If you’ve got a solid customer base but stale upsell numbers, you’re not alone. Spotting which customers might actually want (or need) more from you is tough—lots of signals, lots of noise. This guide is for folks on sales, customer success, or revenue teams who want a practical way to use AI (specifically Lift-ai) to find upsell opportunities that are real, not just wishful thinking out of a dashboard.
No magic. No promises of “10x pipeline.” Just a clear-headed look at what works, what doesn’t, and how to get started.
Why Bother With Upsell AI?
Let’s get this out of the way: most companies leave money on the table with their existing customers. But blindly spamming “upgrade now!” emails isn’t the answer. You want to spot the customers who:
- Actually use your product a lot (and in ways that suggest they’re ready for more)
- Have needs that line up with your higher tiers or add-ons
- Show signs of engagement, not just “logins per month”
AI, when it’s done right, can help you sift real signals from the noise. But don’t expect miracles. It’s a tool, not a crystal ball.
Step 1: Get Your Data House in Order
Before you even touch Lift ai, check your data. AI can’t find patterns in garbage. Here’s what you need:
- Usage data: How are customers using your product? Think features, frequency, depth—not just “did they log in?”
- Account info: What plan are they on? Company size? Industry?
- Interaction history: Support tickets, chat logs, previous upsell attempts.
- Revenue data: What are they paying now? Any past upgrades or downgrades?
Pro tip: If you have data split across 3-4 different tools (CRM, product analytics, billing), get it in one place first. A basic spreadsheet beats a half-working integration.
What to ignore: Vanity metrics—like “number of emails opened”—aren’t usually helpful for upsell. Focus on behavior, not just clicks.
Step 2: Set Up Lift ai (Without the Hype)
Lift-ai pitches itself as an AI-powered tool for finding revenue opportunities within your customer base. In plain English, it tries to predict which customers are ripe for upsell based on their behavior and profile.
Here’s what actually matters:
- Connect your data sources. Lift ai works best if you hook it up to your CRM (like Salesforce or HubSpot), product analytics, and billing platform. Don’t worry about every integration—start with the basics.
- Map your fields. AI can’t guess what your “Plan_Tier” column means. Make sure your fields are clearly mapped so Lift ai knows what’s what.
- Test with a small segment. Don’t blast your whole customer list. Start with one segment (e.g., customers on your middle-tier plan, active in the last 90 days).
What’s overrated: Fancy “AI-driven” scoring models that aren’t transparent. If you can’t see why Lift ai suggests a customer, you’ll struggle to trust it—or explain it to your team.
Step 3: Review and Tune the Signals
AI tools like Lift ai look at patterns—how your best upsell customers behaved before they upgraded, and who matches that pattern now. But the tool doesn’t know your business like you do.
Look for:
- Heavy usage of features that are gated behind higher tiers
- Signs of growing teams (more seats, more activity)
- Repeated “close calls” with usage limits (e.g., hitting storage or API caps)
- Support requests asking for features only in premium plans
Ignore:
- Customers who are just active, but not pushing limits
- Accounts with lots of logins but no real feature use
- “High engagement” that’s really just troubleshooting problems
Pro tip: Sit down with a customer success rep and sanity-check the top 10 Lift ai picks. Does the list pass the “makes sense” gut test?
Step 4: Build a Focused Outreach List
Now you’ve got a shortlist of customers Lift ai thinks are upsell-ready—but don’t just blast them all with the same pitch.
Break them down:
- Ready now: Customers showing clear intent (e.g., they’ve asked for features, hit usage caps)
- Warming up: Active, growing, but not quite at the tipping point
- Not now: The AI flagged them, but your human review says “meh”
Focus on the “ready now” group first. For “warming up,” maybe put them in a nurture sequence or keep an eye on them.
What doesn’t work: Generic upgrade emails. Personalize your outreach based on what the AI surfaced—mention the exact feature or limit they’re running into.
Step 5: Test, Measure, Repeat
This is where most teams get lazy. Don’t just trust the AI—track what happens after you reach out.
- Did the customer respond?
- Did they actually upgrade?
- If not, did you learn anything new about their needs?
Feed this info back into Lift ai (or at least your own notes). Over time, your signals—and the AI’s picks—should get better.
Pro tip: Set a recurring calendar reminder to review your upsell “wins” and “misses.” Patterns will emerge, and you’ll get a lot savvier about which AI signals are worth chasing.
Where AI Helps (And Where It Doesn’t)
Let’s be honest—AI won’t magically fix a broken upsell process. Here’s where it helps:
- Surfacing non-obvious opportunities that humans miss (especially in big customer bases)
- Prioritizing outreach for stretched teams
- Spotting usage patterns you might not think to look for
But it won’t:
- Replace talking to customers
- Fix bad product-fit or pricing
- Write convincing, personal emails for you
If you expect AI to do all the work, you’ll be disappointed.
Common Pitfalls (and How to Dodge Them)
- Trusting the AI blindly: Always double-check with a human. AI is wrong more often than you think.
- Poor data quality: Garbage in, garbage out. If your usage data is patchy, fix that first.
- Over-automation: Don’t set and forget. Upselling is still a conversation, not just a workflow.
- Ignoring feedback: If customers don’t bite, find out why and adjust your signals or approach.
Wrapping Up: Keep It Simple, Keep It Human
AI like Lift ai can help you spot upsell opportunities that actually make sense—but only if your data is solid and you use some common sense. Start small, test often, and don’t get distracted by fancy dashboards. The best upsell teams pair automation with old-fashioned curiosity about what customers really need.
Iterate, learn, and don’t overcomplicate it.