TOP offers

Why using a TOP list of promising offers is a cool idea

What makes whitehat products special is that such offers don’t last long. A product offer can be runned actively for 2-3 months, then, the products gradually become over-used. Thus, the affiliates who find promising offers first and start to run an offer from the very first days of its lifespan manage to make the most profit.

Only the offers introduced during the last 2 weeks can land on the list of the TOP promising offers.

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. Calculations 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 enter the TOP-15 list of offers in terms of sales volume (meaning that it’ll be successful). Entering 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 =

Let’s say, the metric shows that offer X is good. Does it mean that the offer will really be proffitable? 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.

Correlation coefficient

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	weak
from 0.3 to 0.5	moderate
from 0.5 to 0.7	noticeable
from 0.7 to 0.9	high
more than 0.9	quite high

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.