V-Day Special: How Web Analytics is a lot like Dating
In slew of Valentine’s Day and wrapping up my course in Web Analytics, I thought it’d be amusing to compare the subject to something we can all relate to: Dating. While some may roll their eyes to this thought or balk at the idea of comparing web analytics to the frivolous world of dating, hear me out. Have you ever thought that having a website and trying to reach customers and/or sell products is a lot like finding a soul mate? Well the two topics have more in common than you may think and for those who are just coming to the world of Web Analytics, it’s a nice way to learn key terms and concepts to apply to their own online ventures.
In dating, it all begins with boy meets girl or vice versa. This could also be the same when a customer finds a website or a website seeks potential customers. It’s all about initial attraction, alluring content, and ultimate retention. One could say that both dating and web analytics reside in competitive markets. With so many options for individuals to choose from (we can only hope in terms of dating), it’s beneficial to understand the factors that can help us reach our goals and environment for which these two topics exist in.
To make this argument on how web analytics is a lot like dating, I’ll create a side-by-side comparison of the two concepts and illustrate the technical terminology to finding and maintaining the perfect companion.
Key Business Requirements (KBRs)
"If you don't know where you're going, you probably won't get there" -Yogi Bera
For any website, a KBR constitutes an overall objective that the business is trying to achieve. Since every business (online or not) is unique, there will be varying KBRs across the board. They do however share a common goal: to contribute to the overall improvement of a business.
Examples of a KBR include:
-Selling more products-Expanding to new markets or attracting different types of customers
-Improving the customer experience
-Increasing brand awareness
Like any business, individuals in the dating world also have objectives he or she is trying to achieve. Often times these goals (or KBRs) can depend on varying circumstances that can often coincide to what a business is experiencing. Everything from timing (dating: where an individual is in life; business: a product life cycle) to resources (dating and business: available funds).
Examples of a KBR include:
-Wanting your partner to "pop" the question-Looking for a long-term relationship
-Needing free meals (hey some girls are on a budget and need to eat!)
-Looking for a fling or casually dating
Key Performance Indicators (KPIs)
"The measure of love is to love without measure" -St. Francis De Sales
To provide a useful comparison of KPIs in both Web Analytics and Dating, I'll outline four popular metrics and provide descriptions in Web Analytics terminology how they would translate to the dating world.
1. Visits, Visitors and Unique Visitors
-Web Analytics: The number of visits correlates to the number of arrivals to a website. These arrivals can be broken down into visitors and unique visitors. The difference between a visitor and a unique visitor is that a visitor only visits the website once, while a unique visitor returns at least once.
-Dating: The number of visits in dating could translate into the number of dates an individual goes on. A "visitor" would a potential mate that never turned into a second date, while a "unique visitor" found themselves making "the cut" to the second date.
2. Time on Page and Time on Site
-Web Analytics: The "Time on Page" represents the time a visitor (whether unique or not) spends on each individual page within a website. On the other hand, "Time on Site" represents the total session time a visitor spends on a website.
-Dating: While there are many ways I could compare these two definitions to dating, I'll stick to the most appropriate representations.
The "Time on Page" could represents time spent discussing different subjects on a date (i.e. "What are your hobbies?", "Where do you see yourself in 5 years?" (Let's hope you're never asked this on a date)). The "Time on Site" would therefore represent the total time spent on date.
3. Bounce Rate
-Web Analytics: As stated by Digital Marketing Evangelist Avinash Kaushik, "the Bounce Rate is the sexiest web metric ever!". The bounce rate measures the percentage of visitors who enter a website and "bounce" (leave the site) rather than continue viewing other pages from that website.
-Dating: In terms of dating, the bounce rate would represent the percentage of potential suitors who unfortunately leave a date without giving any second chances (which would of course never happen to any of us!).
4. Exit Rate
-Web Analytics: While this may sounds similar to the Bounce Rate, the Exit Rate signifies how many visitors left your site from a certain page, meaning whether they left from the "home page" or the "about me" page. Knowing the exit rate provides great insight to a business by showcasing where on their site needs potential improvement.
-Dating: Knowing the "Exit Rate" in dating correlates highly with one's self-improvement. An example of an Exit Rate would be the amount of people that no longer stay interested after knowing how many felonies you've committed (yes it's extreme, but possibly true in some cases) or knowing how many divorces you've had within the last year.
After outlining the KBRs and KPIs and their representation in Web Analytics and Dating, it would only be appropriate to analyze the final outcome. Often times we need to both step back as a company or individual and determine how we measure success. While success can often be numerical valuations from a company's viewpoint, dating on the other hand is incredibly subjective. For the sake of this post however, we'll focus on the most common success metric for websites: conversion rate.
The technical meaning of a Conversion Rate is defined as Outcomes divided by Unique Visitors (or Visits). Whether a business chooses to use "Unique Visitors" rather than "Visits" depends on their business objectives. If they were to use "Visits" as the denominator, it would be assumed that for every visit, the website has a chance to have the individual purchase a product and thus convert the user. Alternatively, if the company were to measure the conversion rate with "Unique Visitors", it would imply that a visitor could visit the website multiple times prior to purchasing a product. The difference between the two depends entirely on their marketing scheme and how much customer loyalty exists.Measuring success in dating often depends on the individual and their unique circumstances. Therefore translating the Conversion Rate into dating terms could go many different directions.
Here are some examples Conversion Rates in Dating:
The percentage of potential suitors that ...
-Making it to the second date
-Achieving a "yes" answer to a marriage proposal
-Reaching the "boyfriend-girlfriend" status
Additional Resourses on Dating and Web Analytics