In-Market Audiences: Buyer Intent Data to Reach Ready-to-Buy Prospects.

Buyer intent data is intelligence gathered from a prospect's digital activity: the search queries they run, the websites they visit, the content they download and the competitors they research. Today's online shoppers, whether B2C consumers or B2B decision-makers, leave a detailed trail of online shopping behavior before they ever contact a vendor. Multiply Revenue tracks different types of intent data across search engine activity, publisher networks, review platforms, trade publications and social media. We combine first-party intent data with third-party intent data sources to give you the most complete picture of who is actively shopping in your category right now. All data is collected ethically in compliance with applicable privacy laws, aggregated at scale and matched to verified contact records in our database.

We track three main types of intent data. First-party intent data comes from your own digital properties: website visits, form fills and content downloads from your own pages. Second-party intent data is shared directly between partners, for example a publisher sharing behavioral data with us. Third-party intent data comes from our broader monitoring network across thousands of publisher sites, review platforms and search activity trackers. We also track online purchase research activity across ecommerce marketplaces and comparison sites, capturing the full spectrum of online shopping behavior that signals commercial intent. All three types of intent signals are combined and scored to identify B2B companies showing intent across multiple intent topics simultaneously, which produces the highest-confidence buyer signals.

Our contact database maintains strong accuracy on verified phone numbers and email addresses, validated through continuous data hygiene processes including email verification, phone number validation and regular database cleansing. Signal accuracy, meaning whether the behavioral data correctly reflects genuine purchase research, is validated through our activity model which filters out noise and bot traffic. Clients typically see significantly higher call answer rates versus cold lists, which reflects both contact accuracy and the quality of intent signals we surface.

Intent signals are captured continuously across our monitoring network and processed batches are delivered to clients every 24 hours. Your morning delivery includes all new active prospect activity detected in the prior day. This daily refresh cycle is critical because using buyer intent data only works when the signals are current. Online consumers move fast. A homeowner researching roofing contractors today, or a business owner comparing software options on their mobile devices tonight, needs to be contacted within hours not weeks. Our fastest-responding clients, those who call within 4 hours of receiving the data, consistently report the highest conversion rates.

Yes and B2B sales outreach is one of the highest-ROI applications of buyer intent data. Because prospects are actively Googling your services and filling out forms right now, call connect rates are dramatically higher than cold lists and conversations are more productive since the prospect has context for why your service is relevant to them. Many clients pair our intent data tools with their own sales team while others combine it with our AI Sales Agent service to automate initial outreach and qualification at scale. Both approaches produce strong B2B sales results.

Three differences stand out. First, signal breadth: we monitor sources across eight distinct buying signal categories covering all types of intent data while most competitors rely on 2-3 signal types. Second, matching accuracy: our prospect matching connects intent signals to verified decision-maker contacts, not just company-level data. Third, daily delivery: we refresh and deliver every 24 hours while many providers operate on weekly or monthly cycles. When your intent data is 30 days old, it is functionally no different from a cold contact list. Intent data helps you most when it is fresh.

Getting started takes under 48 hours. You book a strategy call with our team where we discuss your target buyer profile, geographic focus and ideal industries. We configure your intent signal filters and CRM integration preferences, then run a 7-day test delivery so you can see the quality of the data before committing. After reviewing sample data and confirming the configuration is accurate for your use case, your full daily delivery begins. Most clients are making B2B sales calls from their first buyer intent data list within 72 hours of signing on.

Our buyer intent data collection and processing practices are designed to comply with GDPR, CCPA and other applicable privacy regulations. We collect behavioral data in aggregate from publisher and data partner networks where appropriate consent frameworks are in place. Contact records in our database are business contact information used for legitimate B2B marketing and sales communication, which falls under recognized lawful bases under GDPR. We do not sell consumer data for non-business purposes. For clients operating in regulated industries or serving EU-based prospects, we recommend consulting with your legal counsel regarding your specific use case.

