Demystifying Web Analytics for Beginners
It’s an exciting time to own and run a successful online business. Be it an eCommerce store or an online learning platform, the website is the focal point for business owners and customers. As an online business owner, you can practically track everything that online visitors and customers are doing on your website, including:
· How did they arrive at your website?
· Which webpages are they viewing the most?
· What actions are they taking on your website?
And so on...
What are the factors that convert an online visitor into a loyal customer? This is what is referred to popularly as the Customer Journey. Let’s first try to understand this aspect.
Explaining the Customer Journey
Simply put, a customer journey is the entire process right from the first time a customer learns about a website — until the time they make their first purchase (and beyond!!). A customer journey varies depending on the individual’s buying behavior to what they want to purchase. For instance, you can’t expect customers to have the same behavior when they are investing in real estate or purchasing a fashion accessory.
Here’s a typical customer journey for an eCommerce store:
However, a customer journey is primarily composed of multiple “moments of truth” (or MOT). These moments are critical decisions that customers make across the entire customer journey — that mark a significant turning point.
Primarily, there are 5 MOT stages in any customer journey, namely:
1. Zero MOT—or the moment when a user identifies a crucial need and starts searching for a product (or service) to meet that need.
2. First MOT — or the moment when the user lands on a website (or online store) to check if that brand can fulfil their need.
3. Second MOT — or the moment when the user transitions into a customer by making their first purchase — and then starts using the product (or service).
4. Third MOT — or the moment when customers evaluate the level of customer service they receive — in terms of timely delivery, post-sale support, and email updates.
5. Ultimate MOT — or the most crucial moment when customers determine if they want to reengage with the brand — and make repeat purchases.
To understand the customer journey, online brands must first understand every customer’s current stage or MOT. Based on this data, they can design their selling strategy and help customers arrive at their next stage of MOT. In short, an MOT largely influences the customer’s perception of the online brand.
How do online brands identify the MOT stage of any customer or user? Through web analytics. In this blog, let’s discuss everything you need to know about web analytics as a beginner.
What is web analytics?
Simply defined, web analytics is the process of collecting, measuring, analysing, and reporting real-time web data for the purpose of understanding and optimising website usage. Besides measuring web traffic, web analytics is an effective business tool to improve the effectiveness of any website and facilitate the customer journey.
Here’s an example of the working of web analytics:
Effectively, web analytics provide valuable information or insights about the incoming traffic to your website. Here are some of the insights:
· Which digital channels are sending the maximum traffic to your website?
· How many website pages are being viewed by first-time visitors?
· How many unique visitors does your website receive on a daily or weekly basis?
· Which are the most frequently-viewed pages?
· What actions are visitors performing on your website? For example, submitting an online form, subscribing to a monthly newsletter, or purchasing a product or service.
· Which are the countries (or regions) that are the source of most of your traffic?
· Which devices are visitors using to browse through your website?
How does Web Analytics work?
Primarily, web analytics comprises 4 basic steps namely:
1. Collecting data from various sources
In the first step, companies use traffic data to analyze the sources from where their traffic is originating. This could be in the form of organic traffic (for example, Google search), customer referrals, or digital ads (for example, Facebook marketing). In the case of search engines, they determine the popular keywords (for example, “iPhone 14 stores near me”) that are driving the maximum traffic.
2. Processing data into metrics.
In the next step, web analytics tools process the collected data into relevant metrics or key performance indicators (KPIs). Some of these metrics include:
· Bounce rate — or the percentage of users who left the website after visiting only one page.
· Page views — or the total number of webpages that the visitor opened in a set time period.
· Unique page views — or the number of unique visitors that opened a specific page within a time period.
· Number of sessions — or the number (or frequency) of users visiting the website within a specified time period.
· Repeat visitors — or the percentage of users visiting the website repeatedly.
· Session duration — or the average time that users spend on the website.
Here’s a snapshot of customer behavior metrics:
3. Monitoring KPIs.
The next step in web analytics is when organizations monitor the most relevant KPIs for their business. For instance, using KPIs like conversion rate, companies can track customer conversion — and the cost of acquiring a customer.
4. Formulating the action strategy
In the final step of web analytics, companies formulate their action plan or strategy to achieve their business goals. This could be in the form of increasing traffic, conversions, or revenues. For instance, A/B testing is one strategy of testing two different webpage designs for more conversions.
Web analytics also allows them to continuously monitor the impact of their action strategies — and identify areas of improvement. For instance, improving page loading speed.
Here’s an example of using A/B testing:
Types of Data Analytics — and Popular tools
Essentially, there are 4 types of data analytics used for web or business analytics. These are:
1. Descriptive analytics
This is the basic form of analytics that is based on “what happened in the past.” Using historical data, this technique uses data mining and aggregation to answer important business questions. Here are some examples of descriptive analytics:
· Annual sales and revenue
· Year-over-year sales
· Customer surveys
2. Diagnostic analytics
This form of analytics is more advanced than descriptive analytics — and explains “why things happened in the past.” Here are some examples of diagnostic analytics:
· Why is there a drop in annual sales in 2023?
· Why are customers preferring a particular product or service?
· Why was the bounce rate high?
3. Prescriptive analytics
Prescriptive analytics is more advanced than descriptive and diagnostic analytics. Essentially, it recommends “what needs to be done” by providing actionable insights from the data. Here are some examples of prescriptive analytics:
· Measuring the client’s risk profile in the insurance industry.
· Recommending new product features based on customer demographics and surveys.
4. Predictive analytics
Predictive analytics is the most advanced form that uses AI and predictive modelling to predict “what is likely to happen.” Using historical data, predictive analytics leverages data patterns and trends to predict future outcomes. Here are some examples of predictive analytics:
· Product recommendations in eCommerce
· How customers will respond to a new product launch or marketing campaign
How do organizations leverage the capabilities of web analytics? Using web analytics tools. There are plenty of free and premium tools used for web analytics. With a 71.4% market share, Google Analytics is the most popular tool.
Other popular web analytics tools include:
· Adobe Analytics
· Hotjar Analytics
· Jarvis Intelligent Analytics (developed by Tata Digital)
Apart from web analytics tools, most companies also use data visualization tools to display their KPIs and metrics through a visual elements like maps, charts, graphs, and heat maps. Data visualization makes it simpler for business users to interpret the presented data — and make informed business decisions. Some of the popular tools for data visualization include:
· MS Power BI
· Tableau
· Zoho Analytics
· Olik Sense
Conclusion
To summarize, web analytics is a crucial tool that organizations need to make sense of their website traffic and customer behavior. By using web analytics, customer-centric companies can improve the overall user experience and meet their business objectives.
With over 18 years of industry experience, Arnab Ganguly is the Analytics and Visualization head at Tata Digital. In this masterclass, Arnab talks about the growing importance of web analytics in the digital world.
By attending this masterclass, you can learn about web analytics from a leading expert in this field. Register for free today!!
Reference links:
https://www.growcode.com/blog/moments-of-truth-in-customer-experience-journey/
https://blog.hubspot.com/marketing/guide-to-web-analytics-traffic-terms
https://tracker.my.com/blog/276/what-is-web-analytics-the-beginner-s-guide?lang=en
https://research.aimultiple.com/web-analytics/
https://business.adobe.com/blog/basics/descriptive-predictive-prescriptive-analytics-explained
