How To Integrate Goal And Business KPI In Analytics
In this article, I will be looking a bit closer at the main insights and analysis that can be done using Google Analytics, starting with the importance of aligning goals and KPIs, and then how you can develop and compare audience profiles, how to better analyze behaviour reports, how Google Analytics can help assess and improve technical performance, and finally information that can be obtained around the journey to conversion.
It is important to ensure that your goals in analytics, and the objectives you have set for your website, are aligned to your over business goals. This helps ensure that on and offline channels work harder, together, to deliver results for your business. While your website will have its own specific goals, the overall business objectives should be aligned with business KPIs.
Taking a look at the slide above we can see two examples of the best practices for aligning goals and business KPIs:
- If your overall business objective is to increase the number of repeat customers you have, you want to transfer this same objective on to your website. A good way to do this is to set up a goal that will track users who complete more than one transaction (come back and buy again) – and the aim is to increase the number of these conversions. Using this goal you can then analyze and optimize your website to provide a good user experience, communication at the end of purchase and follow-ups after the purchase.
- Increase your Average Order Value. While the average order value might be different on and offline, the objective of increasing it, relevant to the channel is appropriate both on and offline. Tactics to upsell would be different, however, and for instance, online you would plan to upsell products during the check-out phase, with related and relevant products or offers.
Using the audience reports, marketers can gain a better insight into who their online audience is – who is visiting and converting to customers on their website. This information can be compared to offline insights and your offline audience, in order to understand how these audiences differ, in both demographics and behaviour.
For example, here we see that the offline audience is slightly older with an equal gender split, while the online audience is a bit younger with a higher number of males than females.
Understanding your online audience helps to segment and analyze the behaviour of your online visits and you can gain insights into which audiences are most engaged, and which ones have the most conversions. This helps you to not only understand who your visitors are, but which audience is most valuable based on conversions and spend.
For example, here we are comparing e-commerce conversion data for males and females and this shows us that while females have more conversions and bring in more revenue, traffic from males has a higher conversion rate, and males tend to spend more than females do.
This deep understanding of your audience, from visits to spend, enables marketers to optimize website and marketing campaigns, and ensure marketing spend is more efficient and targeted towards your most valuable audience.
These insights can help inform content based on the engagement of your audience, messaging and targeting of online advertisements based on audience online footprint, and their engagement per platform and ad.
Optimizing your content can come from your audience, but it can also come from knowing more about how they are coming to your website, and what their user journey is like.
Analyzing this journey, such as this example of looking at the behaviour flow by landing page, allows you to see the most common journeys and identify your audiences’ interest in content or issues with it.
This can help to make optimization decisions on how to improve the content, call to actions and overall journey on a website in order to drive users towards the conversion.
Analyzing the top pages helps you to understand the most popular content, the pages which lead to high bounce rates, and also which pages that contribute to conversions and therefore have the highest page value.
Areas for improvement can be identified, for example, with pages that have a high bounce rate. Optimization can be made to improve the landing experience, content, call to actions and more, in order to lower the bounce rate and retain more visitors on the website.
If your website has a search function, tracking how this is being used and the search queries your visitors enter in the search bar can provide insight into the most popular products or topics related to your website, service or brand.
It can also help to identify a demand for content and information that is either not available or hard to find on the website.
These insights can help to improve the actual content on your website, as well as the structure and user journey, by ensuring that the in-demand content is visible and easy to access.
Event tracking, when set up, allows you to track non-page load interactions, such as clicks (to emails, external links, social links), downloads (of brochures, guides or other PDFs), video views (as well as detail on how much of a video is watched).
The information within the Events section, such as the overview report here, gives greater insight into how many events and unique events take place, on your website.
This section can also provide insight into how your visitors are interacting with your content and what they are engaging with. They can be used to improve your website by focusing on areas such as a call to actions, copy and layout.
Within the behaviour reporting area, there is an area for analyzing site speed where you can gain information on how fast your website and pages load for visitors.
This insight can help inform web development improvements and updates, such as optimizing for specific browsers as in this example and can help get information for speeding up slow loading pages.