In-market audiences in Google Ads are audience segments built from consumer behaviour signals collected across Google properties including Search, YouTube, Gmail and the Display Network. Google analyzes search queries, page visits, content engagement and previous purchases to identify users who are actively researching a product or service category right now. When you add an in-market audience segment to an ad group, Google targets or observes users whose recent digital behaviour places them in that buying window. You can apply in-market targeting in observation mode first to collect performance data without restricting reach, then switch to targeting mode once you have enough signal to confidently narrow your audience. In-market audiences work best when combined with strong creative and landing pages tailored to users who are ready to buy.

In-market audiences and affinity audiences represent two different stages of the buyer journey and two distinct targeting strategies in Google Ads. Affinity audiences are built from long-term interests and lifestyle patterns: someone who consistently reads fitness content is part of a 'Health and Fitness Enthusiasts' affinity segment regardless of whether they are currently shopping. In-market audience segments, by contrast, are based on recent consumer behaviour signals that indicate active purchase research. A user in the 'Home & Garden > Home Appliances' in-market segment is searching for appliances and comparing products right now. Affinity audiences are most effective for brand awareness campaigns targeting users early in the funnel while in-market audiences drive conversions by reaching users who are ready to buy. For full funnel coverage in digital advertising, run affinity audiences at the top and in-market audience campaigns at the bottom.

Custom intent audiences are a more precise form of audience targeting in Google Ads that let you define your own audience segment based on the exact keywords and URLs relevant to your business rather than relying on Google's predefined in-market categories. You build a custom intent audience by uploading a list of high-intent search terms your prospects use and competitor URLs they visit. Google then targets users who have recently engaged with that specific content. Standard in-market audience segments use Google's categorization which covers broad categories like 'Vehicles & Transportation' or 'Business Services.' Custom intent audiences let roofing contractors target users who searched 'roof replacement cost 2025' or visited three specific competitor sites. For businesses with niche offerings or in categories not well-covered by Google's predefined segments, custom intent audiences deliver significantly better performance than standard in-market targeting.

Audience signals are data inputs you provide to Google's Smart Bidding and Performance Max campaigns to guide the algorithm toward your best-converting users faster. Rather than waiting weeks for the algorithm to learn who converts, you supply audience signals upfront by uploading customer match lists, remarketing audiences, in-market segments and custom intent audiences. Google uses these signals as a starting point for audience segmentation and then expands from there based on real conversion data. Strong audience signals that match your actual customer demographics and consumer behaviour patterns dramatically reduce the learning period for new campaigns. Campaigns with high-quality audience signals typically hit their target CPA within the first two weeks while campaigns without them can take 6-8 weeks to optimize effectively. Feeding fresh in-market audience data into your audience signals is one of the highest-leverage optimizations available in modern Google Ads account management.

The most common mistake in in-market audience campaigns is applying targeting mode too early before gathering enough observation data. Start every new in-market segment in observation mode for at least 2-3 weeks so you can see conversion rates by audience without restricting your reach. The second mistake is using in-market audiences in isolation rather than layering them with demographics. An in-market segment for 'Real Estate' is broad; narrowing it with homeowner demographics and income filters transforms it into a precision targeting tool. Third, many advertisers skip adding in-market audiences to search campaigns entirely, focusing only on Display. In-market targeting on search campaigns allows you to adjust bids for users who are both searching your keywords and showing in-market signals, which compounds their purchase intent. Fourth, avoid treating all audience types within a segment equally: users who visited competitor pricing pages in the last 7 days are far more valuable than users who browsed a general category 30 days ago. Use custom intent audiences to capture the highest-intent subsegments rather than relying entirely on Google's broad in-market categories.

Market segmentation transforms in-market audience targeting from a broad reach strategy into a precision conversion tool. When you apply audience segmentation across your Google Ads account, you split your in-market audience into distinct groups based on demographics, geography, device and prior engagement. A solar company might segment their in-market audience into homeowners in high-sun states versus renters, or into users who visited their pricing page versus those who only viewed the homepage. Each segment then gets tailored ad creative, landing pages and bid adjustments matched to where that group sits in the buyer journey. The result is that users who are ready to buy see conversion-focused messaging while earlier-stage researchers see educational content that moves them down the funnel. Proper market segmentation applied to in-market audience campaigns typically reduces cost per conversion by 30-50% compared to running a single unsegmented in-market audience.

