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What is intent intelligence?
Can intent even have intelligence? Well yes, in the brave new data-science ecosystem of AI, those two words do work together. If you’re thinking intent intelligence might be information about people’s intentions on the online buyer journey, you’re on the right track.
Intent intelligence is high-quality knowledge culled from data gathered from the activities of website or app users (ecommerce shoppers, for instance), with analytical technology continually learning from what they do and assessing whether they possess viable buyer intent.
So intent intelligence is not just a key real-world thing, it can be a super valuable tool for use cases from creating the right sales intelligence to avoiding pain points in order to create a great customer experience, whether for B2B sales or consumer transactions.
The quality of gathered intent intelligence is impacted by both artificial intelligence and intent data. AI and intent data are both used to figure out buyer purchase intent and analyze buyer needs, but they do it in slightly different ways.
AI — machines simulating human intelligence — continues to be put into practice in and gain traction for companies looking to improve their user experience and bottom line.
Of course, nobody knows quite where AI in its many forms is going to ultimately take humanity, which is an ever-unsettling thought.
For now, though, let’s focus on AI’s impact on customer data as it relates to online retail. It can be particularly instrumental in identifying a shopper’s intent to buy, and then prompting appropriate marketing team or sales team support, such as by popping up a data-driven on-screen chatbot to “discuss” the user’s perceived needs.
AI is ubiquitous: it can be digging in many digital places at once, gathering numerous consumer and B2B fingerprints. AI tools also excel because they continually improve and adapt what they know based on factors such as competitive pricing and seasonal changes.
Where are we with AI in the corporate world? According to technology futurist Bernard Marr, in 2023, artificial intelligence will “become real in organizations. No-code AI, with its easy drag-and-drop interfaces, will enable any business to leverage its power to create more intelligent products and services.”
He points out that some companies have already been getting their digital feet wet making product recommendations with the help of AI-based algorithms. One example is online clothing retailer Stitch Fix.
Among other things, Marr predicts that AI will:
In other words, AI is ascending, and ready to start blasting off.
Machine learning is defined by writer Sara Brown of MIT Sloan as “a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed,” and to get progressively better with new learning opportunities as they process more data.
“When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously,” she explains.
Machine-learning models operate on algorithms; they can’t make sense of words, so in order to have content “speak their language,” words are converted into mathematical equivalents (vectors). This enhanced level of machine understanding is often achieved with the help of natural language processing (NLP) technology. The relationships between words are preserved, so for instance, two related words are seen as more closely related than two unrelated words.
The process of collecting data is obviously not a static undertaking, since data is ever changing. When new information becomes available, it’s easy for AI-based software to add it to existing data and then draw accurate conclusions from the combined information.
Therefore, machine learning also encompasses an additional, key dimension: continual learning (CL). This term is self-explanatory and also used interchangeably with the similar-sounding continuous learning, incremental learning, lifelong learning, continual lifelong learning, and online machine learning.
Continuous machine-learning models don’t just chug around scarfing up initial data they find. Left to “their own devices,” they keep right on gathering and fine-tuning their previously learned knowledge to improve the efficacy of their treasured data troves.
And then, as a human being might do in terms of formal learning, career development, or personal development — with the confidence that comes with learning new skills, gaining new competencies, and understanding something more comprehensively — these basically unsupervised digital beings (unlabeled datasets are analyzed and clustered by machine-learning algorithms) can start to adapt their “thinking” to incorporate the new knowledge.
With constant learning activity, they can start updating their overall prediction models. A process that, over time, as with a human’s continual learning trajectory to log benchmarks in an educational process — which, for a human, might include things like undertaking training programs, attending seminars such as on professional development, taking on-demand online courses, watching webinars and listening to podcasts, doing self-directed learning, and getting certifications — makes them considerably more intelligent (that is, accurate as prediction models).
The upshot: without the added dimension of the continuous online learning experience that facilitates machine intelligence, AI would not have the adaptability to make predictions with such authority.
Now let’s delve into intent data. That’s what you’d hope to get from the application of AI processes such as machine-learning tools: a sufficient amount of accurate info about potential buyers that indicates what they could likely do next. Details that speak to and illuminate the depths of people’s intentions. That’s intent data.
Intent data typically revolves around particular topics, products, and services. And it zeroes in on the (positive or negative) sentiment associated with topics, supplying companies with a more comprehensive view than traditional profiling or behavioral data could.
With software powered by artificial intelligence and advanced algorithms, an online retailer can decipher what users are looking for and whether they’re likely to buy. This is made possible because 97% of consumers are doing their due diligence — they’re researching products and services online before making a purchasing decision so that they’ll be less likely to have buyer’s remorse.
