How to export and analyze sales pipeline data in Wume for actionable insights

If you’re responsible for sales ops, pipeline reviews, or just want to know where your deals stand, you need answers—not dashboards that hide the real story. This guide is for anyone using Wume who’s tired of fuzzy metrics and wants to turn raw sales data into something you can actually use to make decisions.

I’ll walk you through exporting your pipeline data, cleaning it up, and squeezing some honest-to-goodness insights out of it. No fluff, no jargon—just practical steps and a few things to watch out for.

1. Why Export Sales Pipeline Data (Instead of Relying on Dashboards)?

Let’s get this out of the way: dashboards in most CRMs (Wume included) are fine for a quick glance, but they’re almost always missing context or flexibility. If you want to:

  • Slice and dice data your way (not just how Wume thinks you should)
  • Spot trends, bottlenecks, or sandbagging
  • Build custom reports for your team or execs

…then you’ll need to get your hands on the raw data. Exporting lets you skip the “guess what this chart means” game and focus on what’s really happening in your pipeline.

Pro tip: Don’t export everything “just in case.” Decide what you want to figure out first (e.g., why deals stall, which reps are lagging, how long deals are stuck in each stage).

2. Step-by-Step: Exporting Your Pipeline Data from Wume

Wume claims exporting is easy. In reality, it’s easy if you know what you’re looking for and avoid exporting a 10,000-row monster file you’ll never open again.

Step 1: Head to the Right Module

  • Log into Wume.
  • Go to the “Deals” or “Pipeline” module (name may vary depending on your setup).
  • Use filters to narrow down to the deals or timeframes you want. The more you filter now, the less cleanup later.

Step 2: Choose Your Export Options

  • Look for the “Export” button—usually tucked away near the top right.
  • Pick CSV or Excel. CSV is universal, but Excel is friendlier if you want to pivot.
  • Select which fields you actually need. At a minimum: deal name, owner, stage, amount, close date, created date, status. Ignore vanity fields you don’t care about (“last viewed by CEO,” etc.).

What not to do: Don’t bother exporting dozens of custom fields unless you’re sure you’ll use them. More columns = more headaches.

Step 3: Download and Save with a Clear Name

  • Give your file a name that makes sense (e.g., wume-pipeline-Q2-2024.csv). You’ll thank yourself in three months.
  • Store it somewhere you or your team can find it again.

3. Cleaning Up Your Export: What to Fix (and What to Ignore)

Raw CRM exports are messy. Before you analyze anything, you’ll want to clean up:

  • Delete empty columns. If a column is blank for every row, get rid of it.
  • Check for duplicates. Wume usually does okay here, but sometimes imports or API glitches create double entries.
  • Standardize dates. Make sure all date columns are in a format your spreadsheet tool understands.
  • Fix “picklist” weirdness. Sometimes stages or statuses export as internal codes (STG_2_IN_PROGRESS instead of “Negotiation”). If so, run a quick find-and-replace.
  • Amount in the wrong currency or format? Convert now, not later.

Ignore: Typos in deal names, unless they’re causing real confusion. Don’t get bogged down fixing what doesn’t matter.

4. Analyzing Your Pipeline Data: Finding the Story

Here’s where most people get overwhelmed. Don’t. You’re looking for patterns, bottlenecks, and things you can actually act on.

Step 1: Build a Simple Summary Table

Start with the basics. In Excel or Google Sheets, use a pivot table to group deals by:

  • Stage (to see where deals pile up)
  • Owner (to spot who’s overloaded or falling behind)
  • Expected close date (to see how many deals are supposed to close soon, and how realistic that is)

Pro tip: Don’t overcomplicate your first table. You want a bird’s-eye view, not a dissertation.

Step 2: Calculate Your Conversion Rates

  • What % of deals move from one stage to the next?
  • Where do deals most commonly die?

Set up formulas to track stage-to-stage conversion (e.g., number of deals moving from “Qualified” to “Proposal Sent” divided by total deals in “Qualified”).

Watch out: If your data has a lot of “stuck” deals (e.g., in one stage for 90+ days), include a column to flag those.

Step 3: Find Your Bottlenecks

Look for stages with lots of deals and low conversion to the next stage. That’s usually where things get stuck.

Ask:

  • Are certain reps or teams consistently slow at a specific stage?
  • Are deals of certain sizes or industries more likely to stall?
  • Is a particular month always worse? (Seasonality is real.)

Step 4: Track Sales Velocity (How Fast Deals Move)

  • Calculate average days in each stage.
  • Spot deals that are “rotting”—the ones that have been open way longer than your usual sales cycle.

Honest take: Don’t expect perfect data here. No CRM is airtight, and salespeople fudge dates sometimes. Look for trends, not gospel truth.

Step 5: Segment by What Matters (Ignore the Rest)

You can slice by product type, lead source, deal size, etc.—but only if it’s actually useful. If your team only sells one thing or has a simple funnel, don’t overthink it.

5. Turning Analysis Into Actionable Insights

Data is useless unless you do something with it. Here’s how to turn your findings into next steps:

  • If deals are stalling in one stage: Talk to the reps. Ask what’s actually happening—tooling issue, pricing, approvals, or something else?
  • If a few reps are overloaded: Rebalance assignments or add support.
  • If conversion rates are dropping: Dig in—are lead sources changing, is competition heating up, or are your qualification criteria too loose?
  • If the same deals keep getting pushed: Call out “hopium” in your forecast meetings. Either move them forward or clear them out.

Don’t: Create a 20-slide deck for the exec meeting. Three bullet points and a plan beat pretty charts every time.

6. What to Ignore (Unless You Love Busywork)

Some things just aren’t worth obsessing over:

  • Hyper-precise forecasting. Everyone wants it, nobody gets it. Focus on ranges and likelihoods, not exact numbers.
  • “Activity” metrics (calls, emails). They’re easy to measure but don’t always mean much. Focus on outcomes, not busyness.
  • Complex attribution models. Unless you have a big marketing team, don’t waste time arguing about which ad “caused” the deal.

7. Pro Tips for Saving Time Next Quarter

  • Build a template. Once you’ve made a useful pivot table or chart, save it so you’re not starting from scratch next time.
  • Automate exports if you can. Wume supports some basic scheduled exports—set it and forget it.
  • Keep a “data dictionary.” Jot down what each column actually means. Future you (or your replacement) will be grateful.
  • Share findings, not files. Nobody wants another CSV in their inbox. Summarize insights in a quick note or chat.

Keep It Simple—Then Iterate

Don’t get paralyzed trying to build the “perfect” pipeline analysis. Start simple: export, clean, summarize, and act on what you see. Each quarter, tweak your process based on what worked (and what was a pain). The best insights come from looking at the real world, not from chasing the perfect chart.

Want to get more out of your sales data? Start with the basics, stay skeptical of flashy metrics, and focus on what you can actually change. That’s how you turn pipeline numbers into real results.