Behind search engines results there is a complex mathematical formula called “algorithm” that determine which pages should rank before others; like the formula of Coca Cola this is the best kept secret by Google and other major search engines such as Bing and Yahoo, actually the quality of the algorithm of Google was the responsible of the rapid adoption of this search engine virtually around the world.
The algorithm is in other words a combination of numbers and probabilities that analyze hundreds of ranking factors to classify the information they display, in other words the science behind search engines is a mathematical formula. The algorithm is search engines could be perhaps the most complex formula to understand in online marketing, but it is not the only one, I have found other useful formulas that can help people understand the complexity of the online marketing world and help them understand the concept from a scientific perspective. These are some of the formulas that I have found but please if you find any other one related with online marketing share it to everyone on this post, I’ll be added them as I get it.
Conversion Rate Formula
CR = Conversions / Clicks * 100
This is the most simple and significant formula for advertisers, the conversion rate it means just how many people that click in your ad finally made a purchase. This is an example:
Let’s see 1000 people click in your online Ad, and out of those 1,000 just 25 finally made a purchased or any other action that you considered conversion (download a brochure, sign up for a newsletter or more), on this case your conversion rate is 0.01%
CR = (25/1000) * 100 = 2.5%
Website Success Formula
B = V x C x L
Where:
B = amount of business done by the site
V = unique visitors coming to the site
C = conversion rate (the percentage of visitors who become customers); note that the concept of conversion applies not only to ecommerce sites, but to any site where there is something you want users to do)
L = loyalty rate (the degree to which customers return to conduct repeat business)
Jakob Nielsen
This is a formula shared by Peter Morville, Dr. Susan Weinschenk, Dr. Jakob Nielsen, and Kim Krause Berg to www.searachengineland.com, for Jakob Nielsen the L factor is the most important variable for long-term website strategy: “It is imperative that websites liberate themselves from being overly-dependent on search engines and regain the positioning as the place users turn for the type of problem they address,” he said. “Right now, the best loyalty mechanism is the email newsletter, so it’s important to balance the site design in such a way that it encourages newsletter sign-ups at the same time as it also drives conversions.”
Landing Page Conversion Formula
C= 4m + 3v + 2(i-f) – 2a
Where:
C = Conversion
M = Motivation (what is the motivation of the user when he/she gets to your page? Do they have a level of interest?)
V = Clarity of Value Proposition (what is the valuable offer to your clients)
I = Incentive (sense of urgency, offer valuable exchange for an action)
F = Friction (elements to process, difficult forms can cause friction)
A = Anxiety (entering sensitive or personal information)
- Value Prop and Incentive are Value Contributors
- Friction and Anxiety are Value Inhibitors
- You need the Contributors to outweigh the Inhibitors
This is a heuristic algorithm for landing page conversions developed by www.marketingexperiments.com, there is a webcast available at Inbound Marketing University that speaks about this topic
The value of this formula is that it represents each factor that directly affects the conversion of a visitor on a landing page.
Potential Lost Revenues from Landing Pages
(RPV * NVL) * BR = LRPV
Where:
RPV = Revenue per visitor
LRPV = Lost Revenue Per Visitor
NVL = Number of visitors who land on landing page
BR = Bounce rate
(RPV * NVL) is also known as RETS = Revenue Estimate For The Traffic Source
This formula is presented by Tim Ash on his book “Landing Page Optimization: The Definitive Guide to Testing and Tuning For Conversions” allows website owners to calculate how much they could loose from a bad optimized landing page. Tim Ash though mention that in real life things are a little bit more complicated, sometimes a page can serve as both a landing page and as a link in the conversion path from other pages stream to it, more details about this formula and specific samples can be found on his book.
Viral Marketing Formula
Where:
Custs = Initial set of costumers
K = Invitations send by each new customer * Conversions %
ct = Viral cycle time
t = time
David Skok and Stan Reiss
This is one of the online marketing formulas I liked the most, it was developed by David Skok VC at Matrix Partners and writer of the Blog www.forentrepreneurs.com and Stan Reiss General Partner at Matrix Partners.
This formula is great to understand how fast a viral message or product can travel over the Internet, when the “viral cycle time” of a campaign/product is short, (1 or 2 days) an initial group of 10 customers can reach almost 30.000.000 users in 20 days. It is worth to see this analysis and understand deeper this concept. The complete explanation of this formula along with spreadsheets where you can make your own calculations can be found at forenterpreneurs.
Finally here you can find some useful calculators from Bplans that you can use to when creating your next eMail marketing or PPC campaign
eMail Marketing Return of Investment Calculator
Pay Per Click Return of Investment Calculator
This article has been written by Libardo Lambrano, founder of Syndikomm; an online marketing firm based in New York city and specialized in multicultural markets. Libardo Lambrano, a digital citizen of the world can be reached@llambrano