One area where intent data can prove to be a gold mine for marketing initiatives is user search. Whether for SEO, conversion rate optimization (CRO), or other disciplines, intent data provides super-specific insight without appearing to be stalkerish. Each user’s online explorations invisibly provide the necessary intent data, and the retailer simply needs to collect it, interpret it, and use it to their best advantage.
Being able to assess customer intent data can substantively help marketers and decision makers identify potential prospects and pinpoint when they’re ready to buy. Not only that, it can give them the direction to, for instance, confidently create the right ad targeting copy, personalized to effectively reach prospects and qualified leads. It can enhance lead scoring to give salespeople a leg up.
Buyer intent data can be amassed for each individual shopper’s buying journey with the help of tools such as natural language processing (NLP) and semantic search. Intent data is collected as people click around, using identifiers such as IP addresses and browser cookies, plus clicks on keywords and content.
So if someone’s shopping-content-consumption process includes reading online reviews, perusing blog posts and clicking links that land them on other content, watching videos, gaining access to gated material, and comparing products, all of these data points would be duly noted to estimate intent.
For reference, there are three types of intent data: first-, second-, and third-party.
It’s easy to confuse the concepts of buyer intent data and predictive analytics. And that’s common because predictive analytics and buyer intent data are both used to help companies determine buyer purchase intent and needs. They go about it in slightly different ways, though.
Predictive analytics (or predictive intent) utilizes Big Data to make algorithmically calculated predictions about who will purchase which items and when. Information is collected from various data sources, such as customer profiles and sales, incorporating data mining, data modeling, machine learning models, historical information, and AI. It’s based on historical data, not up-to-the minute browsing or shopping activity, and it relies on human expertise, which is obviously pretty limited, to reach a target audience in the best way.
Buyer intent data, by contrast, is created from tracking and recording actual shoppers’ online journeys. Because it’s more reflective of what’s actually happening, you can use intent data to do a better job of helping marketers and sales reps use relevant content to identify potential customers for lead generation and pinpoint when they’re ready to buy.
To summarize, gathering buyer intent data is less complicated (with fewer integrations needed), and it immediately supplies companies with specific real-time insights.
The main advantage of using buyer intent data is the AI component machine learning, which can consult a staggering number of current data points and thereby achieve so much more than a predictive model, assessing individual buying intent in real time. Accurately applied intent data from trained models can tell marketers what individual shoppers are doing, allowing them to assess buying signals to quickly figure out whether a prospect is worth pursuing. When it comes to pulling ahead of the competition, that kind of “insider information” can be pretty unbeatable.
Where are most companies these days in terms of effectively utilizing these two tools — AI and intent data?
Many have been embracing AI and enjoying the resulting problem solving and brighter outlooks. For instance, more than half of executives (54%) say they’ve already increased their companies’ productivity by implementing AI solutions.
As for buyer intent data, an estimated 76% of B2B companies are currently using it to guide their marketing and sales strategies.
That’s a great start. The question is, how many online retailers that want a competitive edge are using both of these tools together, in a coordinated way, to create highly accurate intent intelligence and get the best results for their bottom lines?
The key is using both. Both of these tools utilized together can:
When you’ve got the right intent intelligence on your users, the sky’s the limit.
One way you can put it to use: create effective intelligent search for your site or app users.
Correctly analyzed user search intent reveals what someone wants when they enter a search query; it provides very specific insight without being stalkerish. Intelligent search is what you get from a search engine that has the ability to understand user intent based on what the person enters in the search box, plus use machine learning algorithms to optimally rank search results.
Like other computer-science-related domains, intelligent search combines techniques including NLP and machine learning. For example, it can make connections between semantic terms that a traditional search engine (one that’s simply looking at keywords) would be unable to discern. The searcher indicates their intent, and all companies have to do is note it, identify it, and correctly interpret it. When it comes to customer retention and revenue, that can be a giant game changer.
You can harness the benefits of both AI- and intent-data-powered search with Algolia. We’re the industry’s most intelligent search platform, built with self-learning AI running on trillions of searches.
We leverage machine learning (and reinforcement learning based on user behavior) to dynamically re-rank and personalize results, detect data trends, synonyms, and categories, and provide recommendations. And soon, we’ll be offering vector-based semantic search that understands concepts and longer queries.
For instance, our technology continually learns from how users rewrite their queries in order to suggest synonyms, so that the next time they search, it could take less time. Our customers and partners leverage our AI-powered search intent prediction model to significantly improve their click-through rates (CTR) — sometimes by phenomenal percentages.
Sr. Director, Digital Marketing
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