Introduction to Web Analytics (KEY TERMS & CONCEPTS (A/B test,…
Introduction to Web Analytics
KEY TERMS & CONCEPTS
Testing two different versions of a web page to see if one has a significant impact on user actions. Also called a split test.
The sequence of clicks made by a user on a website in one session.
A user completing a pre-defined goal that the website owner wants them to take.
A defined path that users should take to reach the final conversion.
A raw figure captured for analysis. These are the most basic web analytics metrics.
User interactions with content that can be tracked independently from a web page or a screen load.
A defined action that users should perform on a website.
data visualisation tool
that shows levels of activity on a web page in different colours.
Reds and yellows
show the most activity and blues and violets the least.
A popular web coding language, used in web analytics for page tagging.
Key Performance Indicator (KPI)
A specific metric which is used to evaluate, track and analyse progress against a business or digital goal.
Text files created on the server each time a click takes place, capturing all activity on the website.
Testing many variables in different combinations to see if one combination has a significant impact on user actions. Usually conducted as a split test with up to eight combined versions.
Personally identifiable information: any information that could be used to track an individual in the real world, such as home address, name, phone number and email address.
An interpretation of data captured, usually expressed as a percentage.
The website that a web visitor arrived from.
Breaking down a whole into its parts based on distinct, shared characteristics that enable analysis by segment.
An individual (that is not a search engine spider or a script) visiting a website.
Web analytics software collects many small bits of browsing data to tell the story of:
How users got to the site
● When they arrived, and which part of the world they were from
● Whether they'd been to the site before
● What devices they were using
● What they did on the site, and for how long
● What they bought
● When they left.
Off-site and on-site analytics
web analytics refers to measuring a user's activity as they enter and interact with your own website. This includes the sites they came from, visitor information, the content a user views on your website, and conversions. This type of data helps you to identify your website's key performance indicators (KPIs), which measure site performance against your objectives.
web analytics is the measurement and analysis of a user's actions and interactions on the wider web, for the most part across other sites and tools (such as emails, social networks and mobile app stores). This information tracks a user's internet habits, interests, browsing activities and navigation patterns, which in turn identify and measure potential market opportunity.
Quantitative and qualitative data
Quantitative data is objective, factual information, while qualitative data is subjective, opinion-based information
Quantitative data: Statistically reliable results that determine if one option is better than the alternatives. E.g: Clicks, pageviews, time on site, abandonment rates
Qualitative data: Looks at the context of issues and aims to understand users' perspectives. E.g: Sentiment, feeling towards brand, user experience
Integrating quantitative and qualitative data
Many people assume that clickstream data is the source of all online decision making. Clickstream data is objective web data which tracks the sequence or stream of clicks that users make, however this represents only a small part of web data
Generating insights is the process of looking at all the available data - qualitative and quantitative - and then applying your own knowledge, common sense and lateral thinking to it, in order to try to understand what has happened, and why. It's also important to consider the context based on other business activities (in other words, what the business or its departments have done that might help to explain performance). This context is often needed for insightful analysis. Even the most expensive analytics software won't yield actual insights on clickstream data: it will just produce lots of data and reports.
Principles of Data Analysis
Focus on performance metrics, not vanity.
Vanity metrics are figures or measures that look interesting and useful on the surface, but don't really contain any substance when examined more deeply. They are the metrics that people like to brag about, like follower numbers on social media and visitor counts on websites. While it's great to see that a certain number of people are visiting the brand, this doesn't say anything about whether they are participating meaningfully. For example, many Facebook pages have a large percentage of "legacy likes” - people who liked the page in the past but never returned.
Performance metrics, on the other hand, better indicate whether the brand's objectives are being met. Some examples here include conversions (like making a purchase) and engagement metrics (like time spent viewing content or comments on a blog post).
Look at trends, not absolutes
Web analytics is the process of looking at changes over time, rather than isolating one specific moment. Trends give real insights, since they show how things are improving (or not) over time.
Once you have gathered web data over a long enough time, you will start to see emerging patterns - these could be daily traffic spikes, regular weekly user patterns or annual, seasonal changes. Understanding these tells you the optimal time for posting new content, when to expect annual dips, and much more - excellent for business planning even beyond web analytics.
The beauty of understanding patterns means you can quickly identify if anything out of the ordinary is happening on your website. Any unexpected increase or decrease in typical traffic patterns should be investigated. This could reveal new opportunities or problems that need to be resolved quickly.
Don't forget the real world.
Context is an essential consideration when working with web data; after all, things on the internet don't happen in isolation. They are directly influenced by news events, personal characteristics, trends, national and religious holidays, and much more. Keep a close eye on your brand's real-world context to ensure you have a full picture of what your data is trying to tell you.
Objectives, Goals, KPIs & Targets
Your online objectives need to reflect and support your business objectives. Ask yourself: why does your website exist? What role does it play in your business model? How does it help to achieve the goal of making money? It is very important to align your website (and web analytics) objectives with business objectives so that your online investments bring the desired results to the business. This is not only true for ecommerce businesses - clear objectives are important even if your website does not make money directly.
5 types of digital strategies that all have slightly different objectives
Ecommerce - Selling products and services
Lead generation - Collecting potential leads
Content publication - encouraging engagement & repeat visits