Understand these E-commerce metrics before applying
What is this blog about?
In this blog, e-commerce metrics, we will try to understand different things which are asked in a company that focuses on their website or application conversion. Web analytics is a way to understand the sentiments of your online users, why they are buying what they are buying and what they are not buying because by the end everything boils down to a user buying something on the internet.
So, in the blog “Metrics to know before applying to e-commerce companies” we will try to solve a few things for you that will make you comfortable when you apply to some of these companies
With this blog, you will be able to come up with a strong candidature in companies like Myntra, Amazon, Flipkart, OYO rooms, Facebook, Linkedin, etc.
So if you are already in the analytics domain or are looking forward to applying to one such company in the coming months, then do give a read to this blog.
What is the need of knowing these metrics?
When you apply to any of the e-commerce companies then there is an unsaid expectation that you should be aware of the things tracked, apart from that it’s always good to talk in the same lingua as used by your interviewer. Also, you will easily get some brownie points by explaining these points in the technical as well as case study rounds. These are the most important things to know after your technical expertise. Go prepared 🙂
Do I need any prior experience?
You are reading this blog on an online mode which means that you are well aware of the internet and that you know you are a visitor on this page. As long as you are comfortable with these points you do not need any other experience.
P.S. – We will also have a detailed video with live examples where you can understand concepts in a better way, if the link is not provided here, do check it out on our youtube channel i.e. The Data Monk
Visitor – If you are reading this article then you are a visitor to our website
Unique Visitor – If you visit our website 30 times today then also you are only one unique visitor
Visits – Now you have made 30 visits to the website
Repeat visitor – Suppose The Data Monk defines the logic that a visit span is 30 days. If you come two times in the last 30 days then you are a repeat visitor
Return visitor – Suppose you came last time in Oct’21 and it’s currently Jan’22 then you are a return visitor
Views – number of times you visit a particular page is the number of views. Even if you refresh the webpage then also the number of views will increase
Session – If the admin of the website had defined a session to be of 30 minutes then accordingly a session id will be generated. Ex. you start at 11:00 in the morning and surf for 10 minutes, then come back at 4 in the evening, then a new session id will be generated for you
Bounce – If you close the website on the first page itself then it is termed as a bounce, though you will still be a visitor and your number of views will be 1
Click-Through Rate – The click-through rate refers to the percentage of people who click through a certain link compared to the total number of people who saw the link. For example, if 1,000 people see a Google ad and 10 of them click on that ad, that ad has a click-through rate of 1 percent. You should aim for a click-through rate on your Google Ads of about 3-5 percent
Impression – Number of times your advertisement or web link is shown to the customer on the online space. For example, when you google something then you get some sponsored links, you may get around 4-5 images at the top. It does. not matter if you click on these images or not, but there are impressions of these products. Suppose this same page is shown to 1000 people and only 10 people clicked on the one which is priced at Rs. 21900, then the CTR for the product id 1% with 1000 impressions and 10 clicks
Load Time – Load time refers to the amount of time it takes a page to appear when a user lands on it. About 30 percent of users will leave a website whose pages take longer than 3 seconds to load, so that number should be your benchmark. Optimize slower loading pages to maximize their speed.
Time on Page – Time spent by users on a particular page.
Session Time – What is the average time for which a user explores your website or app
Conversion – If you are selling a product then what is the conversion i.e. number of people entering the buy flow and the number of people actually buying something on the web portal.
What type of questions are asked in an interview (mostly in the case study round)?
You will b asked something like, “Think of the important metrics which you need to track for the CXOs to give them a glimpse of their daily business” or “What all the KPIs in an online market”
Now, once you know these 14-15 metrics then you are already 2 steps ahead of an unprepared candidate plus you have a lot of time to think of other derived metrics like a customer flow diagram, Customer Life Time Value (CLTV), the Failure rate of the final conversion page, etc.
Now you can start explaining things like:-
-Tracking the performance of the payment page
-What is the average bucket size of a customer
-Coupon code success rate
-Customer Life Time Value
If you have some more metrics which can be used to explain a fresher or beginner then do comment below.
Hope you liked the content of e-commerce metrics.
We will have the next blog on Customer Churn Analysis which is one of the most standard projects done in every e-commerce company and is very useful to understand before you appear for an interview
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There are a few things which might be very useful for your preparation
The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews for Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics Manager, etc.
Go through the watchlist which makes you uncomfortable:-
All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
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