Voice search and digital assistants are becoming ever-present in everyday life. Voice search, in particular, is increasingly becoming part of consumers’ interaction with the internet, and information is increasingly being sought by a spoken request to a digital assistant. This change fundamentally affects how search engines function and how content can be optimized for them.
Voice search has become increasingly popular since the launch of voice-recognition technology on China’s first search engine in 1999. In 2017, millions of people in the UK used voice search at least once a week. Instead of typing a query into a search engine, voice search allows users to search the internet by speaking to a device with voice-recognition technology. Searches are mobile-focused, with search queries emerging when driving, while walking, or even while in social situations. Voice search is often used for local, navigational, and general questions. Digital assistants, which are part of devices such as smartphones, interactive speakers, or smart home products, return spoken or visual results in response to voice queries as well as the results that are sent to the users’ linked email or apps. While generally used for more complex tasks such as transcribing spoken words, sending messages, or carrying out tasks, digital assistants also conduct spoken or voice-related searches based on querying search engines.
Typically, voice search engines are integrated into three main types of smart home products, including smart speakers, smart screens, and smart displays. Smart speakers are hands-free speakers controlled by voice and also feature voice-controlled smart assistants. Smart speakers are generally docking stations for a voice-activated virtual assistant. Users primarily interact with smart speakers via voice communication. The first smart speaker was launched in 2014 and is equipped with an intelligent personal assistant, with sub-brands including Dot, Show, and Tap. Alternatively, virtual assistants use natural language processing and machine learning to enable a broader range of capabilities and a more conversational format. The primary function of a smart speaker is as a voice-controlled reaction tool and information terminal in a variety of situations. In 2017, the number of smart home passenger vehicle shipments stood at approximately millions of units worldwide. Other products in the IoT space use voice command, including smart earbuds, smart headphones, and in-car digital assistants.
The Evolution of Voice Search Technology
Speech recognition and voice search technology has seen the natural progression of becoming smarter and more user-friendly, with the first system for voice search emerging on the Personal Dictation Machine in 1962. From this point, several major milestones were achieved in the development of speech recognition technology, which have led to the voice search capabilities that are currently available, including entirely automated speech recognition, which is now available on a range of software devices and smartphones. Early breakthroughs in the 1980s and 1990s allowed for the differentiation between parts of speech and to differentiate speech from static noise, and the development of the Weighted Finite-State Transducers in the 1990s. Today’s most advanced speech and voice recognition technologies are largely founded on advancements in both machine learning and AI and are able to analyze other complexities of natural speech.
Today’s standard voice search software includes speech-to-text applications that provide audio translation in both mobile and desktop formats, as well as the updated voice assistant systems that provide both device and system functionalities. The product arrived with the launch of a major voice assistant in 2012, becoming the present incarnation in 2016. A major company released their smart speaker system in 2014, rolling out both non-pro version services today that we would describe as complete smart speakers capable of connecting to other household and lifestyle technology. The runaway product taking the world by storm at the moment is the visual smart display. Since the first voice assistant incarnation was created in 2009, a large fleet of devices (with increasingly more recent innovations) are eligible for voice assistant functions. Getting into both Android and Apple’s hardware allows each of these services some access to a global base of over one billion users. This significant world presence of the main players indicates how conventional it has now become for consumers to change their retail-based queries to their tablets – and increasingly other devices besides smartphones – and get instant vocal results. This ease and ready adoption by over 40% of adults attests to voice search’s significant advancement in the software arena, making it a key venture for revenue-generating businesses.
NLP Enhancements and Voice Search Accuracy
Modern voice search systems greatly depend on the ability to develop better thinking systems capable of utilizing more natural language, which is central to increased user satisfaction in the relevance of returning responses. Natural Language Processing enhancements center on educating machines on how to better understand and interpret the complexities of spoken human language. NLP is largely a component of voice search, facilitating the move towards more accurate voice detection, speeding up word recognition, or omitting the learning period. While the vast majority of businesses are not aware of the search engines’ improvements in this component, the user sees a vast improvement, and the appeal in using voice search increases. Improved NLP leads to the satisfaction and trust of the consumer that the engine will respond exactly as it was spoken.
There are numerous advancements in NLP in countless search engine components and parts. Progressive algorithms, such as word segmentation, are showcased in deep neural networks. Improvements are ongoing on acoustic models, the component of the engine where NLP is most significant, so more accurate word recognition ultimately improves the overall resulting device interpretation. Machine learning frameworks are constantly evolving in a wider context to improve voice detection. These frameworks include significant decreases for more accurate recognition rates on training times. The tie between more accurate NLP and more accurate voice search and detection is seen in various investigations, surveys, and real-world examples. There are no companies today that have yet to accomplish the improvement of 100 percent word recognition and interpretation.
Content Optimization Strategies for Voice Search
Given that voice search is conversational, content needs to be conversational in tone when optimizing it. This will enable it to match the voice search queries and, in turn, the featured snippets. When structuring voice search content, it is important to write with the user in mind in order to fulfill micro-moments and position zero requirements. Both of these strategies will assist search engines in finding the structured content to potentially populate voice queries. Utilizing structured data and schema markup, businesses can escalate the prominence of content, thus assisting the website in appearing in response to voice commands. Creating Q&A-type pages allows content writers the ability to directly answer a given question on a website.
