{"id":27370,"date":"2023-10-16T05:26:32","date_gmt":"2023-10-16T13:26:32","guid":{"rendered":"https:\/\/www.intentwise.com\/blog\/?p=27370"},"modified":"2024-02-06T07:01:22","modified_gmt":"2024-02-06T15:01:22","slug":"n-grams-how-to-identify-search-terms-that-drive-growth","status":"publish","type":"post","link":"https:\/\/www.intentwise.com\/blog\/ad-performance-optimization\/n-grams-how-to-identify-search-terms-that-drive-growth\/","title":{"rendered":"N-Grams: Finding the hidden gems in your search term data","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"\n

Most shoppers start their Amazon purchase journey with the search bar. Understanding the search terms customers are using to find you is therefore core to your advertising strategy. <\/p>\n\n\n\n

The problem is, analyzing your search term data can be challenging. <\/p>\n\n\n\n

What if you could take your search terms one step further? <\/p>\n\n\n\n

Recently, we at Intentwise built a way to detect new patterns in your search term data. We created a feature that identifies the specific words and phrases that perform best across all of your terms. <\/p>\n\n\n\n

With a glance, you can answer the questions like: What words and phrases are performing well, and which are not? Or: What words and phrases are not currently part of the keywords you are advertising on?<\/p>\n\n\n\n

You can use those answers to optimize your search terms. Negative match the low-performing words and phrases, and add the appropriate ones you are missing as keywords.<\/p>\n\n\n\n

That new feature is called N-Grams<\/a>. <\/p>\n\n\n\n

Wait, what is a N-Gram? <\/strong><\/h3>\n\n\n\n

N-Grams can be a little confusing, but we promise they are well worth understanding. Bear with us a bit. <\/p>\n\n\n\n

At their most simplistic, N-Grams are search terms, broken down into parts.  <\/p>\n\n\n\n

To create a N-Gram, you take an existing search term, and split it into its component parts. Then, roll up performance of those component parts\u2014those N-Grams\u2014to identify high- and low-performing phrases.  <\/p>\n\n\n\n

Let\u2019s start with a very basic example. You\u2019re a brand that sells sneakers, and one of your top search terms is \u201crunning shoes women.\u201d <\/p>\n\n\n\n

First, break it up into all of its component parts: \u201crunning,\u201d \u201cshoes,\u201d \u201cwomen,\u201d \u201crunning shoes,\u201d and \u201cshoes women.\u201d These are each N-Grams. <\/p>\n\n\n\n

Next, analyze the performance of those N-Grams across all<\/em> of your search terms that contain them. Let\u2019s take \u201cshoes women\u201d as an example. As we discussed, that phrase appears in the search term \u201crunning shoes women.\u201d But it probably also appears in \u201ctennis shoes women,\u201d \u201cshoes women arch support,\u201d and \u201cshoes women running.\u201d<\/p>\n\n\n\n

When you create a N-Gram \u201cshoes women,\u201d you can then analyze the performance of all of the search terms that contain \u201cshoes women.\u201d <\/p>\n\n\n\n

Why does this matter? <\/p>\n\n\n\n

At scale, this data can tell you exactly which component parts are working well for you, and which are not. Let\u2019s say you discover that your search terms with the N-Gram \u201cshoes women\u201d actually perform best, while the N-Gram \u201crunning shoes\u201d is associated with some of the poorest results. <\/p>\n\n\n\n

You can then start to take the necessary optimization actions.<\/p>\n\n\n\n

What we\u2019ve outlined above is a very simple example. But imagine running this at scale, across thousands of search terms. <\/p>\n\n\n\n

What can N-Grams actually do for my brand? <\/strong><\/h3>\n\n\n\n

There are two immediate actions you can take using N-Grams <\/p>\n\n\n\n

#1. Negative-match low-performing keywords <\/strong><\/p>\n\n\n\n

Let\u2019s say all of your running shoes have laces. When you analyze your search terms at the N-Gram level, you will be able to split up search terms like \u201cwomen\u2019s running shoes without laces\u201d and \u201cwomen\u2019s tennis shoes without laces,\u201d and see that the N-Gram \u201cwithout laces\u201d is associated with some of your worst performance. <\/p>\n\n\n\n

