Spoiler alert: it’s not dead, but it is evolving.
Businesses put an enormous amount of time, energy, and resources into connecting with leads and trying to convert customers. They’re using a million tools out there, including lead generation tools, ad platforms, CRMs, business consulting services, and more.
There’s a blind spot, though, when it comes to lead scoring.
Most businesses don’t use lead scoring, which is the process of ranking leads based on criteria like how they discovered your brand or what industry they’re in. This is because early interactions of lead scoring tools often would be underwhelming at best.
But a number of factors have changed to create a new iteration of lead scoring. The new iteration has been fueled by more advanced data technology, shifted consumer behavior, and improved data management practices. It’s become an exceptionally useful resource for businesses who want to identify high-value leads and act fast to convert them.
Related: Top 50 Lead Generation Tools in 2023, Ranked & Rated 📚
Why lead scoring failed before
We’ve all heard the “big data” buzzword. It spiked in 2010, when data capabilities were made available to businesses in new ways.
Companies knew they needed to brace for more tracking, more information, more data, more complexity. While it seemed like the time was right for lead scoring, it was still too early. There are a few reasons why.
More data isn’t always better
Everyone went all-in on the “the more data the merrier,” but that’s not the whole picture.
More data isn’t always better. More accurate, relevant data is what’s crucial. This is why data management practices became essential over time, but they were lacking when lead scoring first hit the market over a decade ago.
Without data enrichment, you can end up with incomplete fields in your database. You could also end up with poor quality or inconsistent information in your records without data hygiene. And without a data refresh, you can end up with stale data.
Sometimes issues are minor but leave significant impacts. In a database for a B2B brand, some users will have a “VP” title while others have a “Vice President”. It’s a minor detail that could actually impact machine learning, algorithms, and segmentation.
And what about when you’re using outdated information to form an ICP? The data is three years old, and only about 7% of the information fields contain any data. Your information is limited, and what is there may be irrelevant, with users who have shifted to different roles.
Tools couldn’t piece together the right insights
You need to resurface the right insight at the right time, or the data doesn’t matter.
And “insight” is different from “data”. Insights stem from data and are guided by both human knowledge and machine learning.
So if you have a lead scoring tool that isn’t capable of drawing accurate insights from the data it’s given, that can just derail the value of the tool quickly (and it can derail your sales and marketing teams).
Operators focused on the “ideal perfect state”
With that “the more data the merrier” cliche came to the false notion that you would collect as much data as possible and it would be the “ideal perfect state” of data. You’d have literally everything you could ever need to know, and the data would be perfect and pristine.
This isn’t the reality, however. Even today, we have “fuzzy” data without good data management practices, which is incomplete or inaccurate.
The goal is to enable your company to execute in a “fuzzy” world where the data is constantly improving/expanding but forever imprecise and incomplete, where customers’ job roles changed, people left companies, and the market itself shifted over time.
Back then, lead scoring tools were not capable of adapting to all these changes. This is particularly true considering that many operators weren’t adapting, either; they were so focused on the “perfect” state of data that they neglected to realize there was no such thing.
There wasn’t enough computing power
If you work in tech, you’ve almost definitely heard about Moore’s Law. In 1965, Gregory Moore observed how the number of transitory densely integrated circuits doubled about every two years. He projected that this rate of growth would continue for at least another decade.
Fifty-five years later, that trend is still true. It’s hard to appreciate the sheer scale of change: from 5k to 50B transistors.
This goes hand in hand with computing power.
In 2000, the year after Salesforce was founded, one dollar would buy about 100 times less the computing power that it would buy you just 10 years later.
When the first wave of lead scoring solutions was built, they were designed in a data-poor world where computing itself was a cost. As a consequence, the default design choice was to try to minimize it for the sake of keeping cost structures under control.
And with minimal data and computing – and definitely no machine learning like what we have today – the tools were inherently limited.
How today’s landscape has changed lead scoring potential
Today’s technology and data world couldn’t be more different from what it was like 15 years ago.
We’re in a data-rich world with more information about data management practices. And for all intents and purposes, computing is basically a non-cost compared to the past; even small businesses can access computing power that’s almost unreal compared to even ten years ago.
It’s an incredibly liberating thought that allows and warrants a complete redesign of lead scoring and its mechanics, opening the field up to new possibilities.
