metrics: Building blocks of web analytics

Let us start with the definition of the building blocks of web analytics:
Visit/Session
User/Cookie
Request/Click

Imagine a world with only 3 machines: McA, McB and McC
and that folks named A, B and C are logged on to those machines

Consider the following sequence of browsing by the person on McA

DateTime Machine Page
11th Jan 10 AM McA Home_page
11th Jan 10:20 AM McA Category_page
11th Jan 10:30 AM mcA Product_details_page
11th Jan 10:45 AM McA add_to_cart_page
11th Jan 11 AM McA payment_page
11th Jan 11 AM mcA thankyou_page

Every page visited corresponds to a page_view/request/click
So above there were a total of 1+1+1+1+1+1 = 6 clicks/requests/page_views

so a page_view/click/request is defined as a "request for a page"

Time between the clicks is as follows
1 & 2: 20 minutes
2 & 3: 10 minutes
3 & 4: 15 minutes
4 & 5: 15 minutes
5 & 6: 0 minutes

All clicks with no idle time of 30 minutes between them form part of a

visit. So in the above sequence there was only 1 visit

So in our above example there was ONE visit and SIX clicks

Since it was from the same machine (assuming the user did not clear the

cookies), it is a single user.

Now let us change the sequence



When we visit a website, the website stores a cookie on our machine - This

is to identify us uniquely the next time we visit the site. That is how

Amazon identifies visitors on their repeat visit and shows them web pages

with products/categories viewed on earlier visit - customizing the

experience

Typical Ecomm Sales Funnel

The sales funnel

For hypothetical purposes, let us imagine a website: www.abc.com
with only 5 pages:
www.abc.com Called Home_Page
www.abc.com/cameras called Category_page
www.abc.com/cameras/camera1 called Product_detail_page1
www.abc.com/cameras/camera2 called product_detail page2
www.abc.com/addtocart called Cart_page
www.abc.com/payment called payment_page
www.abc.com/thankyou called thankyou_page



As you can see there is a purchase funnel where people can start off at any
of the pages prior to thankyou_page and complete a purchase

Key Retail Metrics: Finding parallels in etail store (italics)

Web Analytics is a relatively new science - and what better way to decipher the metrics than drawing a parallel with the world we are all familiar since birth - the "retail" world


For a moment imagine you are the owner of the BestBuy Store (a US retailer) in RoundRock, TX OR the owner of BigBazaar (an Indian Retailer) on the Inner Ring Road, Bangalore.

To make money and be a leader, You need to run the business efficiently - which means
you need to
* Get more folks to come to your store vis-a-vis the competitor
Prospective Customer has a choice here - he can come to your store or go to the competitor's store. You need to get him to your store
* Of the folks that come to your store, you need to maximize the percent that buy
* You want more of the customer's wallet - you want him to spend more on items at your store

The folks coming to the store maybe
* Repeaters:
Those who have come before (They present a huge data mining opportunity - Data can tell us about their buying patterns and their segments meaning we "know" them - and we can take marketing actions to drive more financial upside from these folks; this is a separate topic)
* Newbies
Those who are new
Our retail stores (BestBuy/BigBazaar) can increase the number of new prospects by advertising in local media, radio, Television, email Or letting people know through advertisements on websites
Optimizing advertising spend and ROI on the same itself is a challenge for the retail store

Whether folks coming to our store buy AND How much he spends could be a function of
* Ease of finding the product he wants on the store
e.g: Keep items likely to be bought together close to each other
Easy retail layout
* Pricing of the products on the store
* His understanding of the product (whether it satisfies his need or not - many times he may not be aware that a particular product satisfies his need the best)
* His money share planned for the day (implying his demographic 'segment' for example)
* His purchasing efficiencies

In the same particular order of italics, the key metrics/parallels in the online world are as below:

~ stands for "is related to"

* "more folks to come to your store" ~ Visits/Clicks/Users
* "maximize the percent that buy" ~ Conversion
* "more of the customer's wallet" ~ Average Order Value/Total Revenue Per Unit/Revenue Per Visit etc
* "Repeaters" ~ Users/Repeat Visitors
* "Newbies" ~ First time Visitors
* "advertising" ~ Online Demand Generation/Marcom
* "local media, radio, Television, email Or letting people know through advertisements on websites" ~ Advertising Channels/ODG Channels/Marcom Vehicles/Demand Generation Vehicles (DGV)
* "Optimizing advertising spend and ROI" ~ ODG/DGV/Marcom Analytics
* "Pricing" ~ Behavioral targeting Opportunity
* "understanding of the product", "ease of finding Product" ~ Optimal Site Layout/Optimal Site pathing

I have marked quite a few stuff in "italics" and drawn out parallels in the online world - In the subsequent posts, we will find parallels for these in the online e-tailing world - in separate posts