Web analytics

What is web analytics

Web analytics is the measurement and analysis of data to inform an understanding of user behavior across web pages. 

Analytics platforms measure activity and behavior on a website. For example: 

• Marketers and content creators: To assess campaign effectiveness and ROI. How many users visit, how long they stay, how many pages they visit, which pages they visit, and whether they arrive by following a link or not.
• UX designers: To understand how visitors interact with a website or app. Do they like text or prefer images, what icons do they like, and what options do they click on? 
• eCommerce business owners: To measure and benchmark site performance and to look at key performance indicators that drive their business, such as purchase conversion rate.

Web analytics platforms track various metrics, such as visitor count and demographics, time spent on the site, pages visited and navigation paths, traffic sources, conversion rates, and more. 

Web analytics gathers insights through different methods: 

• User segmentation: Grouping visitors based on shared characteristics 
• Behavioral analytics: Studying user actions and interactions on the site 
• Funnel analysis: Examining the steps users take towards a goal (e.g., purchase, downloading a content asset, etc.) 
• Cohort analysis: Collect data from website traffic to compare groups of users over time 

Businesses leverage these insights to optimize site performance and improve user experience. 

Why is web analytics important?

There’s an old business adage that whatever is worth doing is worth measuring.

Website analytics provide insights and data that can be used to create a better customer experience for website visitors.

Understanding customer behavior is also key to optimizing a website for key conversion metrics.

For example, web analytics will show you the most popular pages on your website, and the most popular paths to purchase.

With digital analytics, you can also accurately track the effectiveness of your digital marketing campaigns to help inform future efforts.

Key components of web analytics: 

• Data collection methods: This includes data gathering techniques such as JavaScript tags, cookies, and server logs. Proper data collection is crucial for accurate analysis and decision-making. 
• Metrics and KPIs: The specific measurement parameters used to evaluate website performance. Examples include bounce rate, conversion rate, and average session duration.
• Reporting and visualization: This involves presenting data in easily understandable formats like charts, graphs, and dashboards. Clear visualization helps in quick comprehension and communication of insights. 

Web analytics process

Most analytics tools ‘tag’ their web pages by inserting a snippet of JavaScript in the web page’s code.

Using this tag, the analytics tool counts each time the page gets a visitor or a click on a link. The tag can also gather other information like device, browser and geographic location (via IP address).

Web analytics services may also use cookies to track individual sessions and to determine repeat visits from the same browser.

Since some users delete cookies, and browsers have various restrictions around code snippets, no analytics platform can claim full accuracy of their data and different tools sometimes produce slightly different results.

Mainly, the web analytics process focuses on the four key components:

1. Data collection

   This is the foundation of web analytics, where raw data is gathered from user interactions with the website. For example: 

   • Time stamps: When users visit or interact with the site 
   • Referral URLs: Where users came from (e.g., search engines, social media) 
   • Query terms: What users searched for to find the site 
   • Page views: Which pages users visited 
   • User-agent strings: Information about users' devices and browsers 

2. Turning data into insights

   Data is turned into actionable insights using metrics that describe user behavior. Examples of metrics include: 

   • Bounce rate: Percentage of single-page visits 
   • Unique visitors: Number of individual users visiting the site 
   • Page views per session: How many pages users view on average 

3. Developing Key Performance Indicators (KPIs)

   Metrics are combined with business objectives to create KPIs that measure success. It includes: 

   • Average order value: Typical amount spent per transaction 
   • Task completion rate: How often users successfully complete a specific task 
   • Customer lifetime value: Predicted net profit from the entire future relationship with a customer 

4. Formulating strategy

   Businesses set objectives and create action plans to achieve desired results. Examples of strategic goals include: 

   • Increasing sales by a certain percentage 
   • Gaining a larger market share 
   • Improving customer retention rates 
   • Enhancing user engagement on the website 

Sample web analytics data

Web analytics data is typically presented in dashboards that can be customized by user persona, date range, and other attributes. Data is broken down into categories, such as:

Audience data

• number of visits, number of unique visitors
• new vs. returning visitor ratio
• what country they are from
• what browser or device they are on (desktop vs. mobile)

Audience behavior

• common landing pages
• common exit page
• frequently visited pages
• length of time spent per visit
• number of pages per visit
• bounce rate

Campaign data

• which campaigns drove the most traffic
• which websites referred the most traffic
• which keyword searches resulted in a visit
• campaign medium breakdown, such as email vs. social media

Web analytics is crucial for enhancing user experience, which directly impacts bounce rates and SEO rankings. A/B testing helps compare different versions of web pages or marketing materials, informing design and content decisions. Segmentation allows for tailored UX and marketing efforts based on user characteristics. Targeting, informed by segmentation, enables personalized experiences, improving engagement and conversion rates.

By consistently applying these analytics-driven strategies, you can create a cycle of continuous UX improvement. 

Web analytics examples

The most popular web analytics tool is Google Analytics, although there are many others on the market offeringspecialized information such as real-time activity or heat mapping. Many of these tools also integrate directly into your content management system (CMS) or other parts of your martech stack. 

Making the most out of your web analytics tools

When choosing a web analytics platform, consider: 

• Real-time data access: Ability to view and act on data as it's collected
• Integrations: Compatibility with your existing tech stack and marketing tools
• API capabilities: Flexibility to extract and manipulate data for custom reporting
• Customer journey tracking: Ability to follow users across multiple touchpoints and devices
• Scalability: Capacity to handle your data volume as your business grows
• Ease of use: User-friendly interface and reporting features
• Cost: Pricing structure that aligns with your budget and expected ROI 

Most commonly used web analytics tools:

1. Google Analytics
   The ‘standard’ website analytics tool, free and widely used
2. Adobe Analytics
   Highly customizable analytics platform (Adobe bought analytics leader Omniture in 2009)
3. Kissmetrics
   Can zero in on individual behavior, i.e. cohort analysis, conversion and retention at the segment or individual level
4. Mixpanel
   Advanced mobile and web analytics that measure actions rather than pageviews
5. Parse.ly
   Offers detailed real-time analytics, specifically for publishers
6. CrazyEgg
   Measures which parts of the page are getting the most attention using ‘heat mapping’

With a wide variety of analytics tools on the market, the right vendors for your company’s needs will depend on your specific requirements. Luckily, Optimizely integrates with most of the leading platforms to simplify your data analysis.

Web analytics challenges

Data accuracy is a persistent issue, with factors like sampling, bot traffic, ad blockers, and disabled JavaScript potentially skewing results. Cross-device tracking presents difficulties in accurately identifying users across multiple devices, often leading to the overestimation of unique visitors. 

Interpreting data correctly is another hurdle, as numbers without context can be misleading. Technical limitations, such as the potential impact on site speed and the inability to track offline interactions, further complicate the picture.

Understanding these challenges is crucial. Regular audits of analytics setups, staying current with privacy regulations, and utilizing multiple data sources can help mitigate these limitations.

Sharper analytics, better insights

Data will only tell you something if you can gather it effectively. In lessons learned from 127,000 experiments, we found that teams with analytics outperform teams without by 32%. Additionally, they were 16% more successful by adding heatmapping.

It means that analytics usage is a major improvement opportunity for most businesses.