Is Lead Scoring a Good Idea for Tech Startups?

Forensic Lead Development …

Lead scoring has been one of those ideas in B2B marketing that sounds almost too sensible to question. The theory is to give prospects points for the things they do, and by scoring their behaviour, it tells you something about their intent – a word which is overused, oversold and I personally find, often used like snake oil. Lead scoring allows you to add points for characteristics that indicate a prospect is a good fit and deduct when they become inactive. When someone reaches a certain score, marketing sends them to the sales team as a qualified lead. Simple, right? Well, sort of. Let’s look at why this simple concept can be as controversial as a political statement.

Where Does This Controversy Come From?

As direct response marketing becomes less prevalent and we rely more on inbound marketing, driven by content syndication, the desire to score leads and make sense of who is looking at you becomes increasingly valuable. Here’s an example: A prospect downloads an eBook, visits your pricing page, opens three emails and works for a company of the “right” size, so they receive 80 points and become a Marketing Qualified Lead (MQL). But why 80 points, and why is this prospect now being passed to a salesperson as an opportunity for them to follow up? Why is a visit to a pricing page worth 20 points rather than 10? Why does an email click accumulate five points? Does downloading an eBook indicate buying intent (there’s that word again)? Why does a senior job title really mean that somebody is more likely to buy?

• Sometimes the answer is yes.
• Sometimes it is absolutely not.

That makes lead scoring one of the more interesting areas of B2B marketing. All CMOs work hard to minimise the opportunity for conflict between marketing and sales. So, why adopt a system that can put you directly in the firing line?

What is Lead Scoring Actually Trying to Achieve?

At its simplest, lead scoring is a way of ranking and prioritising leads. A business might score prospects using two broad categories:

• Fit: This considers who the prospect is: company size, industry, geography, job title, technology stack, revenue and other characteristics.

• Behaviour: This considers what the prospect does: website visits, content downloads, webinar attendance, email engagement, product page visits, demo requests and other interactions.

The theory is straightforward. A prospect who looks like a good customer and demonstrates meaningful interest should receive a higher score than somebody who is a poor fit and has shown little engagement. The score then becomes an operational tool. Instead of salespeople receiving a database containing hundreds of contacts and having to work out where to start, the CRM can provide some form of priority order. This is where lead scoring deserves a more nuanced discussion.

The above statement is a much less glamorous proposition than “AI-powered identification of your highest-intent prospects,” but it may be closer to the truth for many tech startups.

The Research is Surprisingly Mixed

There is certainly evidence that lead scoring can work. A MarketingSherpa benchmark study found that organisations using lead scoring reported an average lead-generation ROI of 138%, compared with 78% among organisations that were not using it—a 77% difference. However, this was an observational comparison, not proof that lead scoring itself caused the improvement. That is an important point to discern. It could be that mature companies have better resources than tech startups. This means they are likely to be better at segmentation, CRM management, nurturing, measurement and sales alignment. In other words, lead scoring might be a symptom of marketing maturity rather than the cause of better performance.

There is other evidence that makes the picture considerably less rosy. Demand Gen Report’s 2016 Lead Scoring Survey found that 86% of marketers were using lead scoring. Yet fewer than two in ten rated their programmes as highly effective, while only 15% said Sales would rate leads meeting their scoring thresholds as highly qualified, real opportunities. That’s quite a gap between adoption and confidence. It gets more interesting as the same research found that companies which had been scoring leads for more than two years were considerably more likely to regard their programmes as effective. Organisations that regularly reviewed their models also reported better results, with highly effective ratings reaching 50% among those reviewing their models weekly. That suggests something important: Lead scoring isn’t really a “set it and forget it” exercise.

The biggest problem is we’re scoring assumptions. Traditional lead scoring usually involves deciding what signals indicate buying intent, but who decides? More to the point, what evidence are they basing the scoring on? Usually, it’s marketing that drives the lead scoring effort, with input from the sales team. All they are doing is making educated guesses based on researched information, their experience doing the job and a hopeful finger in the air.

Where Does the Confusion Start?

Now let’s consider a hypothetical B2B cybersecurity startup. The marketing team might decide that a Chief Information Security Officer visiting the pricing page is a very strong buying signal. This seems logical, but what if that person is researching the market for a report they are writing? What if a junior employee downloads five pieces of content because they have been tasked with researching suppliers, while the eventual buyer never visits the website at all? What if an existing customer repeatedly visits the website? What if a competitor’s employee downloads everything you publish? Suddenly, the score starts looking rather less scientific.

A 2022 Openprise survey of more than 250 B2B marketing professionals found that only 35% had full confidence in their ability to accurately score leads. The biggest challenges included sales adoption, incorporating firmographic information and incorporating behavioural signals. That is a revealing statistic. If only around a third of practitioners are completely confident that their scoring is accurate, perhaps we should be careful about presenting a score of “87” as though it represents some objective measure of purchase intent. Any marketer worth their salt knows it doesn’t. It represents a model, and a model is an approximation of reality.

