Why using a TOP list of promising offers is a cool idea
What makes whitehat products special is that the offers don’t last long.
A product offer can be used actively for 2-3 months, then, the products gradually become burned-out.
Thus, only the ones who find promising offers first and use an offer from the very first days of its life reap rewards.
It turns out that you can predict the success of an offer during the first 2 weeks after it’s launched in an affiliate network. Estimates suggest that the offer which hits the TOP in terms of prospects will be 44% likely to land on the TOP-15 list of sales volume during the following 2 months!
In other words: if you test the TOP offers, almost every second product you test will land on the TOP-15 list of offers in terms of sales volume (meaning that it’ll be successful). Landing on the TOP-15 list within 2 months is a good result because, average, 600-700 offers get traffic thanks to cpa.tl within 2 months.
How the TOP promising offers are chosen
Every Traffic Light department has its own KPIs. The effectiveness of the advertiser’s department is measured by the success of offers the company is launching. The success of the offers is measured by the number of leads. But how can you evaluate the work of a department if you have no statistics on new offers yet? In this case, you need 4 special metrics designed to analyze the effectiveness of working with new offers. Let’s give them names: Metric 1, …, Metric 4.
It turned out that one of them is strongly correlated with the number of
leads an offer brings in the future. This is Metric 4 that can be used in
two weeks after the company starts working with an offer.
What is this wonderful Metric 4? It is extremely easy to calculate. Metric 4 for an imaginary offer X =
The number of the affiliates who had at least one approved lead for offer X
The number of the affiliates who visited the card of offer X
Let’s imagine that the metric shows that offer X is good. Does it mean that the offer will really succeed? Statistical analysis is here to help. We’ll use the following data:
The number of leads within a month after a company starts working with an offer;
Metric 4 for this offer.
Correlation analysis based on historical data shows that Pearson correlation coefficient between these two indicators is 0.57.
Pearson correlation coefficient (Pearson’s r) is used to analyze the relationship of two variables and allows finding out how proportional variability of variables is.
Okay, is 0.57 coefficient big or small? Let’s have a look at Chaddock scale.
Absolute value rxy
Strength of correlation relationship
less than 0.3
from 0.3 to 0.5
from 0.5 to 0.7
from 0.7 to 0.9
more than 0.9
As we can see, the correlation relationship is noticeable, meaning that Metric 4 highlights promising offers better than many TOP lists :) That’s why this metric is used to make a list of TOP promising offers.