What is lead scoring? Lead scoring and routing explained

Lead scoring is attributing a value between 0 – 100 to both inbound and outbound leads in order to estimate their likelihood to convert or do business with you.

The lead score attributed to a lead is always an estimate or prediction, based upon external and internal signals.

It will allow your organization to better predict the potential value of business which plays a crucial role in your loadbalancing of resources.

Companies taking their sales development reps seriously and aiming to do business at scale, will use lead scores in order to guide prioritization (especially in businesses with high volumes of leads) and use scores as a signal in order to estimate their potential sales pipeline.

Lead scoring can be set up directly in your CRM or using software such as LeanData. Nevertheless, the location of your lead scores is inferior to the data it is based upon and how the data is used in order to attribute a score to a lead.

In complex scoring models, both internal and external scoring signals are used in combination with a machine learning model in order to estimate the value of the lead using up to date data to learn, in a live setting, the correlation between the data and the lifetime value of existing clients.

Implementing an effective lead scoring strategy will allow your organization to decrease its cost of sales, while increasing the average value of the leads you work.

It starts by defining the (potentially) best performing leads and allocating the right resources to value generating activities. Or simply put: decrease the time necessary to make more net new revenue.

In order to do so, organizations need to:

• Get rid of non-value generating activities
• Make revenue generating activities easier
• Understand and prioritize activities that generate more money

Lead scoring plays an important role in the ability of a company to do this. It impacts the quality and prioritization at the start of the sales funnel which will prevent waste of resources further down the sales process.

Lead scoring is like fueling your car with the most efficient resource. It decreases your cost (you pay less for the resource), increases the value generated (you can drive longer) and it limits your waste of resources (your environmental impact is lower).

The three most important benefits of lead scores are:

• You will work on leads that will generate more revenue (drive longer)
• You will invest less time of reps on low value opportunities (waste is lower)
• You will be able to use that time for other activities or remove headcount (pay less)

In short: you will be able to decrease the cost of sales without compromising on value.

It shouldn’t come as a surprise that the best signals for lead scoring are highly dependent on your business. Companies will generally categorize signals in three types:

• Internal future signals
• Internal current signals
• External signals

While the specific signals within these categories might be (and better be) different between organizations, the overall type is similar amongst all organizations. To future clarify, you will find a breakdown below with examples.

Internal future signals are attributes of existing good performing clients which we will look for in newly created leads.

They are basically an indication of ‘what success looks like today’ and for which we want to type cast when looking for new potential clients.

Examples are: industry, company size, software on client’s websites (which can be found using BuiltWith), annual revenue, headquarter location, presence of departments etc.

Important when looking at future signals is to keep two caveats in mind:

• When you score leads highly that have the same attributes, there is a risk your company will become overdependent on these type of clients (lack of diversification)
• You need to have access to live data as what is performing today might be bound to seasonality, macro economics, your current product features etc.

To prevent reading data wrong (e.g. reporting) and take decisions without deeper insights, your revenue operations, sales finance and sales strategy partners need to collaborate.

Example of signals: LinkedIn Advertising Solutions

LinkedIn Advertising Solution might use future signals such as: employee growth, the presence of a career page, rapidly growing industries, etc.

The company is prone to mistakes when it would take reporting for granted without looking behind the data.

Example of signals limitations: LinkedIn Advertising Solutions

One can expect that air travel companies had tons of jobs after covid (which made them in desperate need for advertising job postings), but once all routes are reestablished, their headcount and subsequent job posting advertising needs would tumble.

Internal current signals are signals we are getting either through a lead form, newsletter or event registration and from product usage.

The first category of internal current signals is simply a combination of all the parameters that a potential lead declares when signing up for your product or service.

In an outbound context, these parameters are the ones that are publicly available to us.

The goal is to match these signals with the ‘internal future signals’ in order to find out how well the parameters of your new lead are matching with your existing best performing clients. This match rate is what eventually defines our lead score or the prediction of the potential performance of this new lead.

The second category are signals we can obtain from interactions between the lead and our product or service. Let’s look at an example.

Example of internal current signals

Consider Cognism, a company founded in 2015 which provides software for contact enrichment either manually or directly in your CRM.

Based on the different product-led actions the user has taken, Cognism will be able to drive sales, marketing or growth (outside the scope of this e-book) but also determine the ‘willingness to work’ with their software of the lead.

This is an important indicator. The product-led signals give an indication of how advanced the lead is in the funnel which will have a positive impact on their likelihood to convert or buy the product.

Machine learning can give companies an additional competitive advantage. The algorithms will analyze the data, identify patterns, build models to predict lead conversion likelihood and eventually help attributing a lead score in real time.

The best lead scores are not static and changes based on new interactions.

The last category is external signals: signals you are able to find online to enrich your internal signals at scale.

The important difference with the previous category of signals is:

• They are not directly provided by (an action of) your leads
• They are provided at scale (e.g. with minimal human intervention)

“Data is the new oil” — Clive Humby (2006), mathematician and entrepreneur

And your competitors know this too. Data is the new oil and data input from humans is often limited. To make better decisions about the likelihood of a lead to convert and give a lead a score in line with it, companies can choose to enrich their data with external data.

Through integrations with enrichment software, databases and libraries a company can collect more relevant and correct information about their leads. This will positively impact the prediction.