Demographics are the most powerful refinement layer you can apply to an in-market audience segment. On their own, Google's in-market categories group users by purchase intent across a broad category. Adding demographics such as age range, household income, parental status and homeownership status narrows that group to the subset whose demography actually matches your ideal buyer. A dental practice targeting new patients gets far better results by combining an in-market segment for dental services with demographics filtering for adults aged 25-55 in their local zip codes. Without demographic overlays, your ads reach anyone in the broad category regardless of whether their consumer behaviour and life situation make them a realistic buyer. Apply demographics as an observation layer first to measure performance across demographic groups before committing to targeting mode. Audience segmentation that combines intent signals with the right demography is consistently one of the highest-ROI optimizations available inside any Google Ads account.

Audience signals are the single most impactful input you control in a Performance Max campaign. The best audience signals to add are: your customer match list uploaded from your CRM (this gives Google real examples of who converts), remarketing audiences of past converters and high-value page visitors, in-market audience segments that match your buyer category and custom intent audiences built from your highest-converting search terms. These audience signals do not restrict who Google shows your ads to. Instead they give the algorithm a conversion-ready starting point so it can perform accurate audience segmentation faster. Campaigns with strong audience signals that reflect real customer demographics and consumer behaviour patterns typically reach target CPA within 2 weeks. Campaigns launched without audience signals can take 6-8 weeks to optimize because Google has no prior context about your ideal buyer. For most accounts, the single best audience signal to add is a customer match list paired with an in-market segment from Google's audience types library that most closely matches your buyers.

Real-time buyer signals are behavioral data points that indicate a company or individual is actively researching a purchase decision right now. In B2B sales, the most valuable real-time signals include: a company searching for your category of solution multiple times in a 30-day window, a decision-maker visiting your pricing page, a competitor's customer downloading comparison content and a contact whose company recently received funding or changed leadership. Real-time buying signals let your sales team prioritize outreach to accounts showing active purchase intent instead of calling a cold list. When integrated with your CRM workflow, buying signals in sales automatically trigger outreach sequences the moment a signal fires. Agentic AI systems can act on signals in seconds, placing a call or sending a message before the prospect reaches out to a competitor.

Using buying signals in sales starts with defining which signals matter for your specific product. High-intent signals include: competitor keyword searches, review site visits (G2, Capterra), pricing page views and content downloads about the problem your product solves. Mid-intent signals include: category keyword research, webinar attendance and social engagement on relevant topics. Once signals are mapped, your CRM workflow routes hot signals to immediate AI outreach and warm signals to nurture sequences. Real-time signals give your team a reason to call that makes the conversation feel timely rather than random. Instead of 'I am just checking in,' your rep says 'We noticed your team has been researching outbound automation. That is exactly what we help companies with.'

Bombora and 6sense are the two most recognized enterprise intent data providers in B2B. Bombora aggregates content consumption data from a co-op of B2B publisher sites to surface surge scores by topic and company. 6sense layers intent data with firmographic data and AI-driven account scoring to predict which accounts are in an active buying cycle. Both require significant technical setup and enterprise contracts. Multiply Revenue uses in-market audience data combined with real-time signal feeds to target and contact prospects automatically. Rather than delivering a data file your team has to action manually, our platform acts on the intent data directly by calling the right contacts at the right companies the moment buying signals fire. For small and mid-size businesses, this done-for-you model delivers better outcomes than a raw data subscription.

Online shopping data refers to behavioral and transactional data collected from people who shop online. It includes purchase history, category browsing patterns, cart abandonment signals, retailer preferences and seasonal purchasing behavior. Marketers use online shopping data to build audience segments based on what types of products a consumer has bought or is actively researching. For example, a home improvement retailer can target consumers who recently browsed flooring products across multiple sites. During peak periods like Black Friday and Cyber Monday, online shopping data lets advertisers adjust bids and messaging based on real-time purchase intent rather than demographic assumptions. In the United States alone, e-commerce now accounts for more than 15% of total retail sales, making online shopper data one of the most valuable targeting inputs available.

