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.

Monitoring

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.

3. Contingencies

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.

4. Conclusions

– 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.

This site uses Akismet to reduce spam. Learn how your comment data is processed.