So, you’re using Pathfactory to get more eyes (and clicks) on your content, but the “recommended for you” boxes aren’t exactly lighting up your analytics. Maybe you’ve set up some tracks, but engagement is flat. Or you’ve been chasing AI-powered magic that never quite delivers. Truth is, content recommendation engines aren’t plug-and-play miracles—they need real tuning to work for real people.
This guide is for marketers, content ops folks, and anyone tasked with getting more value from Pathfactory. We’ll cut through the hype and show you what actually moves the needle.
Step 1: Get Real About What Pathfactory’s Recommendation Engine Can (and Can’t) Do
First things first: Pathfactory is solid at organizing and serving up your content, especially for B2B. But its recommendation engine isn’t Netflix-level.
Here’s what it does well: - Tracks what users view and for how long (aka “content consumption”) - Serves up related assets based on rules: most viewed, recent, “next in sequence,” or sometimes basic topic matching - Lets you tweak logic—but you still need to feed it structure and good content
But here’s what it doesn’t do: - Deep personalization for every visitor (unless you’ve really invested in segmentation and tagging) - AI that magically understands your content’s meaning or intent - Fix bad content or bad journeys
Pro tip: If you’re expecting the engine to do all the work, you’ll be disappointed. Pathfactory’s recommendations are only as good as the content structure and data you give it.
Step 2: Clean Up Your Content Library and Metadata
Garbage in, garbage out. If your library is a mess, recommendations will be too.
What actually matters: - Accurate titles and descriptions: If your content is full of “Q3 Report_v3_FINAL” nonsense, fix that. - Consistent tagging: Use clear topics, personas, funnel stages, and content types. Don’t go overboard—pick tags that matter. - Up-to-date assets: Kill or archive anything outdated. Nothing tanks trust faster than recommending a three-year-old webinar.
How to do it: - Do a content audit. A spreadsheet works just fine. - Prune anything that’s stale, duplicate, or off-brand. - Standardize your tags. If you have “product demo” and “demo video” as separate tags, merge them.
What to skip: - Don’t bother tagging every tiny detail. Focus on what users actually care about (industry, pain point, role, etc.).
Step 3: Set Up Tracks and Content Pools with Intent
Pathfactory’s “tracks” and “content pools” are how you group content for recommendations.
What works: - Funnel-based tracks: Awareness, consideration, decision—keep content grouped by buyer stage. - Persona or vertical tracks: If you sell to pharma and finance, don’t mix their content. - Topical pools: For high-volume topics, group content so recommendations are relevant.
What to watch out for: - Don’t dump everything into “general” pools. The more generic, the less relevant. - Avoid overlapping tracks unless you know exactly why (and are okay with some repetition).
Pro tip: Start simple. You can always add more tracks, but it’s a pain to untangle a spaghetti mess later.
Step 4: Tune the Recommendation Logic (and Don’t Trust Defaults)
Pathfactory lets you set rules for how recommendations work—don’t just accept whatever’s pre-configured.
Options usually include: - Most viewed: Good for social proof, but can quickly become self-reinforcing (the same stuff always wins). - Recently added: Useful for surfacing new content, but watch for old stuff getting buried. - Manual sequence: For journeys where you want control—think nurture tracks. - Tag/topic matching: Only as good as your metadata.
What works: - For short journeys, manual sequencing keeps things tight. - For large libraries, “related by tag” is decent—if your tags make sense. - For evergreen content, “most viewed” is fine, but review it quarterly.
What doesn’t: - Letting AI or “smart” logic run wild. Pathfactory’s automation is basic compared to, say, Amazon. Check what’s being recommended. - Mixing logic types in the same track. Pick one method per track unless you have a clear reason.
How to test: - Preview tracks as a user—do the recommendations make sense, or do you see junk? - Ask a few teammates (preferably not the people who built it) to click through and give real feedback.
Step 5: Personalize Where It Matters (and Where You Can Measure)
Personalization sounds good, but most B2B orgs overcomplicate it and end up with little to show.
Where personalization works: - Account-level: If you have ABM motion, set up tracks or pools for your top accounts. Use industry-specific content. - Role or persona: If you know the visitor’s job title, recommend based on their pain points—not just content type. - Behavioral: Recommend “next best” content based on what they’ve already viewed in the session.
Where it’s not worth the trouble: - Trying to personalize for everyone, especially anonymous visitors. - Building dozens of micro-tracks for every possible scenario. You’ll burn out and the data won’t support it.
Pro tip: If you can’t measure the lift (more time on page, more assets consumed, higher form fills), skip it. Personalization for its own sake is busywork.
Step 6: Measure Engagement Honestly (and Ignore Vanity Metrics)
Pathfactory serves up a lot of metrics—some useful, some just noise.
What to track: - Content consumption time: Are people actually reading/watching, or just bouncing? - Assets per session: More is better, but only if the content is actually relevant. - Down-funnel actions: Form fills, demo requests, etc.
What to ignore: - “Impressions” or “clicks” on recommended content that don’t result in engagement. - Heatmaps or scroll depth—interesting, but rarely actionable in B2B.
How to use the data: - Identify tracks or assets with high drop-off. Fix or remove them. - Double down on content that leads to conversions, not just views.
Pro tip: Share real numbers with your team—what’s working, what isn’t. Don’t hide behind “engagement rates” if the pipeline isn’t moving.
Step 7: Iterate, Test, and Don’t Fall for Shiny New Features
Recommendation engines always need tuning. What worked last quarter might flop now.
How to keep improving: - Review top and bottom performers monthly. - Try one change at a time—new tag logic, different asset order, updated content pools. - Watch for new Pathfactory features, but don’t assume every new AI thing will help. Test it yourself.
What to skip: - Overhauling everything every time someone gets a new idea. Small, measured tweaks beat big resets. - Chasing the latest “personalization” trick if you can’t prove it works for your audience.
The Bottom Line: Keep It Simple, Measure What Matters, and Don’t Overthink It
Pathfactory’s recommendation engine can absolutely boost engagement—if you feed it good content, use sensible tracks, and keep a skeptical eye on what’s actually working. Don’t get lost in the weeds of over-personalization or fancy AI claims. The basics—clean metadata, smart grouping, and honest measurement—do most of the heavy lifting.
When in doubt, simplify. Cut what doesn’t work, build on what does, and keep iterating. That’s how you turn content recommendations from “meh” to meaningful.