If you’re here, you probably know that “lead scoring” is one of those phrases people throw around more than they actually use well. You’ve got a pile of leads, and you want to figure out which ones are actually worth your sales team’s time. Madkudu promises to help with that, but—like any tool—the magic is in how you set it up. This is for marketers, ops folks, or anyone who needs to build a customer fit model that does more than look pretty in a dashboard.
Here’s how to build a customer fit model in Madkudu without getting lost in the weeds, wasting cycles, or buying into hype. I’ll walk through the process step by step, flag what’s worth your time, and point out the stuff you probably don’t need to stress about.
Step 1: Understand What a “Customer Fit Model” Is (and Isn’t)
First, some grounding. A customer fit model is just a way to predict how likely a lead is to become a good customer, based on what you already know about your best customers. It’s not a magic box. It’s not going to solve bad marketing or broken sales processes. It’s a tool for prioritizing—nothing more.
What it can help with: - Routing the right leads to sales - Focusing marketing on high-potential segments - Filtering out obvious tire-kickers
What it won’t do: - Tell you exactly who will buy - Fix your product-market fit - Make up for missing or bad data
Set your expectations accordingly.
Step 2: Get Your Data House in Order
Madkudu is only as good as the data you feed it. Garbage in, garbage out. Before you even touch the platform:
Checklist: - CRM data: Make sure your CRM (Salesforce, HubSpot, whatever) is up to date. Junky or missing fields? Fix them. - Customer list: Identify your current “best customers.” You’ll need these as a reference. - Enrichments: Decide if you want to use third-party enrichment (like Clearbit or ZoomInfo). Madkudu can use these, but don’t assume they’re perfect. - Lead fields: Standardize things like company size, industry, and job title. Inconsistent data here will throw off your model.
Pro tip: It’s tempting to dump every field you have into the model. Don’t. More data = more noise, not necessarily better predictions.
Step 3: Integrate Madkudu with Your CRM
This is where you actually connect Madkudu to your source of truth. The setup steps are straightforward, but don’t breeze past them.
What to do: - Go to Madkudu’s integration settings. - Connect your CRM. This usually means authenticating with admin credentials. - Map your key fields—company name, domain, revenue, employee count, etc. - If you’re using enrichment providers, connect those too.
Things to watch out for: - Field mapping mistakes: If you map the wrong field (like “employees” to “revenue”), your whole model will be nonsense. - Data sync issues: Make sure your CRM is actually syncing with Madkudu. Test with a few sample records.
Ignore: Any “recommended” fields that don’t actually matter to your business. Just because it’s available doesn’t mean it’s useful.
Step 4: Define What a “Good Customer” Looks Like
This is the step most people half-bake. Don’t just say “anyone who buys is a good customer.” Get specific.
How to do it: - Pull a list of your best customers (the ones you’d clone if you could). - Look for patterns. Common industries? Company sizes? Roles? - Write down the criteria. For example: “Tech companies in North America with 50-500 employees, using our product for at least a year.”
Why bother? Madkudu’s model uses this as the gold standard. If you’re vague here, your model will be vague too—and your sales team will notice.
What to skip: Vanity metrics. “High website activity” or “opened our last email” does not make a customer “good.” Focus on outcomes.
Step 5: Build and Configure Your Customer Fit Model
Now you’re finally ready to use Madkudu’s modeling features.
In the Madkudu dashboard: 1. Go to the Customer Fit Model section. 2. Select “Create new model.” 3. Pick the fields you want to include (company size, industry, tech stack, etc.). 4. Designate your list of “good customers” as the positive examples. 5. Optionally, flag churned or low-value customers as negative examples.
Madkudu will: - Analyze your data and propose a scoring formula. - Automatically test different combinations of fields. - Show you which factors seem most predictive.
Don’t get hung up on: - Making it perfect on the first try. The first model is just a starting point. - Including every possible field. Stick to what matters. - The “black box” effect. If you can’t explain why the model picked certain criteria, revisit your data and positive/negative examples.
Pro tip: Use Madkudu’s explanations to sanity-check the results. If it says “Website domain length” is the best predictor, something’s off.
Step 6: Test, Validate, and Tune
Here’s where most teams either skip ahead or overcomplicate things. You need to check if your model actually works before rolling it out.
How to validate: - Backtest: Apply the model to last quarter’s leads. Did it surface the best customers? Or did it miss obvious winners? - Get feedback: Show the scored leads to your sales team—do the high scores make sense to people who actually talk to customers? - Spot check: Look at some “bad” leads that scored high. Try to figure out why; is it a data error, or is your model overfitting?
Tuning tips: - If you’re getting too many false positives, tighten your “good customer” definition. - If your model is too narrow, consider adding more positive examples or relaxing criteria. - Don’t be afraid to remove fields that aren’t helping.
What to ignore: Don’t obsess over getting the “perfect” score distribution. Real-world data is messy. You want something directionally helpful, not a theoretical maximum.
Step 7: Deploy and Monitor
Once you’re happy with your model, push it live. Madkudu will start scoring incoming leads in real time.
Key things to do: - Set up lead routing or alerts based on scores—don’t just let the scores sit in a dashboard. - Check in with your sales team regularly. If they start ignoring the scores, something’s broken. - Review the model’s performance every month or quarter. No model stays accurate forever.
Pro tip: Keep a feedback loop open. If sales keeps flagging certain types of leads as “not actually a fit,” update your model.
Don’t waste time on: Over-automating before you know the model’s working. Manual review is fine in the early days.
Step 8: Iterate (But Don’t Overthink It)
Customer fit models aren’t “set it and forget it.” Your business changes, your customers change, and so should your model.
- Schedule regular reviews (quarterly is usually enough).
- Add new “good” and “bad” examples as you get more data.
- Prune fields that stop being predictive.
One last thing: Resist the urge to tweak constantly. Your gut and your team’s feedback are worth more than minor statistical improvements.
If you take one thing away: keep it simple, stay close to reality, and don’t confuse “model” with “magic.” Madkudu is a solid tool, but your results will depend far more on clear definitions, good data, and tight feedback loops than on fancy algorithms. Build, test, listen, and adjust. That’s how you actually get value—no hype required.