STEP BY STEP GUIDE TO FREE TEMPLATE
DOWNLOAD FREE TEMPLATE HERE
The first step is to harvest all the relevant search terms from the various tools you wish to use. The best place to start is Google Adwords, then move on to other tools you may wish to use, such as Uber Suggest. You should end up with a list of terms, along with their associated monthly search volumes.
You will inevitably have duplicate search terms in your list, which need to be removed. A good way is to use the remove duplicates function in Excel. You should end up with a list of unique terms, along with their associated monthly search volumes.
Next, paste them into the ‘Categorizer’ tab in the keyword research planner, into ‘Search 3 Term’ and ‘Average Search Value’ columns.
All of your terms need tagging as either ‘Negative, ‘Brand’, ‘Direct’ or ‘Related’. Since we have thousands of search terms to tag, we need an easy and quick way of doing so, without having to tag each search term individually. We do this by using the ‘Group Tag’ tab. Any terms you want to tag as negative, by adding that word or phrase to the ‘Negative’ column in ‘Group Tag’, you will automatically be tagging any search terms that contain that word or phrase also as negative.
This is the case for the rest of the categories – ‘Brand’, ‘Direct’ and ‘Related’. However, there will be some cases whereby certain search terms can contain words that are say, ‘direct’, but the rest of the words in the search term might make the whole phrase ‘related’, so there are rules in the template whereby some tags override others.
To put this into context, say we have ‘dress’ and ‘evening dress’. ‘Dress’ we will categorize as ‘direct’ if we are a fashion brand selling dresses. But ‘evening dress’ is related, since there is some other elements of indication of intent here. So we add ‘evening’ to the ‘related’ column in the ‘Group Tag’ tab. This will ensure the search term ‘evening dress’ is tagged as related, rather than direct.
By going through around the first one hundred search terms on your ‘Categorizer’ tab, a lot of search terms will be auto tagged as they will contain words already grouped. Click ‘Tag Search Terms’ on the ‘Categorizer’ tab. The ‘Tag Usage’ column will turn green on any search terms captured by the tagging you have already done. Red cells indicate search terms not tagged, where words or phrases you’ve already tagged don’t capture these search terms. Filter on those terms, and start the tagging process again on terms not captured.
Again, it’s highly unlikely you’ll have to tag each term individually. By going through another round of tagging, you’ll capture more and more terms. Do this until all terms are tagged, before scanning quickly that all of them make sense.
Next move on to the ‘Keyword Research’ tab. Click ‘Insert Unique Strings’. This will pull all unique words from the ‘Categorizer’ tab, essentially pulling the words out from the phrases individually.
We now need to sort all these words into groupings to make sense of them and the user intent behind them. There are two default groupings – ‘Non-semantic’, words like ‘of’, ‘and’ etc, we put in here, and ‘Target Search Term’, which should only comprise of a few words that are very high-level and don’t provide much information. In the example of the fashion retailer, the ‘Target Search Terms’ would be ‘clothes’, or ‘clothing’.
We can start with around 6-10 groupings, depending on the field or industry. To begin with, start with core groups, such as product types for a fashion retailer, ie ‘dresses’, ‘tops’, ‘shoes’ etc. But don’t be afraid to add or change groups as you work through categorizing your list of unique strings. Add your starting groups on row 2 in the template, starting after the ‘Target Search Term’ column. Your starting group titles will run along row 2 horizontally.
Starting with your core groups, work through your list of unique strings on the ‘Keyword Research’ tab, putting a ‘1’ in the relevant cell under the correct groupings. For example, you may have words like ‘cocktail’, or ‘shift’, which relate to dresses, so manually place a ‘1’ under the ‘dresses’ category. The exact word will also be categorized, eg ‘dress’ is still categorized as ‘dresses’.
When working through your unique strings, you will realise there are lots of words that don’t relate to a specific product. This is where you will start to add further groupings. In the fashion example, you may end up with categories like ‘materials’, ‘price’, ‘pattern’ and ‘fit’.
Once all unique strings are tagged in ‘Keyword Research’, click ‘Scores’. This is where all of the scores from each search term are added together to give individual words an overall scoring.
The ‘Keyword Research Overview’ tab now shows all of our words and groupings in a readable format. It’s worth at this stage reading through to check groupings make good sense. It is also here we can review ‘Group Keyword Count’, ie the amount of words in any given group, a suggested length and number of paragraphs of a given piece of content relating to the topic of the group, and the priority of that topic, all based on the scorings.
If we already have content on our website, we can run it through the ‘Text Match Analysis’ tab, which shows us how many of the keywords, if any, we’ve already hit in our content. This can be done by using a tool such as http://boilerpipe-web.appspot.com/, which will crawl our webpage in terms of text content.
The ‘Text Match Analysis’ tab then calculates in %, how much of our content matches the key words. It’s useful to do this before and after any text optimizations, to see the improvement.
DOWNLOAD FREE TEMPLATE HERE
DOWNLOAD THE WHITEPAPER PDF HERE