This has an impact on the ability to improve bounce rates or identify problems such as un-optimized images or browser compatibility issues.
Site speed reports detail the pages with the slowest load time, analyzing cross-referencing bounce rates, such as in this example on the left, or devices users and geographical location, such as in the example on the right.
The detail provided in these reports are based on individual pages, as well as browser and geographic location, and they are easily cross-referenced metrics such as bounce rates and devices for greater insight.
Another key consideration to assess when improving onsite user experience is to look at devices used to access your site.
Device reports detail whether users come from a mobile, tablet of desktop and provide topline metrics for each of these so you can easily see differences in bounce rates, time on site, conversions, etc.
You can easily segment any report by device for even greater insight and analysis of user interaction and understand how each device works within your website.
This understanding can help improve layout and content on mobiles, for instance, or even give a better understanding of device used as part of the full user journey.
Website errors can sometimes be uncovered from Google Analytics reports, such as 404 pages in content reports, identified by the title ‘/pagenotfound’. This can depend on your website and set-up, causing drops in traffic and conversions where there are server errors or from the website going offline. Custom alerts can help to notify you of these immediately.
Goal funnels, when in place, show how users move through the conversion process and how many reach each stage. In this example, we can see that almost 2,500 sessions started the process, but only 559 completed it.
The funnel visualization can help to identify areas within the conversion path that have a large drop off rates, as seen here in red, or possible pain points. This highlights parts of the process that could be improved on in order to enhance the user journey and increase the conversion rates. The issues, when investigated could be down to technical issues or poor content.
Assisted conversion reports provide detail on how different channels, and campaigns specifically, contributed to conversions, and what value these channels provided when compared to direct conversions.
It can help to provide greater insight into an upper funnel and lower funnels campaign performance and how certain channels act as supporting channels more than direct conversion drivers.
This helps to give more credit to supporting channels, which otherwise might not appear to be valuable in contributing to goal completions. In this example, we can see how many conversions each channel assisted with and the value of those conversions, versus the amount of last click or direct conversions.
The top conversion paths report provides more granular insight than the assisted conversion reports into the most popular journeys users take to conversion. This will specifically layout the most popular steps and channels used by visitors during their conversion journey. In the example shown, we can see the top 10 conversion paths for the website highlighted in the blue box.
Understand that some channels are important from a supporting or upper funnel perspective, and others work better as a direct conversion channel, both help to better plan and operate a full marketing strategy. Attribution modelling can help to determine, based on a set of rules, how credit should be allocated to the different channels along the conversion path.
In this example, we can see and compare how the main channel groups perform as first interaction, last interaction and linear models.
By default, Google Analytics uses last-click attribution for its reports. So any report throughout the account, as well as the e-commerce and goals sections, will be providing data just on the last channels a user came through when they made their conversion. If a user, for instance, came three times through a paid search ad, before coming through an organic listing and making a purchase, the organic search visit will be credited with the conversion and the paid campaigns will receive no credit by default.
Therefore, using and comparing different attribution models can be helpful in understanding how all channels all to the conversion, and help you understand that without those previously paid ads, the users may never have come back organically and converted in the end.
The Model Comparison Tool within the Attribution reporting section helps you compare different models and what value your different channels get within each of these models.
- Last Interaction: the default in Google Analytics, which provides credit to the last channel a convertor interacted with
- First Interaction: does the opposite and provides the credit to the first channel a convertor interacted with
- Last Non-Direct Click: this works similar to Last Interaction, but removes “direct” traffic as a channel and will give credit to the channel before a direct conversion
- Last AdWords Click: this will look specifically at AdWords traffic and give more credit and insight to your AdWords campaigns
- Linear: this will provide each channel in the journey with an equal share of credit
- Time Decay: will provide each channel with credit, but on a time-based model, with channels closer to conversion received a higher ratio of credit
- Position Based: will provide each channel with credit but it will be customized by where the channel sat in the full journey, for instance, with the first and last channel receiving the most credit and those in-between just a little.
I know you might agree with some of the points that I have raised in this article. You might not agree with some of the issues raised. Let me know your views about the topic discussed. We will appreciate it if you can drop your comment. Thanks in anticipation.
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