Online shoppers in the United States span every age group, but Millennials and Gen Z represent the highest purchase frequency per year according to multiple retail studies. Key data points that define online shoppers as a target audience include: device type (mobile vs desktop), category affinity (electronics, apparel, home goods), purchase frequency, average order value and retailer preference (Amazon vs direct-to-brand vs brick and mortar). For advertisers, the most actionable online shoppers data is recency combined with category intent. A consumer who bought on Amazon in a specific category last month and is now browsing competitor products is a higher-value target than someone who bought once two years ago. Multiply Revenue's in-market audience data surfaces these high-recency, high-intent segments for outreach.

A real-time signal workflow is an automated sequence that triggers outbound sales actions the moment a buying signal fires. The workflow typically runs like this: intent data feed detects a target company researching your category, the signal is matched against your CRM to check if you have a contact at that company, if yes the workflow triggers an immediate AI call or email, if no the workflow adds the company to a prospecting queue for enrichment and outreach. The agentic AI component handles the execution automatically so your team does not need to manually review signal feeds every hour. The result is a sales motion that responds to buyer intent in minutes rather than days, which is the window where conversion rates are highest.

Companies that shop online for B2B solutions leave clear intent trails. They search review sites like G2 and Capterra, download comparison guides, visit multiple vendor websites in a short window and read pricing pages more than once. Intent data aggregates these signals across the web to identify which companies are in an active buying cycle for your category. When combined with firmographic data (company size, industry, location, revenue) intent data lets you filter for the exact types of companies that match your ideal customer profile and are actively evaluating solutions right now. This is more precise than any demographic audience because it reflects actual behavior rather than assumed interest.

The types of buying signals worth prioritizing fall into three tiers. High-intent signals: competitor pricing page visits, review site comparisons (G2, Capterra), demo requests that did not convert and repeat category keyword searches within a 14-day window. These signals to prioritize leads indicate an active evaluation is underway and the window for outreach is days, not weeks. Mid-intent signals: content downloads on category topics, webinar attendance and LinkedIn engagement with solution-provider content. Low-intent signals: a single blog post view or a LinkedIn connection request. Real-time signals in sales create urgency. A prospect who visited your pricing page 20 minutes ago is 10 times more likely to take a call than one you reach from a cold list. Prioritizing real-time buying signals in sales over cold outreach is the single biggest lever for improving outbound conversion rates.

Bombora and 6sense are the two dominant enterprise intent data providers. Both deliver excellent data but require technical setup, dedicated ops resources and enterprise contracts that start at $30,000 to $100,000 per year. For small and mid-size businesses, the cost and complexity create a data advantage gap where only enterprise companies can act on intent signals. Multiply Revenue closes that gap by combining in-market audience data with done-for-you outreach. Instead of delivering a data feed your team has to action manually, our platform uses intent signals to trigger AI calls to the right contacts automatically. You get the targeting precision of enterprise intent data without the enterprise setup cost or the need to hire a RevOps team to manage the pipeline. For businesses spending under $50,000 per year on sales and marketing, this model delivers better outcomes than a raw intent data subscription.

To use intent data effectively, map signal types to outreach timing. When a target account triggers a high-intent signal (pricing page view, competitor comparison search), reach out within 24 hours. Research shows response rates drop by 80 percent after the first 24-hour window. When an account triggers a mid-intent signal (category content download), add them to a 5-day nurture sequence that starts with a value-first touchpoint. When an account triggers a low-intent signal, add them to a longer 30-day educational sequence. The intent data tells you the what. The timing tells you the when. Combining both with AI outreach that acts on signals in real time removes the human delay that kills most intent-based outreach programs. Most teams that buy intent data see poor ROI because they review signals weekly rather than acting within hours.

The most valuable B2B online shoppers data points for targeting are: recency of purchase or research activity (someone who bought in your category 30 days ago is more receptive than someone who bought 18 months ago), category specificity (searching for your exact solution versus a broad category), device behavior (mobile research often precedes a purchase decision made on desktop) and channel behavior (prospects who research on review sites like G2 convert at higher rates than those who only use Google). In the United States, B2B purchase research increasingly happens on mobile during evening hours as buyers research solutions outside of business hours. Combining online shopping data with firmographic filters (company size, industry, revenue) lets you focus on the accounts that are both in-market and fit your ideal customer profile simultaneously.