If you’re in B2B sales and tired of sifting through “hot leads” that go nowhere, you’re not alone. Lead scoring can help, but only if you set it up right. This guide is for sales and marketing folks ready to get practical with Lift-ai. No fluff, no magic bullets—just a step-by-step walkthrough to get lead scoring working for your team (and what to ignore along the way).
What Is Lead Scoring—And Why Bother?
Lead scoring is simple: you assign points to leads based on signals that actually matter. The goal is to show your sales team who’s worth their time.
But here’s the thing: most lead scoring models get too clever for their own good. If your scorecard has 15 signals nobody trusts, reps will ignore it. If it’s too basic, they’ll ignore it for a different reason. You want a model that’s simple, clear, and improves as you learn.
Lift-ai claims to do a lot of the heavy lifting for you. But it’s not magic. You’ll still need to think critically about what drives real sales at your company.
Before You Start: What You Actually Need
Don’t start by dumping in every data point you have. Here’s what you need to prep:
- Your best customer profiles. Who’s bought from you before? Why?
- A short list of “buying signals.” Think: demo requests, repeat website visits, job titles.
- Your CRM and marketing tools connected. Lift-ai can pull in data, but only if it’s there.
Pro tip: Ask a couple of your best sales reps what clues they look for in a good lead. This works better than any spreadsheet.
Step 1: Connect Your Data to Lift-ai
Before you can score anything, Lift-ai needs to see your leads. This isn’t always as smooth as the marketing site promises, so expect a few hiccups.
Key connections to make:
- CRM (Salesforce, HubSpot, etc.): This is non-negotiable. If your CRM’s a mess, clean it up first.
- Marketing automation (Marketo, Pardot, etc.): For things like email opens, web visits, and form fills.
- Website tracking: Lift-ai’s script or integration needs to be on your site to track visitor behavior.
What to ignore: Don’t get bogged down with fancy integrations you don’t use. Focus on the basics first, then layer in more data as you go.
Step 2: Define What a “Good Lead” Looks Like
This is where most teams get it wrong—they copy someone else’s template or let software pick for them. Resist the urge.
How to do it:
- List your top 5-10 recent customers. What do they have in common?
- Identify core qualities:
- Industry
- Company size
- Job title or function
- Key behaviors (visited pricing page, requested demo)
- Write down “dealbreakers.” For example, if you never sell to companies under 20 employees, make that clear.
Honest take: Don’t stress over being perfect—just be specific. The more concrete, the better.
Step 3: Set Up Your First Scoring Model in Lift-ai
Now you’re ready to build. Lift-ai offers both “out-of-the-box” models and custom scoring. Here’s how to avoid rookie mistakes:
A. Start Simple
- Pick 3-5 key signals. Example: Visited pricing page (+10), works at target company size (+5), requested demo (+15).
- Assign intuitive scores. More important actions = more points.
- Set a threshold. For example: 20 points = sales-ready.
B. Use Lift-ai’s Suggestions—But Don’t Blindly Trust Them
Lift-ai will suggest signals based on your data. These are sometimes helpful, sometimes laughable. Review them, but use your real-world knowledge.
C. Build, Save, and Test
- Use Lift-ai’s “preview” feature to see how past leads would have scored.
- Adjust weights if it’s flagging too many (or too few) leads.
Pro tip: If you’re stuck, default to activity-based signals. People who are actually doing things (not just opening emails) are better bets.
Step 4: Align With Your Sales Team (Seriously)
A lead scoring model that sales ignores is useless. Before you go live:
- Show examples to real reps. Ask if these “top leads” look right.
- Get their buy-in on thresholds. Too many “hot” leads means nobody trusts the list.
- Set up alerts or CRM views. Make it dead simple for reps to find scored leads.
What to ignore: Don’t automate outreach yet. Get manual feedback first. Tech can’t fix bad process.
Step 5: Launch—and Watch What Happens
Go live, but keep your eyes open. For the first couple of weeks:
- Track how many leads hit your threshold. If it’s everyone, your bar’s too low.
- Watch response rates. Are scored leads converting to meetings or sales?
- Collect honest feedback. Ask sales which leads felt right, and which didn’t.
Reality check: The first version will be wrong in some way. That’s normal.
Step 6: Tweak, Test, and Ignore the Hype
The only way to get this right is to keep adjusting. Here’s how to do it without driving yourself nuts:
- Review every 2-4 weeks. Are the signals you picked still working?
- Remove dead signals. If “downloaded whitepaper” never predicts a sale, drop it.
- Try one new experiment at a time. Don’t change everything at once.
Pro tip: Resist the urge to make your model fancy. More complexity usually means worse results, not better.
What Works, What Doesn’t, and What to Ignore
Works: - Activity-based signals (demos, pricing page, repeat visits) - Firmographics that actually matter (industry, size) - Regular, honest feedback from sales
Doesn’t work: - Vanity signals (opened newsletter, clicked random blog) - Overweighting job titles (titles are messy and inconsistent) - Copy-pasting someone else’s model
Ignore: - “AI magic” hype. Lift-ai’s AI is helpful, but it’s not psychic. - Over-promised integrations. Only hook up what you need.
Keep It Simple—Iterate Often
You don’t need a “perfect” lead score out of the gate. Focus on getting a basic, trustworthy model running in Lift-ai, and be ruthless about keeping it simple. The goal isn’t to impress your boss with a fancy dashboard—it’s to help your sales team spend more time with people who might actually buy.
Iterate, ask for feedback, and keep an eye on real sales outcomes. That’s how you’ll get real value—no matter what the software claims.