You can then negate the N-Gram \u201cwithout laces\u201d to stop wasting money if needed.<\/p>\n\n\n\n

#2. Identify new top keywords to add<\/strong><\/p>\n\n\n\n

Let\u2019s take the example above, but flip it. You\u2019re still a sneaker brand that only sells shoes with laces. But let\u2019s say you have really great arch support. <\/p>\n\n\n\n

The script will split up search terms like \u201cwomen\u2019s running shoes with arch support\u201d and \u201cwomen\u2019s tennis shoes with arch support,\u201d and uncover that the N-Gram \u201cwith arch support\u201d is associated with the highest conversion rates. <\/p>\n\n\n\n

Best part, it will flag that you don\u2019t actually have \u201cwith arch support\u201d as a keyword in your campaigns already. <\/p>\n\n\n\n

Now you know that you should add that N-Gram to your keyword list.  <\/p>\n\n\n\n

You might be wondering: What does this all actually look like? <\/p>\n\n\n\n

At Intentwise, we just rolled out N-Grams in our Ad Optimizer product<\/a>. We\u2019re extremely excited about it. <\/p>\n\n\n\n

Here\u2019s a view of how this appears in our platform: <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

We also make it easy to filter out the phrases that are performing badly (\u201cN-Grams with zero revenue\u201d) as well as the ones that aren\u2019t currently in your keywords (\u201cN-Grams not in keywords\u201d).<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

That way, you won\u2019t have to waste time combing through thousands of search term results. <\/p>\n\n\n\n

How do you use N-Grams? <\/strong><\/h3>\n\n\n\n

What we\u2019ve described above is only the beginning of N-Gram-based analysis. <\/p>\n\n\n\n

Eventually, we think N-Grams are going to unlock much bigger systemic analysis for brands. <\/p>\n\n\n\n

Say you have a series of N-Grams like \u201cred running shoe,\u201d \u201cblack running shoe,\u201d \u201cgreen running shoe.\u201d Soon you\u2019ll be able to identify that \u201cred,\u201d \u201cblack,\u201d and \u201cgreen\u201d are all part of an attribute called \u201ccolor.\u201d <\/p>\n\n\n\n

Then you can use that as a lens for analytics. You can instantly figure out how much you spend on a particular color, and which colors perform the best and worst. <\/p>\n\n\n\n

So how do you use it? <\/strong>The problem, of course, is that analysis at the N-Gram level is going to require a lot of data processing. You\u2019ll be splitting up thousands of search terms into N-Grams. <\/p>\n\n\n\n

You can\u2019t use Excel for it. <\/p>\n\n\n\n

To convert search terms to N-Grams, you\u2019re probably going to need a software partner. Intentwise now offers an N-Grams feature, which we\u2019re quite proud of. <\/p>\n\n\n\n

But even if you don\u2019t work with Intentwise, we think N-Grams will take your search advertising optimization one notch up.<\/p>\n\n\n\n

(Want more content like this? Subscribe to our newsletter<\/a>.)<\/em><\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

Most shoppers start their Amazon purchase journey with the search bar. Understanding the search terms customers are using to find you is therefore core to your advertising strategy.  The problem is, analyzing your search term data can be challenging.  What if you could take your search terms one step further?  Recently, we at Intentwise built […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":22,"featured_media":27371,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","cybocfi_hide_featured_image":"","footnotes":""},"categories":[21],"tags":[96,155,180],"class_list":["post-27370","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ad-performance-optimization","tag-mobile","tag-newsletter","tag-search-terms"],"yoast_head":"\nHow N-Grams can optimize your search terms<\/title>\n<meta name=\"description\" content=\"Understanding how your search terms are performing\u2014and which new terms you should target\u2014is hard. 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