Does Lead Scoring Actually Give Sales Better Leads?

This is ultimately the question that matters – at least if you work in sales. If a startup spends weeks developing a scoring model but salespeople continue to say, “These aren’t the leads I want,” then the exercise has failed. Lead scoring should therefore be judged on commercial outcomes, not how sophisticated the scoring system looks.

Ask these questions about your lead scoring:

• Does the highest-scoring group convert at a materially higher rate?
• Do sales spend less time investigating poor-fit prospects?
• Has response time improved (the time it takes sales to respond to an enquiry)?
• Is the percentage of marketing-generated opportunities increasing?
• Do the sales team trust and use the score?

Those are much more useful questions than whether you have 47 scoring rules configured inside the CRM.

Academic research provides some encouragement. A systematic review of 44 studies published between 2005 and 2022 found that lead-scoring models can positively affect sales performance, with predictive approaches generally outperforming traditional approaches in the studies reviewed. However, the research also highlights significant problems, including poor-quality data, insufficient datasets and models based on historical conditions that may no longer reflect the market. In other words, better mathematics doesn’t necessarily solve a bad-input problem.

Garbage in, beautifully calculated garbage out.

Predictive Scoring Isn’t Necessarily the Answer for a Tech Startup

This is where vendors can make the subject sound much more complicated than it needs to be. Predictive lead scoring uses historical data and machine-learning techniques to identify characteristics associated with conversion, which can be powerful. A startup may not have enough historical data to make it worthwhile, so the timing is critical. If you have generated 300 leads and closed 17 customers, you may not have a sufficiently large or representative dataset to build an impressive predictive model. Even if your CRM platform offers predictive scoring, the existence of a button doesn’t mean you have the data quality required to make the output relevant and meaningful. This is one reason I would be cautious about a startup investing in sophisticated scoring before it has established the basics.

What you need to get started:

• A clear understanding of your ideal customers.
• Reasonably clean CRM data.
• Enough lead volume to identify meaningful patterns.
• Define outcomes against which to test your assumptions.

So, should a B2B tech startup implement lead scoring? The answer is sometimes yes and sometimes no. I wouldn’t make it an automatic part of the initial marketing technology checklist. If your startup generates only a handful of genuinely relevant opportunities each month, manually reviewing them may be better than building a complex scoring system. If your sales team has ten highly targeted accounts and each requires a personalised approach, a score of 73 versus 68 probably isn’t going to change much.

If you’re selling an enterprise solution with a long, complicated buying journey, the individual “lead” may not even be the right unit of measurement. There might be six people or more involved in the buying committee. One person downloads a report. Another attends a webinar. A third searches Google. Someone else speaks to your salesperson. The buying signal belongs to the account, not necessarily to one individual. This is why account scoring and account-based marketing can become more relevant as B2B technology companies move upmarket.

On the other hand, lead scoring becomes much more attractive when you have a substantial volume of inbound leads and a relatively standardised product and sales process. If Sales has 500 leads sitting in the CRM, it needs a sensible way to decide which ones deserve attention first, and that’s where scoring can become extremely useful.

Lead Scoring May Be More About Prioritisation Than Prediction

This is the most important distinction. Don’t necessarily think of lead scoring as a machine that tells you: “This person is going to buy.” Think of it as a system that says: “Based on what we currently know, this person deserves more attention than that person.” That is a much more realistic proposition that also makes the implementation considerably easier.

You don’t need 50 different scoring criteria. Instead, you might start with five or ten. For example:

• Is the company in our target market?
• Is the person in a relevant role?
• Have they demonstrated meaningful interest?
• Is their behaviour significant enough to represent real interest?
• Has that activity happened recently?
• Do they have characteristics associated with existing customers?

You can then create a small number of practical categories rather than obsessing over an allegedly precise number. Here’s an example of the categories you may consider:

• Priority: Sales must investigate now.
• Nurture: Potentially valuable but not ready for direct sales attention.
• Low priority: Keep in the database but don’t actively pursue.

That’s already useful and gives marketing and sales teams a foundation to move forward. It allows for future development and optimisation as you accumulate more data and learn about the market and how it operates. This avoids getting yourself tied up in complexity too soon, or overpromising scoring accuracy to an increasingly frustrated sales team. Instead, marketing and sales set appropriate expectations based on a true understanding of what lead scoring really means and remain conflict-free when the inevitable priority lead turns out to be someone who thought they were downloading an eGuide on an adjacent topic and got confused. Keeping things simple and working within the limitations of what you have and what’s available is the sensible path forward. While ownership of this activity tends to sit within marketing it’s incredibly important to work with sales from the start and continuously collaborate to improve and refine as you scale the business.

You may want to read: “How Startups Can Implement Lead Scoring.”