The AI-Powered SEO Process- Enrich

Search engine optimization, or SEO, has changed significantly in the past few years. Thanks to the power of machine learning and artificial intelligence, the only way to build a sustainable, long-term SEO strategy is to create content people want to share. To combat these new trends, we need to employ our own machine learning technology to find what works and perform modern SEO at scale.

modern ai powered seo process

In this post, we’ll look at step 2: enrich.

Enriching Our Knowledge

One of the most difficult parts of SEO is understanding what our audience is searching for. Traditional SEO keyword tools tend to produce short, broad keyword research, which is fine for things like pay-per-click advertising. However, audiences search very differently today than they did even a few years ago.

For example, thanks to screenless devices like Google Home and Amazon Echo, as well as voice interfaces on mobile devices like Siri and Cortana, audiences now search with much longer phrases and complete questions. Whereas in the past a querant might have typed “coffee shop Boston” into a desktop search interface, today they would ask, “Hey Google, where’s a coffee shop near me that’s open now?” and receive a more specific, useful answer.

How do we identify what people really ask? We look in two ways.

People Ask Publicly

Use machine learning tools to identify and digest massive quantities of conversation about our themes and topics. When people talk about coffee shops in Boston, what do they say at scale?

text tokenization

An example of text tokenization and processing.

Using any data-friendly social media monitoring tool, look at 50,000, a million, a billion conversations people have in public. Digest them down with text mining software to understand what the most common 5, 10, or 20 word phrases are.

People Ask Privately

The greatest repository of questions people don’t ask aloud is held by search engines. Using software like Microsoft Azure’s Web Suggest API, we can take our own inventory and our public research data to determine what else people might search for:

bing api

The Bing/Microsoft Azure Web Suggest API demo interface.

While Bing is not the market leader in search engines, it still has around 30% market share and has open APIs we can query. Additionally, many voice-interfaces use Bing for search results, so it’s better suited for this type of data analysis. Using the web interface is fine for a few search terms; for large quantities of data, we’ll want to build code against the API.

Analyze the Gap

We should have three sets of data at this point:

  • Our internal data from step 1
  • Public questions via social conversations and public content
  • Private questions via search API

We now ask three questions of our data as we compare and contrast it.

  • What’s expected, the common ground, the things that show up in public and private that should be in our internal data?
  • What’s anomalous, unexpected things we found in public and private data?
  • What’s missing, things that we would expect to be in public and private data, but aren’t?

Based on this gap analysis, we will take the findings and validate them in the next step. stay tuned!


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