In addition, voice search features local answers more frequently than a text-based query to accommodate the user’s need to find something quickly. This is an aspect content writers need to keep in mind when attempting to optimize for a voice-based platform. Local service queries and direct action queries receive the most voice search interactions. To achieve success, brands need to produce or copy this framework. A brand could create a blog post outlining five steps on how to open a savings account with them. Customer service queries could be turned into instructional videos and placed in a knowledge graph or as a featured snippet. A more complex strategy would involve understanding user intent, which is utilizing common conversational phrases and voice search questions to create content. This could involve a main column for the surrounding content, answering user intent. Managing your home can be a time-consuming and challenging task, especially when juggling a busy schedule. To find the perfect voice search solution, it’s important to optimize your website content. When it is optimized correctly, it can help improve your website’s traffic and reach.
Adapting Keyword Strategies for Conversational Queries
Since the launch of voice search on mobiles, and particularly with the rise of virtual assistants, we have seen an increasing proportion of searches become more conversational. It’s important for content creators and marketers to understand the difference to create more opportunities. If you’re looking for something in particular, you can search for individual points or pieces of information. When using voice search or a smart speaker, you may ask multiple questions, giving context to your intended search. These conversational searches are less focused on individual keywords and more focused on natural language.
We have seen a rapid increase in conversational and question-based searches with the introduction of voice search. Voice searches are more likely to be in a question format or long-tail keyword, using more words than a text-based query. They are also different in tone when typed. Therefore, to predict the performance of a keyword strategy, marketers need to understand voice search intent. The first step is to rethink keyword research. There are several ways to generate a list of more natural, human phrases. Tools allow users to search keywords and content pages and gain insight into the questions asked that may lead to their web page. Alternatively, click directly on the ‘People Also Ask’ box within a search page for a list of questions. There are sentence mixers that let you highlight the questions and different sentence structures. Given this, marketers can take these phrases and adapt a lot of content as needed. They could even return to search to copy and paste questions. This is the conversation style! An alternative to keep up to date with voice search trends is to use machine solutions and record SEO as part of the overall campaign. This will help businesses monitor search queries and track performance over time. This feature evaluates which keywords are present for voice searches. 2020 has seen the difference with this strategy from keyword-focused to conversational-driven keywords. What effect has this had? One of our clients has seen a 3-month average visibility increase of just under 50%. Another has seen their visibility for some of their voice-targeted, conversational keywords fluctuate down and then come back up by the following month, showing that this changing strategy has had a positive, long-term effect too.
Ensuring Content Discoverability in the Age of Smart Speakers
Envisioning a future where Internet of Things (IoT) devices like smart speakers quickly become ubiquitous is easy, but does this change how users search for information or how organic search results are displayed? We now have the technology to instantly check breaking news, relevant research, and personal reminders without needing to stop what we’re doing. The web will need to adapt to time-sensitive, quickly consumable, or personally curated content experiences that IoT devices enable. Voice searches are fundamentally different from traditional or mobile searches in that they are more likely to resemble “knowledge box” queries, returning a featured snippet or direct answer in response. We explicitly optimize our strategies to capture more voice search traffic wherever possible.
Most queries to voice assistants are simple, conversational, and locally driven. Local search presents an opportunity for brands to get in front of bigger audiences and can lead to site visits, real-world visits, and transactions. Even if a searcher knows the answer or checks the “knowledge box,” asking their voice assistant a clarifying question will allow them to consume the content via audio. Optimization strategies we employ address regional, national, and international concerns. To take full advantage of your site’s opportunities, optimize content now for location-relevant basics, such as in-depth local guides, audio traffic features, and long-tail keyword combinations heavy on questions and comparisons. We also prioritize mobile site functionality. Site accessibility directly impacts how often your content can be accessed on a smart speaker, which may deter users from making a return visit. It proves to both voice assistants that your site content is suitable for smart speaker distribution. Content should also be well-formatted, well-structured, clear, and designed for audio consumption and site navigation. Both short content URLs and concise page titles and H1s improve your site’s likelihood of being featured in a voice search. Optimizing for voice search requires focusing on well-rounded site optimization that emphasizes various ranking signals, including content relevance, helpfulness, authoritativeness, trustworthiness, and more. The same principles that apply to establishing a high ranking can also be optimized for voice search, indicating content is authoritative and trustworthy. Monitoring site performance through smart speakers and other digital assistants will be a key part of the future of SEO and digital strategy. Strategy must be flexible enough to evolve with the digital landscape and take advantage of new technologies. As the likelihood of voice’s influence on content discovery grows, strategies should adjust accordingly until voice-activated devices make up a significant percentage of web traffic.
