Traditional B2B personalization (adding names, basic segmentation) is dead. New research shows 73% of buyers avoid irrelevant outreach despite personalization efforts.
AI-powered relevance wins by analyzing real-time behavior patterns and buying signals instead of just demographics. It answers “what is this person doing right now?” rather than “who are they?”
Key shift: Focus on intent signals over engagement metrics. Three qualified prospects showing genuine buying intent beat 300 unqualified leads with high email open rates.
The result: Better marketing-sales alignment, shorter sales cycles, and more predictable revenue growth.
Bottom line: No more chasing vanity metrics like open rates. Start identifying prospects who are actually ready to buy.
Remember when adding someone’s first name to an email felt revolutionary? When segmenting your list by company size made you feel like a targeting genius?
Those days are over.
A recent MarTech article by Shama Hyder cuts straight to the problem: “Personalization without relevance is just expensive noise.” And the data backs this up—73% of B2B buyers actively avoid contacts sending irrelevant messages.
We’ve all been there. Your marketing team celebrates their 25% open rate while sales complains that “these leads are garbage.” Sound familiar?
The spray-and-pray problem
Here’s what most teams got wrong about personalization: they confused knowing someone’s job title with knowing when they’re ready to buy.
Traditional lead scoring treated all activities equally. Downloaded a white paper? 10 points. Attended a webinar? 15 points. Visited the pricing page? 20 points.
But as Hyder points out in the MarTech piece: “Just because someone downloaded your white paper doesn’t mean they’re ready to take a sales call.”
The sequence matters. The timing matters. The context matters.
What buyers actually want
The Gartner research Hyder cites tells the real story:
- 61% of B2B buyers prefer buying without engaging a sales rep
- 73% actively avoid irrelevant outreach
Translation: buyers don’t want more personalized spam. They want you to show up when they actually need you.
How AI changes the game
Instead of asking “Who is this person?” AI asks “What is this person doing right now?”
The difference is massive. AI can track hundreds of signals simultaneously—website behavior, content consumption patterns, search intent, hiring trends, technographic changes. When these signals align, you get genuine buying intent, not just engagement.
Hyder uses a great analogy: “You want to be in front of three people who are prepared to buy, not 300 who aren’t.”
The Spotify model for sales
The most interesting part of the MarTech article discusses “streaming intelligence”—treating prospect identification like Spotify treats music discovery.
Spotify doesn’t just know your favorite genre. It analyzes what you skip, how long you listen, what similar users discover. Then it creates playlists that evolve in real-time.
Sales intelligence platforms like Lusha work similarly, creating “prospect playlists” based on behavioral patterns and buying signals. Your pipeline becomes dynamic, not static.
Read the full MarTech article here
FAQs
AI-powered relevance uses machine learning to analyze behavioral patterns, intent signals, and contextual data to identify prospects showing genuine buying readiness, rather than just demographic fit.
Traditional personalization focuses on customizing messages based on demographics. AI relevance analyzes real-time behavior to determine when prospects are actually ready to buy.
AI systems track website behavior patterns, content consumption sequences, search intent signals, technographic changes, hiring patterns, and social media engagement to create comprehensive relevance scores.
According to Gartner research, 73% of B2B buyers actively avoid irrelevant messages because traditional personalization often lacks genuine relevance to their current needs and buying timeline.
Similar to Spotify’s music recommendations, streaming intelligence platforms like Lusha create dynamic “prospect playlists” that continuously update based on behavioral patterns and buying signals.
AI-powered relevance improves lead quality by focusing on genuine buying intent rather than engagement metrics, creating better alignment between marketing-generated leads and sales-ready prospects.
Start by identifying key buying signals for your ICP, implement AI tools that track multiple data points simultaneously, and create feedback loops between sales outcomes and marketing strategies.
Focus on relevance scores, buying readiness indicators, intent signal strength, and conversion rates from marketing qualified leads to sales qualified opportunities.
