The full post is in the Blogos, a stance on the possibility for making a monitoring plan for AdSense revenue by applying a linear graph of type mx+b. I confess that I have take part of the content.
Here I leave the review…
1. The base data
blah blah blah based on two years of data …
2. The projection
After entering the data, we can then calculate the expected value in the line graph of the year, which will behave in the form y = mx + b
m is the slope (derivative), in this case we find a 4.63, x is equal to the number of months starting each year from 1 to 12. M may be higher to the extent that well-planned SEO measures will be applied, but 4.63 is a reference point in our case.
b is the abscissa intersect, it will be equal to the cumulative drop value, it is as I said before in 33% on the year’s increase plus a 10% increase that Google gives us by fidelity (or consolation). It is also likely that this is a result that traffic is growing to the extent that keeps a constant publication, a well-defined segment and there are not penalize advertising practices.
Thus the graph for the first year starting from scratch would be
y=4.52 X + 18.81 (with an annual average of $48)
y=4.73 X + 133.91 (with an annual average of $165)
y=4.63 X + 177.98 (with an annual average of $208)
y=4.63 X + 240.27
The chart shows the different stages as it would be made by a marketing plan:
The red zone … It adapts well to the introduction stage in the products life cycle.
The yellow zone: … It adapts to the growth stage.
The orange zone: … It adapts to the stage of maturity.
The coffee area: … This is adapted to the saturation cycle.
The green area: … good time to consider a new product curve … because the decline stage can come if you do not have something in mind for later.
If there is a comparative framework, then it could be a way to go applying changes, efforts and warnings to see if they show better results. As said, I’m in 4th year, which average I can expect at least, what would my likely income in May and what is the worst fall I can accept.
The graph shows several fields that can be used to check if the behavior is the expected minimum. The yellow spaces can be used based on real data; at least for two years, then the columns from year 3 include an expected minimum value and minimum falls and likely increases.
There are some aspects that are not predictable, among them we can mention:
- A wiggle
- The post-wiggle
- The low traffic cycles
- Security Flaws
- Other unforeseen
Blah, blah, blah … some of these contingencies can be prevented, others … not.
– Although there are those who believe that it is not necessary the initial investment, or it’s possible to do it by you, from this will depend on achieving a derivative above 4.5%. Among the likely investments it can be considered not punishable by Google advertising, search engine optimization, creative design, branding, among others.
-The study reflects the income average of the year, it may be the average income of May and June.
-If this data is known, having this average can be considered that total income for the year will be the average multiplied by 12
-From the third year a minimum graphic projection can be done in case of having exceeded the 4.5% derivative it could be expected a 6.5% and a plan for this.
-The results show that the best months are July and August (although they are result of the previous two months) and that the worst falls are in January and September.
-This suggests that three years are the time that a blog should require to be a differentiated and recognized brand.
What a shame, that site is not available.