[Build Backlinks Online] Back to the Future: Forecasting Your Organic Traffic

Build Backlinks Online has posted a new item, 'Back to the Future: Forecasting
Your Organic Traffic'


Posted by Dan Peskin
This post was originally in YouMoz, and was promoted to the main blog because it
provides great value and interest to our community. The author's views are
entirely his or her own and may not reflect the views of SEOmoz, Inc.

Great Scott! I am finally back again for another spectacularly lengthy post,
rich with wonderful titles, and this time - statistical goodness. It just so
happens, that in my past short-lived career, I was a Forecast Analyst (not this
kind). So today class, we will be learning about the importance of forecasting
organic traffic and how you can get started. Let's begin our journey.



Forecasting is Your Density. I Mean, Your Destiny


Why should I forecast? Besides the obvious answer - its f-ing cool to predict
the future, there are a number of benefits for both you and your company.

Forecasting adds value in both an agency and in-house setting. It provides a
more accurate way to set goals and plan for the future, which can be applied to
client projects, internal projects, or overall team/dept. strategy.

Forecasting creates accountability for your team. It allows you to continually
set goals based on projections and monitor performance through forecast accuracy
(Keep in mind that exceeding goals is not necessarily a good thing, which is why
forecast accuracy is important. We will discuss this more later).

Forecasting teaches you about inefficiencies in your team, process, and
strategy. The more you segment your forecast, the deeper you can dive into
finding the root of the inaccuracies in your projections. And the more granular
you get, the more accurate your forecast, so you will see that segmentation is a
function of accuracy (assuming you continually work to improve it).

Forecasting is money. This is the most important concept of forecasting, and
probably the point in where you decided that you will read the rest of this
article.

The fact that you can improve inefficiencies in your process and strategy
through forecasting, means you can effectively increase ROI. Every hour and
resource allocated to a strategy that doesnt deliver results can be reallocated
to something that proves to be a more stable source of increased organic
traffic. So finding out what strategies consistently deliver the results you
expect, means youre investing money into resources that have a higher
probability of delivering you a larger ROI.

Furthermore, providing accurate projections, whether its to a CFO, manager, or
client, gives the reviewer a more compelling reason to invest in the work that
backs the forecast. Basically, if you want a bigger budget to work with,
forecast the potential outcome of that bigger budget and sell it. Sell it well.

Okay. Flux Capacitor, Fluxing. Forecast, Forecasting?




I am going to make the assumption that everyones DeLorean is in the shop, so
how do we forecast our organic traffic?

There are four main factors to account for in an organic traffic forecast:
historical trends, growth, seasonality, and events. Historical data is always
the best place to start and create your forecast. You will want to have as many
historical data points as possible, but the accuracy of the data should come
first.

Determining the Accuracy of the Data


Once you have your historical data set, start analyzing it for outliers. An
outlier to a forecast is what Biff is to George McFly, something you need to
punch in the face and then make wash your car 20 years in the future. Well
something like that.

The quick way to find outliers is to simply graph your data and look for spikes
in the graph. Each spike is associated with a data point, which is your outlier,
whether it spikes up or down. This way does leave room for error, as the
determination of outliers is based on your judgement and not statistical
significance.

The long way is much more fun and requires a bit of math. I'll provide some
formula refreshers along the way.

Calculating the mean and the standard deviation of your historical data is the
first step.

Mean



Standard Deviation




Looking at the standard deviation can immediately tell you whether you have
outliers or not. The standard deviation tells you how close your data falls near
the average or mean, so the lower the standard deviation, the closer the data
points are to each other.

You can go a step further and set a rule by calculating the coefficient of
variation (COV). As a general rule, if your COV is less than 1, the variance in
your data is low and there is a good probability that you dont need to adjust
any data points.

Coefficient of Variation (COV)



If all the signs point to you having significant outliers, you will now need to
determine which data points those are. A simple way to do this is calculate how
many standard deviations away from the mean your data point is.

Unfortunately, there is no clear cut rule to qualify an outlier with deviations
from the mean. This is due to the fact that every data set is distributed
differently. However, I would suggest starting with any data point that is more
than one deviation from the mean.

Making your decision about whether outliers exist takes time and practice.
These general rules of thumb can help you figure it out, but it really relies on
your ability to interpret the data and be able to understand how each data point
affects your forecast. You have the inside knowledge about your website, your
equations and graphs dont. So put that to use and start making your adjustments
to your data accordingly.

Adjusting Outliers


Ask yourself one question: Should we account for this spike? Having spikes or
outliers is normal, whether you need to do anything about it is what you should
be asking yourself now. You want to use that inside knowledge of yours to
determine why the spike occurred, whether it will happen again, and ultimately
whether it should accounted for in your future forecast.



