Donate Ethereum to this address
If you find the blog useful and would like to show some support, a little donation of Ethereum would help to keep this site alive. Appreciate it!
Data alone does not deliver value until we put it into context.
Today, we will be focusing on Web Analytics, answering the what and why.
1. What is web analytics?
Web analytics is simply the collection, analysis and reporting of web data.
At the same time, it is about ensuring collected data are accurate and usable. By reporting the data, we put them into context, tell a story and use that to deliver actionable insights and drive business value.
2. What is web data?
Assuming a webpage is being tracked using web analytics, below are some actions that can result in web data being collected:
- Opening a webpage
- Clicking a button
- Clicking a banner
- Playing a video
- Using the search function
- Completing an application form
Web data collected are primarily information of pages, forms, products, etc.
Some examples are:
- Page name
- Product name
- Product category
- Form name
- Form step number
- Button name
- Banner ID
3. Why do we need web analytics?
With web data, we can better understand the consumer behavior on our websites.
Example: Ecommerce website
Without web analytics, we already how many items were being sold.
But we do not know is, which products are the most viewed and sought after.
In the past, all we can do is to assume that the best product must be the one with the highest sales figure. But more often than not, that’s not true.
We have product A and B, both similarly priced.
Sales figure for both products:
- Product A: 1,000 units
- Product B: 200 units
Through web analytics, we found out how many times the products were viewed:
- Product A: 10,000 views
- Product B: 500 views
Turned out, product A was widely advertised and promoted, while product B was left behind on page 5.
Now, we are given a small budget to carry out a product awareness campaign, which product would you do it for?
Without web analytics, product A was the obvious choice, as it had the highest sales volume.
With web analytics, we can now determine the sales conversion rate to help us make better informed business decision:
- Product A : 1,000 / 10,000 = 10%
- Product B : 200 / 500 = 40%
Now that we know that product B has a much higher sales conversion rate, it makes sense that we focus our marketing efforts on it over product A.
In this example, let’s assume the role of CEO in an Ecommerce company.
With the help of data analytics (not web analytics!), we were able to build a statistical model using historical sales figures.
The statistical model provided us with the forecast of future sales based on historical sales figure:
- 2015: $1,000
- 2016: $2,000
- 2017: $4,000
The statistical model forecasted:
- 2018: $7,500
- 2019: $14,000
From the forecast, it seems like our business is doing great!
To validate the forecast, we decided to involve data collected through web analytics.
- 2015: 100 web visitors
- 2016: 400 web visitors
- 2017: 2,000 web visitors (due to aggressive marketing)
Using both offline(transactional) and online data, we can then calculate the average revenue per user:
- 2015: ARPU = $1,000 / 100 = $10
- 2016: ARPU = $2,000/ 400 = $5
- 2017: ARPU = $4,000 / 2000 = $2
ARPU deteriorates as the number of web visitors increases, which means that the business is unsustainable and has limited scalability.
By combining both data analytics and web analytics, we were able to find out identify the issue, instead of wrongly assuming that the business is doing well.
4. Power of web data
Browsing on the internet is unlike making purchase in a shop.When buying in a shop, you think and rationalize to decide whether should you be making that purchase.
When you browse on the internet, you simply do it. You do not go thinking “should I visit this page?”.
In a way, browsing on the internet is akin to breathing. You do it without thinking about it.
To understand why web data is so powerful, on the internet:
- We primarily look for stuff that are of interest to us
- We search for things that we might not disclose publicly
That is also why we are uncomfortable with sharing our browsing history.
Our browsing history holds information of our wants, likes and needs.
By looking into the web data, we can easily identify potential customers who are showing interest in us.
Web analytics is crucial in helping businesses truly understand what their customers want and need.
It provides an additional data source that is reliable in helping us obtain useful insights that cannot be found in offline data.
So if you’re still new to web analytics, we have some articles that will help you kickstart the whole process:
If you already have Google Analytics set up, here are the reports to get you started: