Intent-based Marketing – Case Studies, Metrics, and Effective Strategies

The proliferation of AI and automation tools has significantly increased the volume and frequency of email outreach and marketing campaigns. While marketers are integrating AI to reach target inboxes efficiently, the surge in automated outbound messages is often seen as a “white noise”—undifferentiated and frequent irrelevant communication that prospects tend to ignore, failing businesses to bridge the initial contact. Notably, 91% of all such outreach emails are ignored. And hence, intent-based marketing is gaining prominence globally.

Post-COVID, McKinsey found that 71% of B2B consumers expect companies to offer personalized communications, and 79% become frustrated when this doesn’t happen. The study iterates that companies can achieve up to 40% higher revenues by addressing client intent rather than using generic messaging. Consequently, 39% of businesses now spend more than half of their marketing budget on intent data, reporting an average ROI realization within six months.

Before dwelling on Intent-data, every email marketer must consider the above statistics, especially as AI-generated email campaigns and automated funnels crowd the marketplace, often generating “white noise” without practical conversions.

In this article, we will statistically explore the reasons behind declining response rates, examine several key performance indicators (KPIs) such as click-through and conversion rates, and illustrate how intent-based Marketing are emerging as a more effective strategy.

AI-Only Approach Fails to Meet KPIs – Case Study

A recent study by Sam Koch, published in the Journal of Business and Artificial Intelligence, investigates the performance of AI-augmented cold outreach compared to traditional human-led and hybrid approaches.

The study spanned over three (3) months, involving a B2B client offering sales development services to SAAS and private equity firms. The targeted prospects were sales development leaders at B2B software companies with an average annual revenue of $5 to $50 million. The goal was to study three distinct approaches with 2,000 prospects each and compare the performance and cost:

• Human-Alone Method: Traditional cold outreach conducted solely by human sales representatives.
• AI-Automated Tools: Fully automated AI-driven cold outreach campaigns.
• Hybrid Approach: A combination of human sales representatives utilizing AI-powered tools.

The key performance indicators (KPIs) evaluated in this study:

• Prospecting Cost: The cost associated with researching and identifying potential leads.
• Personalization Cost: Customizing outreach messages to stand out from the competition.
• Human Resource Cost: The labor cost of sales representatives.

Results: The study’s findings revealed significant differences in the performance of the three approaches:

• Human Alone Method: $350 cost per booked appointment.
• AI Automated Tools: $250 cost per booked appointment.
• Hybrid approach (Human utilizing AI tools as necessary): $141 cost per booked appointment.

(Refer to the journal for further outcomes from the study: Journal of Business and Artificial Intelligence)

This study demonstrates that while AI and automated outreach tools can significantly enhance lead generation and customer engagement, particularly in high-tech B2B companies, their success is still dependent on human expertise to refine the Gen-AI messages, monitor the output and AI models, fine-tuning and for effective data curation. 

AI Cold Outreach – Addressing the ‘White Noise’ Problem

Bloomberg reports that the Gen-AI market is projected to grow significantly, reaching $1.3 trillion globally by 2032. This trend is driving an increasing number of AI-based startups, SaaS products, and automation agencies, which are rising to help businesses integrate AI tools into their operations. 

However, as AI integration in business outreach activities scales rapidly to improve efficiency and reduce operational costs, it contributes to the “white noise” problem. The result? 91% of outreach emails are ignored!

Most AI-automated cold outreach campaigns often flood inboxes with spammy content, prompting ISPs like Google (Gmail) and Microsoft (Outlook) to restrict domains and damage deliverability.

Read More: Bulk Email Deliverability – Gmail and Outlook’s 2024 Guidelines and Enforcements

AI Cold Outreach — Negative Impact on Marketing KPIs:

AI-driven cold outreach often fails to deliver positive results, negatively affecting key marketing KPIs:

• Low Response Rates: Only about 9% of cold emails get a response due to generic, unengaging content.
• High Bounce Rates and Spam Issues: Misidentification by AI tools can lead to high bounce rates and spam emails.
• Generic Content: Monotonous AI-generated messages fail to engage recipients, reducing conversion rates.
• Difficulty in Extracting Insights: Overwhelming content volume makes it hard to find actionable insights, affecting campaign optimization.
• Negative Perception and Satisfaction: Poorly tailored outreach creates a negative brand image and reduces customer satisfaction and retention.

Given the challenges posed by AI-driven cold outreach, intent-based marketing is emerging as a promising alternative to solve these issues. 

Intent-Based Marketing — Solution to Automated Cold Outreach Issues:

Today, 98% of B2B marketers consider intent data as an essential ingredient for lead generation. In addition, 48% of B2B teams that implement intent data report a high level of success in their marketing strategies. Therefore, intent-based marketing is taking over AI-automated outreach tools as the popular go-to strategy while addressing the “white noise” problem.

Image reference: Intentifydemand 

Understanding Intent-Based Marketing

As the name suggests, an Intent-based strategy is built on a solid understanding of the purchase interests and the intent of potential customers to create highly targeted and personalized outreach content. 

Five Key Components of Intent-based Marketing Outreach:

Every intent strategy is built on five pillars: Gathering intent data, classifying intent signals (active or passive), Creating a target account profile, Content & messaging, and Advertisements.

• Intent Data:

  This constitutes behavioral data about users’ web content consumption, such as search queries and page visits. The goal is to understand what users are searching for, who visits specific pages, and what sections are the most viewed on a page. Tools like leadrebel.io help marketers track and gather behavioral, technographic, search and query data, forming the foundations for intent-based marketing and targeting. 

• Intent Signal Classification:

  User intent is categorized as active or passive based on purchasing tendencies for a targeted approach. Active intent is characterized by proactive measures prospects take to acquire in-depth knowledge about a product or service, signalling a positive intent to purchase or convert. Passive intent is mostly informational, hinting at the research phase with no urgent compulsion to decide (sales funnel’s awareness stage).

Machine learning models can often be used at this step to classify large data sets for segmenting audiences based on their journey (awareness, consideration, etc.) and to score the intent signals into active or passive, or high, moderate, neutral, and negative scores.

 

• Target Account List (TAL) or profile:

  A TAL is like building the target customer persona, a comprehensive document outlining the ideal client profile. This profile helps understand how target customers interact across social media platforms, brands, and digital ads. 

• Content, Messaging and Ads:

  Based on the intent data, signal, and customer profile, content (written, audio, or video) is built to target the specific interests and needs of the audience. This includes blog entries, whitepapers, product evaluations, and other content.

Such customized outreach content is scheduled according to prospect behavior to reach their inboxes, mimicking human-like interactions and frequency. 

• Ads and Campaign Optimization:

  Intent-based ads are designed with a mix of display, video, or audio formats, customized to the service or the product, and resonating with the specific inquiries of the audience. Intent data helps to understand prospects’ social media preferences and to optimize ads using A/B testing and real-time monitoring for better engagement and campaign KPIs.

These five components form the basis for an intent-based marketing outreach. Now, let’s dive deep into the types and methods of Intent (Signals or Triggers) data, as well as the means of collection.   

Intent Signals / Trigger Data Collection – Deep Dive:

Before delving into how intent signals are recognized, collected, and managed, let’s first understand their importance across organizations. 

A survey of 200 senior B2B marketers from large companies (500+ employees) in the USA and UK revealed that 99% utilize intent data through various tools (first, second, or third-party). Among them, 80% have established intent collection strategies that have been operational for over 2 years, with 37% maintaining strategies for over 5 years.

This highlights a mature approach among organizations to predict B2B user engagement and purchasing patterns through robust intent signal mechanisms. 

Types of Intent Data

Intent signals are typically sourced from five key types of data:

1. Search Intent: Derived from keyword and query analysis.
2. Web Browsing Intent: Tracked through analytics and cookies.
3. Digital Interactions: Includes clicks, downloads, and other engagements.
4. Firmographics: Demographic data of businesses for targeted marketing.
5. Predictive Modeling: Using historical data to forecast future behaviors.

Let’s review each of these in detail — why they matter, the data collection methods, and the usage of user intent:

1. Search Intent (Keywords and queries):

According to ThinkwithGoogle, B2B prospects conduct an average of 12 searches before visiting a specific brand, underscoring the critical role of search intent in the buyer’s journey. This stat is important as it leaves a trail of the customer’s search and interactions before arriving on a web page.

Research indicates that 71% of prospective buyers begin their journey by searching online with general queries to find solutions or information. And by the time they land on a brand’s website, they have already completed about 57% of their decision-making process.

This ‘Search phase’ is crucial as it provides deep insights into where users stand in the buying cycle and their likelihood of making a purchase.

Let us explore specific Intent Signals/Triggers that should be tapped during the “Search phase.”

A. Informational Search Queries: These initial queries reflect early-stage interest, such as “how to improve SEO” or “benefits of organic marketing.”

Tracking methods:

• Google Analytics or Search Console: GA4 allows brands to monitor the keywords driving traffic to your site, and the search console helps you identify the search terms that bring users to the site and how the pages rank for those terms.
• SEO Tools: Popular platforms like SEMrush, Ahrefs, or Moz offers insights into the specific informational keywords that the target audience is using, along with information on keyword volume, difficulty, and competitive analysis.

Why they matter: These queries indicate users are in the research phase, seeking information rather than making immediate purchasing decisions. Marketers can leverage this insight to create targeted content like blogs and guides.

B. Navigational Search Queries: Users perform these searches when they have a specific website or page in mind, such as “LinkedIn login” or “LeadRebel blog.”

Image Reference: Monsterinsights 

Why they matter? Navigational queries suggest familiarity with a brand or its competitors, highlighting the importance of brand visibility and user experience.

Tracking methods:

• Google Analytics (Acquisition reports) or Google Search Console is used to identify the navigational search terms that land on the site. 
• Tools like Mention or Brand24 can detect mentions of a brand across the web and provide insights into navigational searches.

C. Internal Search Queries: These searches occur within a website, indicating specific user interests like “features” or “contact support.”

Why they matter: Internal search queries provide direct insights into user preferences and can reveal opportunities for content optimization and improved navigation. For example, if users repeatedly search for “pricing,” the pricing section/page can be more accessible or featured prominently.

Tracking methods:

• Google Analytics: Site search (View Settings) tracking enables to monitor what users are searching for on a website. 
• Tools and plugins like Swiftype or Algolia offer detailed analytics on internal search queries.

D. Transactional Search Queries: These queries demonstrate a clear intent to purchase or act and often use terms like “buy,” “best,” “discount,” or “compare,” or phrases like “best SEO tools” or “cheap web hosting.”

Image reference: SEMrush

Why they matter: High purchase intent signals that users are at the decision-making stage, making it crucial for brands to optimize landing pages and content with strong CTAs.

Tracking methods:

• Google Ads Keyword Planner: This tool can help identify transactional keywords with high intent, such as “buy,” “discount,” “best,” and “compare.”
• E-commerce Analytics: For e-commerce sites, Shopify Analytics or WooCommerce Analytics can track what users search for when looking to purchase.
• SEO Tools for PPC: SEMrush, Ahrefs, and similar platforms offer insights into high-intent keywords and the competitive landscape for PPC campaigns.

Brands can target these queries with optimized landing pages or posts, with strong calls-to-action (CTAs) to convert visitors into customers. For example, if a potential customer searches for “best email marketing software,” a landing page comparing the product favorably against its competitors can drive conversions.

Capturing intent signals through these four types of Search queries—informational, navigational, internal, and transactional—brands can create more personalized content that aligns with a customer’s search intention.

2. Web browsing Intent data (Cookies and tracking content)

In 2022, a global survey among marketers managing customer acquisition strategies revealed that 37% of brands rely exclusively on website-based first-party data for personalizing customer experiences, up from 31% in 2021. This underscores the growing importance of user-driven data in global business strategies.

Apart from first-party data, tracking page visits provides valuable insights into customer behavior through the use of cookies, which monitor user activity across sessions. 

However, it’s crucial to adhere strictly to data privacy regulations and obtain user consent before using this data for targeting purposes. 

Let’s break down the web tracking strategies with examples:

A. Content and on-site engagement:

Understanding what content users consume and how they interact with it reveals their intent. Marketers employ various methods to track this:

• On-site user tracking: Measures how users interact with different types of content (blogs, videos, product pages) on a website. Tools like Hotjar offer heatmaps and session recordings to see how users interact across webpages, help track users’ paths, and identify high-intent behaviors.

Metrics (KPIs) for on-site tracking include scroll speeds, link clicks, hotspots, number of downloads, and reviews.

Image reference: hotjar

• Tracking External content sites: This data provides insights into user behavior beyond a brand’s website, helping to understand user interests and intent across the web. Tools like LeadRebel and Bombora aggregate intent data from various B2B content sites to offer insights into user interests and behavior patterns.
• Tracking Social Media and Communities: Through social listening, organizations can track mentions and user engagements to gain insights into specific topics and discussions that resonate strongly with the target audience. Hootsuite and Sprout Social offer social media analytics and listening capabilities that help users understand their specific interests.

B. Tracking Browsing Behavior with Cookies:

Websites often use cookies to track user activity and preferences. There are two main types:

• First-party Cookies: Set by brands on their own websites, these track sessions, pages visited, and user journeys. This data helps in tasks like cart abandonment tracking and personalized recommendations. 
• Third-party Cookies: These are set by domains other than the brands to track user behavior across different sites.  

Image reference: cookieyes.com

The goal is to understand user’s interests and intent and target them with ads and products. Google Ads is a classic example of using third-party cookies to serve targeted recommendations.

Although effective for targeting, third-party cookies are subject to restrictions like Google Chrome’s phase-out plan by Q3 2024, emphasizing the shift towards first-party data and privacy-preserving technologies like Google’s Privacy Sandbox.

Integrating CRM systems with cookie data further improves personalization efforts and offers insights to build Customer personas or TAL profiles. A Salesforce or Hubspot CRM system integrated with browsing data can identify a lead who has repeatedly visited pricing pages and bump them up the lead scoring system to trigger a sales follow-up.

3. Intent from Digital interactions:

While ‘Web Browsing Intent’ provides a broad overview of user behavior and interests, ‘Digital Interactions Intent’ focuses on specific, deliberate engagements with content or features. 

For instance, a browser cookie can capture user page visits and content categories browsed, offering a generalised user intent. However, tracking digital interactions such as downloads, form submissions, button clicks, video plays, or other feature interactions provides granular, event-based data that signifies deeper user engagement or intent.

Image Reference: Tracking Digital Interactions – File downloads and Clicks

Consider this example: frequent visits to product category pages indicate interest but not immediate purchase intent. Conversely, downloading a product brochure or requesting a demo demonstrates high interest and potential buying readiness. 

Therefore, triggering an email campaign based on a user’s download of a specific eBook or completing a survey is highly effective compared to a content recommendation engine, which suggests articles on a “Technology” if a user frequently visits tech-related pages.

Tracking Digital Interaction signals:

• CRM Systems: Platforms like Salesforce, HubSpot, and Zoho track and manage customer interactions across various touchpoints. HubSpot CRM, for example, integrates seamlessly with email marketing, social media, and websites to provide a comprehensive view of user interactions and preferences.

• Marketing Automation Platforms: Marketo, ActiveCampaign, and Pardot automate marketing processes and track user interactions across various channels. Marketo, for example, allows marketers to automate email campaigns triggered by user actions. Actions, for instance, could be visiting a product page or downloading a resource.

Image Reference: ActiveCampaign

• Web Analytics Tools: Google Analytics and similar web page analytic tools can also provide detailed insights on clicks, page visits, and download events to help understand content performance and user engagement. 

Once interaction data and action signals are captured, Conversion Rate Optimization (CRO) tools can optimize user experiences and increase conversions through systematic testing and analysis of user interactions.

4. Firmographic Data in ABM

In an Account-Based Marketing (ABM) strategy, firmographic data enables marketers to focus on high-value prospects by analyzing specific company attributes. This data includes industry type, company size, annual revenue, number of employees, and geographical location.

Why Firmographic data matters? 

While intent signals (from Search, Browsing, and Actions) help marketers understand and predict user interests and engagement readiness, firmographic data allows for segmentation and targeting based on company demographics. It also assists in defining the ideal customer profile and identifying high-value targets for B2B sales.

For example, identifying a mid-sized tech company searching for “best CRM software” indicates potential buying intent, contrasting with a new-age startup that may rely more on free tools.

Tools for identifying Firmographic data:

• CRM systems: Salesforce, HubSpot, and Zoho centralize and manage firmographic data alongside customer interactions, storing various profiles and high-profile prospects.

• ABM Platforms: Specialized platforms like Demandbase, Terminus, and 6sense excel in identifying high-value target accounts and aggregating firmographic data for precise targeting.

Image Reference: Demandbase for Salesforce

• Data Enrichment Tools: Platforms like ZoomInfo, Clearbit, and Dun & Bradstreet add context and enrich existing CRM data with further firmographic information. For example, Clearbit is a popular tool used across marketing teams to enhance their lead and customer data with firmographic details and help build a clearer picture of target accounts.

• Sales Intelligence tools: Platforms such as LinkedIn Sales Navigator, InsideView, and DiscoverOrg offer detailed insights into companies and key contacts. These tools help identify decision-makers and influencers within target accounts, enabling more informed and personalized outreach efforts. 

For example, LinkedIn Sales Navigator can offer insights into a company’s hierarchy, recent activities, and key personnel, helping to create more informed and personalized outreach efforts.

• Business Information Services: Hoovers, Crunchbase, and PitchBook provide comprehensive company profiles and market intelligence, covering financials, leadership, industry positioning, acquisitions, funding rounds, and other significant business events.

Reference: Crunchbase from Techcrunch

In practice, leveraging firmographic data often involves combining CRM systems, ABM platforms, data enrichment tools, and sales intelligence services. These tools collectively facilitate the identification of target accounts, data enrichment, and personalized marketing and sales strategies tailored to resonate with high-potential prospects.

5. PM of historical intent-based Marketing data

PM (Predictive modelling) uses historical and real-time data to forecast future behaviors and decisions. By identifying patterns and trends within existing customer data, marketers can predict the actions of new prospects who demonstrate similar behaviors, enabling more efficient and effective targeting strategies.

Why does Predictive modelling matter? With historical data available, marketers can choose to engage potential customers before they even express clear intent through their actions.

However, predictive modelling requires high specificity and advanced tools to predict the behaviors of individuals when compared to other intent signals. Accuaracy of which can depend on understanding and reacting to the actual behaviors and company demographics. 

Tools for Predictive Modelling:

• Predictive Analytics Platforms: These platforms use high computational machine learning and AI models to analyze vast amounts of data and recognize user patterns to predict future customer behavior. Lattice Engines is an excellent example of a Predictive platform that helps build lookalike models to target new prospects resembling existing best customers.

• Customer Data Platforms (CDP): CDPs aggregate data from various sources onto a unified platform. Tools like Segment collect data from web, mobile, and in-store interactions to create lookalike models, targeting new online users like top spending customers.

• Machine Learning and AI Tools: These tools leverage AI to deliver predictive insights. Salesforce CRM Einstein, for instance, analyses past customer interactions to predict future behavior, automating lookalike modelling and suggesting the next best actions for sales and marketing teams.

• Marketing Automation Platforms with Predictive Features: These platforms employ predictive lead scoring to assess historical customer data and identify new leads likely to convert. Like AI models, they also use this data to create lookalike profiles, identifying new prospects who resemble your best customers.

Image reference: dnb.com, Customer Data Platform

In essence, predictive intent and lookalike modelling provide foresight into potential customer behaviors by analyzing patterns in past interactions and using them to anticipate future actions. This proactive approach focuses on identifying new prospects statistically likely to exhibit behaviors similar to those of your best customers.

Proven Benefits of Intent-based Marketing:

Intent Data Trends (2022) shows that 17% of B2B sales and marketing professionals have improved their lead conversion rates by 30% using intent data, reflecting a 33% year-over-year increase. Globally, over 90% of marketers have observed excellent results from intent-based marketing through data collection, including better prospect building, enhanced content creation, and more effective campaign integration. 

The following report from InboxInsight graphically presents how an intent-based marketing strategy can yield better outreach results:

Intent-based marketing excels in conversions and engagement by precisely targeting the right audience with the right message at the right time. However, an AI-driven cold strategy can also be effective for initial contact and relationship building. 

Therefore, synergising the two methods to leverage both strengths is a better approach. This comprehensive approach drives better ROI, KPIs, and Customer satisfaction while reducing the white-noise problem.