How to use machine learning to improve UX and on-page SEO

As voice search changes the way consumers interact with brands, businesses must actively look into upgrading their marketing stack with newer tools and best practices

27Mar

With major algorithm updates being rolled out every other month, SEO has become a game of keeping up and anticipating changes.

The latest and the most significant disruption for marketers has been voice search. Now that Alexa, Google Assistant and Siri are listening to user questions and learning how to respond to more complex commands, businesses should prepare for this new type of no-screen selling experience.

Just how big is the voice search opportunity? Per Google, 72% of voice speakers owners are using their devices as part of their daily routine. What's even more promising is that a lot of voice users are very interested in interacting with brands:

Survey question: What voice-activated speaker owners would like to receive from brands?

Source: Think with Google

Commuter commerce is another multibillion dollar opportunity for brands. According to the Digital Drive Report 2019, 53% of drivers interact with voice assistants on the go. More than 30% of respondents also regularly use voice technology to place a drive-through order for food, coffee or groceries.

The challenge for businesses, however, is that when voice searches are conducted through AI assistants, only the first several search results stand a chance to be rendered to the user. Getting on that first page of Google SERPs is critical. So how do you win over AI? Your best bet is to rely on the same technologies the smart assistants are using – machine learning (ML) and deep learning. Below you will find the four use cases of ML and AI toward on-page SEO and UX.

1. Switch to predictive analytics for richer customer insights

Traditionally, marketers have used customer personas to determine how they will deliver value to their target audience, based on the descriptive online analytics solutions (e.g., Google Analytics) and via offline methods (e.g., focus groups, interviews).

However, customer journeys today have become more complex and omnichannel. One shopper can experience multiple touchpoints with a brand before converting – all taking place through different devices and channels. To capture and analyze those behaviors, businesses need more advanced tools.

Predictive analytics solutions allow you to capture a wider range of customer activities online – what they have looked at and purchased, where they hang out online, and predictions of what they may seek going forward. All of this data can be gathered, sorted and spat out based on specific queries and transformed into applicable business insights. Predictive analytics can help you determine:

  • The products and services that should be pitched to niche audience segments.
  • The types of content that marketers should be creating to engage with buyers at different stages of their purchase journey.
  • The keyword opportunities worth pursuing to attract segment X.
  • Improvements that can be made to the overall user experience to drive further conversions.

2. Prepare your content for voice search

No longer is a searcher simply typing in "light fixtures". They are now speaking to their smartphones or Alexa units with phrases such as: "Alexa, find me stores that sell kitchen light fixtures in Dallas, Texas". Alexa will then conduct a search using the terms "kitchen light fixtures" and "Dallas, Texas".

Companies are now faced with the new task of optimizing for those natural-language queries with little-to-no guidance or tools from Google and the like. So far, several things have proven to help business rank well in search results for spoken questions:

  • The language of voice search is critical to the content that you produce. AI platforms respond in human terms and they use the vocabulary in the search terms to find page texts that have the best match. Marketers will have to research and anticipate the words that a searcher will use to find them. Specifically, focus on answering the who what, where, when and why types of questions associated with your industry.
  • A site must be fully mobile-friendly, because more voice searches will be conducted on smartphones. If a company relies on local traffic (e.g., a restaurant), Google My Business profile optimization will play a key role.
  • Craft your business FAQ pages carefully to answer those "W" questions mentioned above. They should include those long-tail keywords that are used in voice searches. All other landing pages must do likewise.

3. Invest in personalization/recommendation engines

If you wonder how Amazon recommends additional products for you to purchase, it is the result of analyzing your individual purchasing behaviors, comparing them with other consumers who have purchased those same things and then generating suggestions for you personally. All of this is the result of AI and ML.

Building or adopting a recommender engine may seem like a costly endeavor for brands. However, the ROI of such systems are rather lucrative:

  • 48% of consumers spend more with an e-commerce company offering a personalized shopping experience.
  • Product recommendation systems deliver a 23% lift in conversions rates for web products on average.
  • 71% of consumers state that personalization would influence their decision to interact with the brand's emails.

You can achieve similar results by personalizing messages and content even in real time, providing a user/customer with the personalized experience based on their needs. AI can also be used to determine what action a business should take next to move that target further through the buyer's journey.

4. Deliver better customer support without increasing your team size

Nothing can kill a relationship with a potential or current customer more than a poor customer support experience.

Traditionally, businesses have used call centers, live chats and email to answer questions and resolve issues. These are still good venues, but AI and ML bring great new technologies to this process and can result in great savings at the same time.

Gartner predicts that by 2020, 85% of customer support relationships will occur with no human interactions. Enter the world of chatbots. In fact, they are already here in so many ways – everything from Poncho the weather forecaster, who learns, adapts and converses with and entertains his users, to Tacobot, who takes orders remotely and suggests more menu items. The combinations of AI, ML and natural language processing (NLP) allows these bots to continue to learn and improve interactions with and support for users.

As AI and ML continue evolving, there are almost unlimited opportunities for companies to retrieve and use data and analytics to gain insights on their customers, to craft amazing customer interactions through meaningful messages, and to enhance customer engagement and experiences. In terms of SEO, new search activities of consumers will drastically change the landscape of getting "found" and achieving top rankings.

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