AI and Voice Search: Zero-Click Searches
Whenever a user’s question is already answered on the search engine results page, and they acquire the information without visiting any websites, it is a zero-click search, and AI is helping search engines pull these accurate answers from web pages with increasing efficiency. When fine-tuning the search experience, the effect of AI answering most voice searches and quick question searches within a fraction of a second has led to a significant increase in the number of zero-click searches. Large search engines are displaying data from knowledge panels and snippets obtained from web pages or directly from verified sites. Most of the snippets are taken from the very high-ranking content on the web. Another case study of over 207 billion searches indicates that more than half of the searches do not lead to clicks on any organic or paid links. Data collates: Google Answer Box has an Answer Box on U.S. searches.
The development of voice search has demonstrated a growing share of zero-click searches. A report stated that a significant percentage of buyers use voice-activated search while surfing. An additional percentage have a preference for voice-activated searches for disease symptom searches. However, the rise of zero-click searches due to AI-powered snippets also raises a serious problem. Human attention is a zero-sum game: you only have so much time, and stuffing more in elicits rapidly diminishing returns. What works for search engine marketers is an intricate art. With every new technology, the bar goes higher. If you improve, your content marketing will survive the zero-click search cycle. If you don’t, people are going to pass by you to your rivals.
The Role of AI in Search Intent Detection
Voice search and AI are significantly assisting in trying to understand the real intent behind search queries. Do you want to use this advanced technological approach to consider search intent while optimizing your content? Great! Scroll down to see what points you should put maximum effort into while learning this advanced content optimization method.
The term ‘voice search intent AI’ indicates how the role of AI increases in classifying search intent or user queries. As we’ve seen, identifying what a user is trying to achieve with their search is a successful method to offer human-focused online content. How does voice search AI recognize user intent?
Understanding the intent of people when they input search keywords is their directive. To recognize this, they aim to understand user behavior and compare it to the user’s behavior when a similar question is asked. Enormous data is required. Containing a vast amount of data on people, Google has an algorithm that enables machines to find out what people want with their searches. The primary research into this algorithm does not specifically state how they examine data on users and their habits to determine their intent for search phrases, just that their system studies patterns and can more correctly interpret what users mean. AI classification to detect search intent is based on the following three aspects, which are the factors that can distinguish content optimization: Patterns of data: What do AI algorithms reveal as changes already in data about an individual that can help determine search intent? Ranking data, click-through rate, bounce rate. For example, a particular kind of information or website ranks well in Google as more suitable or useful for knowing or doing something the greater the search intent.
Technical and Strategic Challenges in Voice Search Optimization
The use of large language models (LLMs) for voice search and their success has become a popular subject of study over recent years. The complexity of optimizing documents for voice search can be partitioned into technical and strategic challenges. One technical challenge is the diversity of dialects, accents, and speech patterns. Speech recognition systems must still correctly transcribe spoken queries, and LLMs must capitalize on the transcription in most cases. Another technical challenge lies in the degree of marginal returns from gaining higher precision. Devices and platforms may exhibit different behaviors, meaning one behavior is likely to be dominant across multiple sources. As the device or platform capabilities and use cases grow, the different behaviors may spread out, leading to less predictable returns on precision improvements. The slight inefficiency of these models also shows it is perhaps a poor choice for simple text generation, where computing users’ received items is a complex task. Therefore, principles such as proportional loss for interpretability may not be a good fit. Thus, instead of trying to use LLMs to solve user engagement downstream, we decided to use user engagement upstream to craft superior LLMs for user queries.
Strategically, it is possible to measure the impact of the marginal gains versus the degree of experience needed to repeatedly find such precision optimization. In order to maintain quality, various levels of REELS need to be done iteratively as devices and platforms change. This necessitates a large investment in data, scripting, and partners. A full socialized strategic campaign of incorporating voice search at large, involving knowledge sharing, user sentiment, and voice of customer gathering, needs voice of customer analysis and much more. Efforts require a substantial amount of tech SEO time for analysis, scripting, and documentation. There must be collaborative strategic planning between marketing, technology, content, and creative teams. Optimizing for voice search is much more widespread than simple answer box strategies.
Emerging AI Trends in SEO and Voice Search
AI is constantly evolving and is proving a blessing for the world of voice search and its interaction with search engines. For better engagement, AI helps in generating more efficient algorithms to predict one’s behavior or search intent.
Some of these innovations are:
Personalized Voice: The introduction of this feature attaches the ability to analyze distinct voice recognition to make one’s voice assistant. The speech patterns can be analyzed to help identify what names and words are similar when spoken. A smart assistant also listens to how people interact with each other regionally, including slang, accents, and phrase structure. Adaptive Learning Algorithms: The arrival of this remarkable intelligence allows the search to be tailored by an adaptive AI learning algorithm. This adjusts to a person who searches using the results obtained in the past with a high degree of relevance. The ability to personalize will help in triggering an interaction between locals and visitors who are alike. Integrating AI in the Local Search Initiative: The ability to test new ranking factors is one of the attractive features of local search. There is also reason to believe that customization and personalization through certain actions regarding locally beneficial outcomes to date. Ultimately, AI will soon play an ever-increasing role in the development of one’s search engine and voice search optimization strategies. It is expected that enormously quick developments in AI should also change the landscape of SEO and voice search, affecting visitor expectations and the level of services they hope to receive. Carry on developing fresh suggestions and concepts to keep pace with and remain relevant in this field.
