway that are not part of a single session.
Top Conversion Paths: lists the most common conversion pathways where multiple lead source campaigns assisted the conversion. This may be: Adwords step 1, Display reMarketing as step 2, Email Marketing Signup as step 3, Email Campaign as step 4, Purchase as step 5.
Time Lag: shows data relating to the time between first step conversion and last step conversion. In the example above, this is the time between first clicking on the Adwords ad until the purchase is made at step 5.
Path Length: provides data on the number of steps between first conversion (lead source) and last conversion (goal achievement).
This is a methodology of assigning value to conversion steps in an eCommerce conversion funnel. They can either be assigned to the first step, the last step, or equally over all steps. The model selected will determine the proportional value attributed to any individual conversion step.
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