These are the two CRMs small businesses weigh against each other most often, and for good reason: they answer the same question in opposite ways. HubSpot lets you in for free and then sells you a platform covering marketing, sales and service. Pipedrive charges from day one but hands you the clearest sales board on the market. We scored both 4.4 out of 5. Here is how to decide.

Which one should you pick, if you only have a minute?

How good is the HubSpot free plan, really?

This is the headline argument, and it holds up. HubSpot’s free plan is not a time-limited trial: it is free forever, with 2 users and up to 1,000 contacts. You get contacts, deals, tasks and email tracking.

Pipedrive has no free plan at all, only a 14-day trial with no credit card required. That is a genuine disadvantage for a young company trying to get organised before it has revenue.

Which one does a team adopt faster?

Pipedrive, scoring 4.8 out of 5 on ease of use against 4.6 for HubSpot. Its promise fits in one image: a board where deals move towards a signature by drag and drop, and where the state of the pipeline reads in three seconds.

In practice, a sales team can be running on it in an afternoon, with no CRM administrator and no configuration project. HubSpot asks for more setup, because it offers more ground to cover.

What does each one do?

Which one really costs more?

Both have a pricing trap, and they are not the same one. You need to know both before signing.

Which one for your team?

One closing note: the CRM that fails is almost never the one that lacked features, it is the one the sales team never filled in. Between two tools we score identically, pick the one your team will open every morning.

Past roughly fifty new contacts a month, it becomes impossible to treat them all with the same care. Some prospects are ready and waiting for a call, others have barely discovered your brand. Without a sorting method, your reps work blind and let deals slip while they chase cold contacts. Lead scoring delivers exactly that sorting: a grade that tells you who to talk to first. Let’s look at how to build it in practice.

What exactly is lead scoring?

Lead scoring is a method of ranking prospects by assigning points to each contact in your database, based on how well they match your ideal customer and how engaged they are. The higher the score, the “hotter” the prospect, meaning closer to a buying decision.

The goal is simple: rank your prospects from most promising to lukewarm so you concentrate sales effort where it pays off most. The score is not fixed, it moves with every action: a visit to your pricing page pushes it up, weeks of silence pull it back down. Lead scoring turns an undifferentiated contact list into a prioritized queue. If the idea of a lead itself is still fuzzy, our dedicated definition lays the groundwork before you start grading.

Why score your prospects?

Three concrete benefits explain why sales teams adopt scoring as soon as they handle real contact volume.

• Save selling time. Your reps work high-potential prospects first instead of running the list in arrival order. Their scarce time goes where it converts.
• Align marketing and sales. The score sets a shared definition of a “call-ready prospect.” No more arguments about lead quality: above an agreed threshold, the contact goes to sales.
• Follow up at the right time. A prospect who hasn’t hit the threshold isn’t lost, they enter lead nurturing until their behavior lifts the score. You call neither too early nor too late.

In short, scoring doesn’t create extra prospects, it makes you exploit the ones you already have far better. It’s a productivity lever, not an acquisition one.

Which criteria should you use to score a prospect?

A solid score rests on two families of criteria you must always combine. One tells you who the prospect is, the other tells you what they do.

The golden rule: a high score should require both. A prospect who fits perfectly but is completely passive isn’t ready. A very active contact who is off-target will never buy. It’s the crossing of fit and behavior that identifies a genuinely hot prospect.

What does a point model look like?

Here is an example model suited to a B2B SMB. The values are deliberately simple: you’ll tune them later with your own data. Note the negative points, essential to correct false positives.

With a model like this, a director at a target SMB who requests a quote (15 + 10 + 10 + 30) clears the bar by a wide margin, while a contact who opens three emails but never views your offer stays below the threshold. The model turns into numbers an instinct your best reps already have, but it does so systematically, on every contact, with no oversight.

How do you set the handoff threshold to sales?

A model without a threshold is useless. The threshold is the point total at which a contact becomes an MQL (marketing-qualified lead) and then an SQL (sales-accepted lead) ready to be called. A simple three-tier setup works very well to start.

These bounds are indicative. The right setting comes from watching your actual conversions: if your reps complain about cold leads above 30 points, raise the threshold. The point isn’t to nail the perfect model on day one, but to have one you correct every quarter in light of results.

How do you set up lead scoring in 5 steps?

No need to aim for a complex model from the start. Here is a progressive approach any SMB can apply.

Should you use rules-based or AI lead scoring?

There are two broad families of scoring, and the choice depends mostly on your data volume.

• Rules-based scoring. You set the criteria and points yourself, as in the model above. It’s the right approach for the vast majority of small and mid-sized businesses: readable, transparent (you always understand why a prospect has a given score) and easy to fix. Most CRMs offer this rules engine natively or as an option.
• Predictive AI scoring. Instead of setting points by hand, the tool analyzes your history of contacts and sales to find, on its own, the criteria that predict a deal. Powerful, but data-hungry: it needs several thousand past leads and sales to be reliable. Below that, it learns on too few examples and gets it wrong.

Our advice for an SMB: always start with rules. You’ll understand your own sales cycle, you’ll have a usable model in a few hours, and you’ll keep control. Predictive AI becomes relevant later, once your database is large and rules-based scoring hits its limits. To compare the CRMs that carry both engines, see our best CRM software 2026 comparison.

What mistakes should you avoid in lead scoring?

Even with a good model, a few pitfalls come up often and wreck the whole setup.

• Skipping negative points. A model that only ever adds points and never removes them ends up ranking everyone high. Without penalties, the score loses its sorting power.
• Confusing fit and behavior. A contact who scores well on fit alone, with no engagement signal, isn’t a hot prospect. Always require both.
• Freezing the model. A score set once then forgotten quickly drifts from reality. It should be revised at least every quarter based on observed conversions.
• Wanting an overly complex model. Twenty criteria are impossible to interpret and maintain. Five to eight well-chosen criteria are plenty to start.
• Not closing the loop. If the score triggers no automatic action in the CRM, it stays a decorative number. Crossing the threshold should generate a task for a rep.

That last point is decisive. Lead scoring only has value when wired into your sales tool: the CRM collects the signals, computes the score in real time, fires the alerts and files each prospect into the right sales pipeline. A spreadsheet will never track dozens of contacts that move every day. If you’re looking for the solution that fits your size and budget, our comparison does the sorting.