Predicting Timescales in SEO

It’s been a good few week since I put a post out on SEO Mad, so I thought I’d give out some of the good stuff 🙂

Basically I have been collating data on how to predict how long it will take to achieve 1st page rankings for keywords in Google’s organic search results. For those who are familiar with my blog, will now that I like excel and so that’s what will help me show you what to think about when predicting time-scales in SEO. (Find here a useful resource for On-line marketing excel tips.)

Background

Predicting time-scales for SEO is some what a holy grail, it can make the difference between a business surviving, clinching a good sale or just being able to visualise SEO as a viable marketing method, so why isn’t it just straight forward?

Well simply there are so many factors which go into a website ranking that it is initially impossible to predict time-scales, factors such as:

• age of Site
• Size of site
• No. of links
• Quality of links
• Anchor text distribution
• On page optimisation
• Site speed
• Yadah!
• Yadah!
• Yadah!

To predict time-scales in SEO most marketers use their experience of the types of sites they have dealt with before, what the market is like and then put a potential time-line for when a website will achieve results. This is very difficult as after analysing the competitive landscape it can change very quickly, which of course changes how long it will take to get results.

The Research

So I put together an excel sheet with 13 websites across 60 keywords, I limited to this due to the sheer time-scale involved in collating the data, however I do think that there is some useful information you will find below.

For each website I detailed the following:

• URL
• Category
• Keyword
• Price bracket
• No. of Competing Results
• 12 Months of results
• No. of Months to achieve 1st page
• Increase in Positions (After 12 months)
• No. of links site hard at start
• No. of links site had at end
• No. of links increased
• Site Age

Gathering this data allows us to diagnose a lot of information so lets take a look at what the information says.

Firstly lets look at the graph of all results. (Are tracking tools stopped at 100 so where something was not ranked I labelled it as 101)

(The graph above simply shows all the results over time, the results were taken once each month.)

The above doesn’t really tell us anything apart from how volatile results are, you can see that some results get to the first page and then fall down before finally settling in the top 10 positions.

The Results

So what can we deduce from the results? Let’s take a look at how the increase in links that a website has affects how quickly it will take to get results. This is an “iffy” subject as if you are buying links then producing too many links too quickly can be harmful, however if you carry out a good viral link bait scheme then the number of links you get doesn’t full under the same criteria.

Increase in links

You can see from the graph that the overall trend line shows that the larger the increase in links the quicker you will get results. (Obvious right?) However the graph is quiet erratic and shows you why predicting time-scales is so difficult.

Site Age

You can see by this graph that the trend line is quite straight, I will say that the newest website did take one of the longest times to gain results, but the trend line does show it quite even. In my opinion this is also dependant on the keywords and industry (Visualised later in the post.)

By Category

Above I categorised all keyword by the top level Dmoz categories, so they are a loose fit, but I would agree with the graph in some parts, newer areas such as technology and Internet are highly competitive for natural link portfolio’s although I would say that finance and real estate are very competitive especially for more manipulated link portfolio’s.

Age and Category

This graph simply visualises the categories and site age, to me it shows that the younger sites in less competitive industries get their results quicker. This is backed up below in a graph to show no. of competing pages by category, you can see which categories have less competition.

Competing Pages

The graph above simply shows that on average phrases which have less competition in terms of results per search will achieve first page listings quicker.

Price Bracket

What I mean by price bracket is how much investment each site has put in. You will be able to see in the graph below the average time-scale to achieve listings via price bracket they have allocated to SEO and the number of positions increased.

You can see that the number of months to achieve listings is less per price bracket, this to me is down to simply less spend is required in less competitive markets.

Concluding the data

So if you want to try and predict how long it will take to achieve first page listings you need to think about all the above factors, Category / industry, no. of links, the number of competing pages per search query and how much of an increase in links your site needs.

As an average figure to look at use the following:

It shows the average minimum and maximum values from the data to achieve 1st page listings in Google in terms of the number of months, increase in links required i.e 10 times as many links as your site currently has and how this relates to an increase in positions.

If there are any mathematicians out there who want to add a formula to this I would be grateful @topnotchseo @rikweber 🙂