In the case that you dont want to account for an outlier, you will need to
accurately adjust it down or up to the number it would have been without the
event that caused the anomaly.

For example, lets say you launched a super original infographic about the
Olympics in July last year that brought your site an additional 2,000 visits
that month. You may not want to account for this as it will not be a recurring
event or maybe it fails to bring qualified organic traffic to the site (if the
infographic traffic doesnt convert, then your revenue forecast will be
inaccurate). So the resulting action would be to adjust the July data point down
2,000 visits.

On the flipside, what if your retail electronics website has a huge positive
spike in November due to Black Friday? You should expect that rise in traffic to
continue this November and account for it in your forecast. The resulting action
here is to simply leave the outlier alone and let the forecast do its business
(This is also an example of seasonality which I will talk about more later).

Base Forecast


When creating your forecast, you want to create a base for it before you start
incorporating additional factors into it. The base forecast is usually a flat
forecast or a line straight down the middle of your charted data. In terms of
numbers, this can be simply be using the mean for every data point. The line
down the middle of the data follows the trend of the graph, so this would be the
equivalent of the average but accounting for slope too. Excel provides a formula
which actually does this for you:=FORECAST(x, known_y's,known_x's)
Given the historical data, excel will output a forecast based on that data and
the slope from the starting point to end point. Dependent on your data, your
base forecast could be where you stop, or where you begin developing an accurate
forecast.

Now how do you improve your forecast? Its a simple idea - account for anything
and everything the data might not be able to account for. Now you dont need to
go overboard here. I would draw the line well before you start forecasting the
decrease in productivity on Fridays due to beer o clock. I suggest accounting
for three key factors and accounting for them well; growth, seasonality, and
events.

Growth


You have to have growth. If you arent planning to grow anytime soon, then this
is going to be a really depressing forecast. Including growth can be as simple
as adding 5% month over month, due to a higher level estimate from management,
or as detailed as estimating incremental search traffic by keyword from
significant ranking increases. Either way, the important part is being able to
back your estimates with good data and know where to look for it. With organic
traffic, growth can come from a number of sources but these are a couple key
components to consider:

Are you launching new products?



New products means new pages, and dependent on your domain's authority and your
internal linking structure, you can see an influx of organic traffic. If you
have analyzed the performance of newly launched pages, you should be able to
estimate on average what percentage of search traffic from relevant and target
keywords they can bring over time.

Using Google Webmaster Tools CTR data and the Adwords Tool for search volume
are your best bet to acquire the data you need to estimate this. You can then
apply this estimate to search volumes for the keywords that are relevant to each
new product page and determine the additional growth in organic traffic that new
product lines will bring.

Tip: Make sure to consider your link building strategies when analyzing past
product page data. If you built links to these pages over the analyzed time
period, then you should plan on doing the same for the new product pages.

What ongoing SEO efforts are increasing?

Did you get a link building budget increase? Are you retargeting several key
pages on your website? These things can easily be factored in, as long as you
have consistent data to back it up. Consistency in strategy is truly an asset,
especially in the SEO world. With the frequency of algorithm updates, people
tend to shift strategies fairly quickly. However, if you are consistent, you can
quantify the results of your strategy and use it improve your strategy and
understand its effects on the applied domain.

The general idea here is that if you know historically the effect of certain
actions on a domain, then you can predict how relative changes to the domain
will affect the future (given there are no drastic algorithm updates).

Let's take a simple example. Let's say you build 10 links to a domain per month
and the average Page Authority is 30 and Domain Authority is 50 for the targeted
pages and domain when you started. Over time you see as a result, your organic
traffic increase by 20% for the pages you targeted on this campaign. So if your
budget increases and allows you to apply the same campaign to other pages on the
website, you can estimate an increase in organic traffic of 20% to those pages.
This example assumes the new target pages have:


Target keywords with similar search volumes

Similar authority at prior to the campaign start

Similar existing traffic and ranking metrics

Similar competition


While this may be a lot to assume, this is for the purpose of the example.
However, these are things that will need to be considered and these are the
types of campaigns that should be invested in from a SEO standpoint. When you
find a strategy that works, repeat it and control the factors as much as
possible. This will provide for an outcome that is the least likely to diverge
from expected results.

Seasonality


To incorporate seasonality into a organic traffic forecast, you will need to
create seasonal indices for each month of the year. A seasonal index is an index
of how that month's expected value relates to the average expected value. So in
this case, it would be how each month's organic traffic compares with average or
mean monthly organic traffic.

So let's say your average organic traffic is 100,000 visitors per month and
your adjusted traffic for last November was 150,000 visitors, then your index
for November is 1.5. In your forecast you simply multiply by this weight for the
corresponding index month.

