Tuesday, March 31, 2009

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

about the blog and me - First Post

The heading had a limit of 500 characters and so the full background here :)


About the Blog:

This is an independent blog by on the world of online analytics/web analytics - concepts, techniques, tools, ideas, practitioner interviews and answering user questions.


About the Web Analytics Blog Nuggets Organization

This blog will be organized around the following labels:

  • theory made simple: will talk about concepts, metrics and terminologies
  • tools: will talk about the features and usage of the web analytics packages in the market - both tag based (like Omniture SiteCatalyst, Google Analytics, Yahoo analytics) as well as server log based (Visual Sciences/Omniture Discover OnPremise)
  • Ideas: Will be about thoughts/frameworks/prototypes that can deliver 'real wins'
  • Practitioner corner: Will be about interviews with practitioners of web analytics
  • User: where we will take up and analyze problems faced in by readers of the blog in their web analytics ventures
This blog is a medium to share learnings and learn new things


About Me:

I'm a practicing web analytics consultant at a large US e-tailer/computer maker with hands-on experience in almost all buckets of web analytics - testing and behavioral targeting, business analysis of e-tailing sites, search marketing, support site improvements, visitor segmentation, pathing analytics and data mining.

I have a proven record of converting web analytics insights into dollars ($$):

:)