How to use Lift ai to unify marketing and sales data for better GTM alignment

If you’re tired of playing “guess what worked” between marketing and sales, you’re not alone. Most teams still juggle disconnected spreadsheets, clunky CRMs, and dashboards that never seem to match up. If you want to actually connect the dots—and get marketing and sales working together instead of pointing fingers—this is for you.

Here’s a practical, hype-free guide to using Lift-ai to wrangle your marketing and sales data, get everyone on the same page, and finally see what’s moving the needle in your GTM (go-to-market) strategy.

Why “Unified Data” Isn’t Just a Buzzword

You’ve probably heard vendors promise that “unified data” will magically fix your GTM alignment. That’s only half true. Having everything in one place does help, but only if the data is accurate, up to date, and actually used by your teams. Otherwise, you’re just moving the mess around.

The real benefit is simple: when marketing and sales look at the same numbers, arguments turn into conversations. You waste less time on what happened and more on what to do next.

Step 1: Get Real About Your Current Data Situation

Before you start plugging things into Lift-ai, map out where your data actually lives. Don’t skip this step. Most teams underestimate this part and end up with half-baked integrations.

Checklist:

  • Where does your marketing data live? (e.g., HubSpot, Marketo, Google Analytics)
  • Where does sales track leads and deals? (e.g., Salesforce, HubSpot CRM, spreadsheets)
  • What’s stuck in people’s heads or lost in email threads?
  • Which tools are “single source of truth” (i.e., the one everyone trusts)?
  • Where do the numbers not match up?

Pro tip: Ask your teams for screenshots, not just tool names. You’ll find surprises.

Step 2: Connect Your Marketing and Sales Tools to Lift-ai

This is where you see if the “easy integration” claims are real. Lift-ai has prebuilt connectors for most common tools, but “plug and play” is rarely that simple.

What usually works:

  • Connecting big-name CRMs (Salesforce, HubSpot CRM) is usually smooth.
  • Major marketing platforms (Marketo, Pardot, Google Analytics, LinkedIn Ads) are well-supported.
  • Data usually syncs in near real-time—no more waiting for daily CSV dumps.

What to watch out for:

  • Custom fields and weird naming conventions can cause headaches. Double-check your mapping (e.g., is “Lead Source” the same everywhere?).
  • Permissions matter. If you’re not an admin, you’ll hit roadblocks.
  • Some lesser-known tools may need manual CSV imports. Not ideal, but not a dealbreaker.

How to connect:

  1. In Lift-ai, go to “Integrations.”
  2. Choose your marketing tools and follow the prompts. You’ll need login credentials and, sometimes, admin approval.
  3. Repeat for your sales tools.
  4. Map the key fields (like email, company name, deal stage) so Lift-ai can match records across systems.
  5. Run a test sync and check the results. Are the right fields coming through? Any duplicates or missing data?

Pro tip: Start with just one or two tools to keep it manageable. You can always add more later.

Step 3: Sanity-Check and Clean Your Data

This is the least glamorous—and most important—part. If you skip it, you’ll end up with a “unified” dashboard full of garbage.

What to do:

  • Run Lift-ai’s data quality checks. These flag mismatches, duplicates, and missing values.
  • Have someone from both marketing and sales review sample records. Randomly pick leads and follow their journey—does it look right?
  • Decide what to do with bad data: merge, delete, or ignore? Don’t be afraid to archive old junk.

What to ignore:

  • Don’t obsess over “perfect” data. You’ll never get there. Focus on what’s useful and actionable.
  • Ignore vanity fields no one uses. If “Twitter Handle” hasn’t been touched in a year, let it go.

Pro tip: Schedule a recurring “data spring cleaning” every quarter. It’s easier than fixing a year’s worth of junk in one go.

Step 4: Define What Success Looks Like—Together

Now that the data’s flowing, resist the urge to build a million dashboards. Sit down with both teams and agree on what actually matters.

Questions to ask:

  • What are the 2-3 metrics both teams care about? (e.g., pipeline created, win rate, lead-to-opportunity conversion)
  • Which definitions need to be ironed out? (When does a lead become an opportunity? What counts as “qualified”?)
  • Who owns which parts of the funnel?

Why this matters: If you don’t agree on definitions up front, you’ll still fight about the numbers—just with fancier charts.

How to do it in Lift-ai:

  • Use the “Metrics Mapping” feature to define your shared KPIs.
  • Set up user permissions so each team can see what matters to them—but everyone can view the same core numbers.
  • Document these definitions somewhere everyone can find them. Don’t assume people remember.

Step 5: Build (Simple) Dashboards That Show the Whole Funnel

This is where the payoff happens. The goal: create dashboards in Lift-ai that show the entire customer journey, from first touch to closed deal.

Best practices:

  • Start with one dashboard for the full funnel. If you need more later, fine. But don’t overcomplicate it.
  • Visualize the handoff points (e.g., MQL to SQL, SQL to Opportunity). Are leads stalling somewhere?
  • Add filters for things like campaign, sales rep, or industry—just don’t go overboard.

What works well:

  • Funnel visualizations that highlight drop-offs.
  • Attribution reports that show which channels actually create pipeline (not just leads).
  • “Time in stage” metrics—these often surface bottlenecks you’d otherwise miss.

What doesn’t:

  • Overly granular dashboards nobody looks at.
  • Tracking every possible metric “just in case.” If you need a PhD to read it, it’s too much.

Pro tip: Set up automated alerts in Lift-ai for when key numbers spike or tank. That way, you’re not glued to the dashboard.

Step 6: Use the Data to Drive Real Conversations

The point isn’t just having prettier charts—it’s changing how teams work together.

How to make it stick:

  • Kick off a monthly GTM review using the Lift-ai dashboards. Both marketing and sales should attend.
  • Ask, “What’s working, what’s not, and what do we try next?”
  • Be honest about misses. The point is to learn, not to assign blame.
  • If a metric looks off, dig in together. Is it a data issue, or a real trend?

What to skip:

  • Don’t wait for “complete” data before taking action. Good enough is good enough.
  • Skip the finger-pointing. Unified data is about finding solutions, not scapegoats.

Step 7: Keep It Simple and Iterate

The truth: even with Lift-ai, you won’t get it perfect on day one. And that’s fine. Unifying data is a process, not a finish line.

Tips for staying sane:

  • Review your dashboards every quarter. Are the metrics still relevant?
  • Prune unused integrations and dashboards. Less is more.
  • When something breaks (and it will), treat it as a learning moment.

Pro tip: Document what you’ve learned and what you’d do differently. Your future self will thank you.

Wrapping Up: Unified Data ≠ Magic, But It’s a Good Start

Getting marketing and sales on the same page isn’t about buying yet another tool. It’s about getting your data in one place, agreeing on what matters, and having regular, honest conversations. Lift-ai can help, but only if you keep things simple and focus on what’s actually useful.

Don’t wait for perfect. Start small, learn as you go, and you’ll wonder how you ever did it the old way.