To calculate these seasonal indices, you need data of course. Using adjusted
historical data is the best solution, if you know that it reflects the
seasonality of the website's traffic well.

Remember all that seasonal search volume data the Adwords tool provides? That
can actually be put to practical use! So if you haven't already, you should
probably get with the times and download the Adwords API excel plugin from
SEOgadget (if you have API access). This can make gathering seasonal data for a
large set of keywords quick and easy.

What you can do here, is gather data for all the keywords that drive your
organic traffic, aggregate it, and see if the trends in search align with the
seasonality you are observing in your adjusted historical data. If there is a
major discrepancy between the two, you may need to dig deeper into why or shy
away from accounting for it in your forecast.

Events


This one should be straightforward. If you have big events coming up, find a
way to estimate their impact on your organic traffic. Events can be anything
from a yearly sale, to a big piece of content being pushed out, or a planned
feature on a big media site.

All you have to do here is determine the expected increase in traffic from each
event you have planned. This all goes back to digging into your historical data.
What typically happens when you have a sale? What's the change in traffic when
you launch a huge content piece? If you can get an estimate of this, just add it
to the corresponding month when the event will take place.
Once you have this covered, you should have the last piece to a good looking
forecast. Now it's time to put it to the test.

Forecast Accuracy


So you have looked into your crystal ball and finally made your predictions,
but what do you do now? Well the process of forecasting is a cycle and you now
need to measure the accuracy of your predictions. Once you have the actuals to
compare to your forecast, you can measure your forecast accuracy and use this to
determine whether your current forecasting model is working.

There is a basic formula you can use to compare your forecast to your actual
results, which is the mean absolute percent error (MAPE):

This formula requires you to calculate the mean of the absolute percent error
for each time period, giving you your forecast accuracy for the total given
forecast period.
Additionally, you will want to analyze your forecast accuracy for just a single
period if your forecast accuracy is low. Looking at the percent error month to
month will allow you to pin point where the largest error in your forecast is
and help you determine the root of the problem.

Keep in mind that accuracy is crucial if organic traffic is a powerful source
of product revenue for your business. This is where exceeding expectations can
be a bad thing. If you exceed forecast, this can result in stock outs on
products and a loss in potential revenue.

Consider the typical online consumer, do you think they will wait to purchase
your product on your site if they can find it somewhere else? Online shoppers
want immediate results, so making sure you can fulfil their order makes for
better customer service and less bounces on product pages (which can affect rank
as we know).




Top result for this query is out of stock, which will not help maintain that
position in the long term.

Now this doesn't mean you should over forecast. There is a price to pay on both
ends of the spectrum. Inflating your forecast means you could be bringing in
excess inventory as it ties to product expectations. This can bring in
unnecessary inventory expenses such as increased storage costs and tie up cash
flow until the excess product is shipped. And dependent on product life cycles,
continuing this practice can lead to an abundance of obsolete product and huge
financial problems.

So once you have measured your forecast to actuals and considered the above,
you can repeat the process more accurately and refine your forecast! Well this
concludes our crash course in forecasting and how to apply it to organic
traffic. So what are you waiting for? Start forecasting!

Oh and here is a little treat to get you started.

Are you telling me you built a time machine...in Excel?


Well no, Excel can't help you time travel, but it can help you forecast. The
way I see it, if you're gonna build a forecast in Excel, why not do it in style?

I decided that your brain has probably gone to mush by now, so I am going to
help you on your way to forecasting until the end of days. I am providing a
stylish little excel template that has several features, but I warn you it
doesn't do all the work.

It's nothing to spectacular, but this template will put you on your way to
analyzing your historical data and building your forecast. Forecasting isn't an
exact science, so naturally you need to do some work and make the call on what
needs to be added or subtracted to the data.

What this excel template provides:


The ability to plug in the last two years of monthly organic traffic data and
see a number of statistical calculations that will allow you to quickly analyze
your historical data.

Provides you with the frequency distribution of your data.

Highlights the data points that are more than a standard deviation from the
mean.

Provides you with some metrics we discussed (mean, growth rate, standard
deviation, etc).


Oh wait there's more?




Yes. Yes. Yes. This simple tool will graph your historical and forecast data,
provide you with a base forecast, and a place to easily add anything you need to
account for in the forecast. Lastly, for those who don't have revenue data tied
to Analytics, it provides you with a place to add your AOV and Average
Conversion Rate to estimate future organic revenue as well. Now go have some fun
with it.
________________________________________________________________________________________
Obviously we can't cover everything you need to know about forecasting in a
single blog post. That goes both from a strategic and mathematical standpoint.
So let me know what you think, what I missed, or if there are any points or
tools that you think are applicable for the typical marketer to add to their
skillset and spend some time learning.
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