Lead Qualification

When a company sells something, it continuously collects contacts: people who filled out a form, downloaded a price list, or left their business card at a trade fair. In sales, such contacts are called leads. Most of them will never buy anything. Some only clicked out of curiosity, others don’t have the money for it, and yet others aren’t even allowed to make purchasing decisions at their company. Lead qualification is the process of sorting these contacts and figuring out for which of them a call or an offer is worthwhile. The result is usually a ranking or a division into groups such as “contact now,” “check again later,” and “discard.”

Why sales time is the scarcest resource

A salesperson can manage perhaps twenty serious conversations a day. But if two thousand contacts come in per month, they simply cannot call all of them. Without sorting, they work through the list in random order. Statistically, they then spend most of their time with people who will never buy. This is exactly what makes lead qualification one of the most costly issues in sales.

The economic leverage is considerable. If only three out of a hundred contacts buy, but qualification reliably sorts these three to the top, revenue per hour worked doubles or triples. That’s why companies spend a lot of money on software that handles exactly this sorting. In technical jargon, revenue per unit of sales effort invested is often called sales efficiency.

There is also a downside that is often overlooked. Anyone who filters too strictly discards contacts who would have bought later after all. A student who is only gathering information today might be a purchasing manager in three years. Good qualification therefore doesn’t sort into “good” and “trash,” but distinguishes between ready now and not yet ready.

From BANT questions to model-based scores

Classically, one works with fixed screening questions. The best-known scheme is called BANT and asks four things: Is there a budget, i.e., money for the purchase? Is the person even allowed to decide? Is there a genuine need? And is there a timeframe by which the purchase should happen? Anyone who meets all four criteria is considered qualified. This is simple but crude, because a human first has to laboriously ask for the answers.

More modern is what’s known as lead scoring. Here, each contact receives points for their behavior and characteristics. Three visits to the pricing page earn points, a company email address does too, while a disposable email address deducts points. In the past, marketing staff set these rules by hand. Today, this is often handled by a machine learning model, that is, a program that derives patterns from historical data.

This model gets to see thousands of closed cases: contacts who bought, and contacts who dropped off. From this, it learns which combinations of characteristics are typical for a deal closing. New contacts then receive a probability, for example 0.08 for an eight percent chance of closing. In addition, language models now also read emails and call notes, extracting clues that appear in no form field. An important pitfall here: the model learns from the past. If sales previously ignored certain industries, the model will continue to consider them hopeless.

Where you encounter qualified leads

You experience lead qualification more often than you realize. If you ask for a quote on a website and a chat window first asks about company size and timeframe, qualification is taking place. If you answer “personal use, no budget,” often no one gets back to you afterward. If you answer with a company address, you usually get a call within an hour.

In the business press, the term comes up with providers like Salesforce, HubSpot, or Microsoft Dynamics. Their software is called CRM systems, that is, software for managing customer relationships. When AI features are mentioned there, it’s almost always about automatic point-scoring for leads and text summaries of customer conversations. This is relevant for investors because such features provide easily measurable benefits and are therefore easy to sell.

You should keep two terms apart. A Marketing Qualified Lead, or MQL, has shown interest through their behavior. A Sales Qualified Lead, or SQL, has additionally been vetted by sales and deemed serious. The handover between these two stages is, in many companies, the classic point of contention between the marketing and sales departments.