Lead scoring tools have the potential to be dynamic, as opposed to static like they were in the past. When you’re using a lead scoring tool, you get a static number telling you the value of that lead. But any operator worth their salt knows that following up on a promising lead today is drastically different than trying to follow up in three weeks. You’ve likely lost their intention, they’ve lost some motivation and excitement, and they may have even started up with one of your competitors.
As a result, changing that lead score on a daily basis as time goes by is important. With more computing power, lead scoring tools don’t rely on manual updates to take note of these changes; our tools can do this automatically.
Today, you can say “my sales cycle is an average of 90 days and the data tells me that I need to close within the first two weeks for certain events.” You can then set your lead scoring tool to decrease a bit over a few weeks, and then more aggressively from week three to thirteen.
This, too, wasn’t possible 15 years ago. And it’s so crucial to note that it isn’t just the tech that’s changed. Consumer and business behavior has shifted dramatically over the years, too. Let’s take a look at that.
5 factors that make lead scoring so valuable
Does anybody remember the Cambrian period in history? This was when most organisms were simple until the rate of diversification accelerated at an exponential rate. We’re experiencing the same thing today in the tech world. It doesn’t have a name yet, but it’s easy to see. And not only is tech changing, but the way we interact with it is, too.
There are five human-driven factors that make lead scoring tools more valuable and accurate than ever before.
1. We’ve seen a massive shift from offline- to online-first behavior for consumers
Customers are starting and ending their customer journey online instead of going into stores. This means more interactions are happening online, which can mean more accurate data. It also means that you need to act fast. You can’t wait two weeks to follow up anymore, it needs to be instant.
2. The number of companies doing most of their business online has increased
Even one-person service-based businesses can use self-serve ad platforms, Google Analytics, and tracking tools to get more in-depth knowledge about their customers and the customer journey.
3. The number of customers online has exploded
Again, this means more data coming from more sources as internet users increased from roughly 45M people in 1995 to 4.6B+ active users as of October 2020.
4. Businesses have shifted their approach
They originally started with a sales-driven approach. Then a marketing-driven approach, and then a product-driven approach (product-led growth). Now, businesses realize they need to use all three in tandem, aligning the efforts across departments. This capability is enabled by the internet and technology like never before.
5. The tech stack available for companies has exploded
You can see this with the ChiefMarTec landscape, which started mapping tools and solutions in 2011, when there were only around 150, to 2020 with an incredible 8,000. Companies went from using a handful of tools to an average of 185 tools for a 100-1000 person company, or 288 for a 1000+ person company in 2020.
There are more consumers if you’re a B2C business and more businesses if you’re B2B that engage online than ever before. There’s also more sophistication than in the tech stack, which is more accessible to businesses. This facilitates lead scoring and improving and offering more in-depth data while boosting data management practices.
In a nutshell, most of the paradigms in the context of which the mechanics of lead scoring were thought of aren’t really true anymore.
How to leverage lead scoring for success
Machine learning is definitely not the magical solution to every computational problem. Machine learning has clear limits.
One of those limits is when it comes to both the present and the future because it assumes that both will behave as they did in the past.
The above assumption is not necessarily true, it is actually less true as time goes by and the speed of change in a business’s context increases. It’s a concept called temporal reality. This is something that’s true today, but it wasn’t in the past, and it won’t be in the future.
There are always a million changes in your business on a seemingly day-to-day business. You might want to shift your target audience, go for a rebrand, use a new ad platform, switch up your messaging, push certain products only for a few months, or even adapt to changes in the market. Machines don’t know this; they’ll figure it out six to nine months down the road.
That’s why black boxes don’t really work, and machine-first types of solutions don’t really get the job done in quickly evolving businesses and markets. And it’s why you need a machine-assisted, human-driven approach.
There’s a shared revenue responsibility between marketing, sales, and customer success, bringing back a more holistic approach to the conversion funnel. At its core, a flywheel engine acts as the glue and the grease in between departments, functions, and tools.
So there you have it. If you’ve ever wondered why lead scoring is such a simple idea and yet an incredibly unsatisfying implementation, now you know.
It was implemented 10 years too soon without the data, knowledge, or tech to really make it work.
This clearly isn’t the case anymore.