A couple of ideas here are:

• Connecting with Data.ai (formerly AppAnie) to get information about mobile apps
• Connecting with SimilarWeb to get competitive and market size data
• Connecting with Phantombuster to crawl social media data
• Connecting with Clay to get data from Google Maps, event attendance, etc.
• Connecting with Contact Enrichment tools to get up to data contact information

There is a tremendous amount of software available in order to accumulate useful data based on the unique parameters you mostly will collect through your lead form (first name, last name, email and website). The latter provides a unique combination which can give you access to the endless extra signals.

It’s important though to note that Clive was wrong (sorry, not sorry). Data is not the new oil.

“Data is merely the new crude oil. Insights are the new oil.” — Megan Foster (2023), no one significant

Ok, when I am honest with you, I suspect Clive knew this and I definitely cannot take credit for this reinvented quote. The point is also not who said what, the point is way more simple:

Accumulating data for the sake of accumulating data is useless and can be damaging to your business.

To make sense of data, you need to know which data to collect and how to find insights.

What about constraints and action?

You need to take constraints into account and make theoretical work actionable by sellers:

• Align headcount limitations with demand volumes
• Align headcount with the most revenue generating opportunities
• Provide clear actions to sellers in order to help prioritization decisions

Lead routing is the answer to the three necessities above. It’s an (automated) system to determine how to align your resources (headcount) to demand (leads) and defines prioritization (type and time to response).

Consider a medical emergency center with two ambulances available getting ten calls for help. The core task of the dispatch is to align their ambulances (resources) to people in need (demand) and decide who to help first (prioritization) as they are unable to help everyone all at once.

Similar logic applies for business – although the business is not saving lives. A company has x amount of Sales Development Reps (resources), y amount of leads (demand) and lead scores to decide which leads will most likely generate the most revenue (prioritization).

This decision on how to align limitations with demand volumes is called load balancing and the process to allocate actions to sellers to help prioritize the most revenue generating opportunities is lead routing.

Revenue Operations plays a crucial role to lead the project of setting up, changing and working with L&D* to roll-out lead routing. Technical implementation is outside of the scope of this article, but this mostly can be done through queues in a CRM or external software such as LeanData.

Lead scoring using the right signals, based on the right data and insights, in combination with clear prioritization set by lead routing will be a powerful tool in order to allocate your resources to the most revenue generating activities and eventually decrease the cost of sales while increasing output (conversion and revenue).

1. Revenue Operations Lead Routing © SlideFill 2023

So what’s the role of Revenue Operations and your RevOps Manager in all of this?

Revenue Operations is the strategic link between sales (processes), tools, motions, marketing and service. In the case of assigning lead scores to inbound or outbound leads this manifests itself in three key areas of responsibility:

• Logic to transform signals into lead scores
• Integrations to capture and receive signals
• Lead routing, constraints and load balancing (as seen above)

The first objective of a Revenue Operations Manager is to be able to transform raw signals into lead scores. This in the core means:

Working together with cross-functional (XFN) stakeholders in sales to understand the different parameters driving success for their business and determining ‘the weight on the likelihood to convert’ of these parameters itself.

The role encompasses a tremendous amount of cross-functional stakeholder management and the beauty is the zoomed-out, holistic view Revenue Operations need to obtain in order to understand the impact from start to end (and the other way around) on the sales funnel.

The most successful RevOps Managers you will encounter in a professional setting, are those that know a lot about the different stages in the business, but not too much to be inherently biased about any function in particular. They have Pareto efficient knowledge of every step in the funnel.

Once they determine (together with sales) the needs of the ‘last step’ in the sales funnel (existing business) they work their way back to the start of the funnel (new business) in order to determine which signals are available in new business and how these signals can be weighted to facilitate prioritization and value generating activities for the last step of the funnel.

This weight attribution to parameters requires in-depth data analysis and collaboration with Sales Strategy and reporting to see what are common threats between the most successful clients.

In the most simple terms: they make a list of all data points available for existing clients, that are captured for new potential clients, and put a weight to the correlation of that data point with the revenue and value generated by existing clients.

After this exercise has been done, RevOps will work together with CRM engineering or a software partner (lead routing) in order to implement the automatic attribution of the weighted scores when signals are present on newly created leads.

The sum of these weighted scores will determine the final lead score of the newly created lead and is the indicator helping Sales Development Representatives to prioritize their leads.

Additional note about hiring from the author:

The topic on how to compensate and set up a successful Revenue Operations Manager team is outside the scope of this article, but is covered on SlideFill. Most important to know is that tying variable compensation of RevOps to sales outcome (target attainment) will have a favorable effect on the alignment between personal and sales objectives.

Under the previous objective you looked at weighting data or signals in order to come up with the final score. And as we learned “data is the new oil”, Revenue Operations has a second objective: ensuring the right signals are available to attribute the right score.

The first step can be easily clarified by the ‘Cognism’ example you looked at before. If Cognism decides insights about their product usage could be a meaningful driver to make business decisions, but they do not have the data available on how their users interact with their product, they have a gap.

Analyzing gaps in data is the continuous process of making assumptions of ‘what could have impact on the business’ and finding the missing data points to validate the assumption.

Getting the missing pieces of the puzzle is more complex as signals are often omnipresent in other infrastructure but not in your CRM.

Data can come from:

• Manual data input from sales reps during the sales process
• Product usage signals captured by engineering in a database
• Event registrations input captured by marketing in event software
• Client engagement data captured in SEP* (Outreach) or SIP* (Gong)
• External data enrichment tools such as SimilarWeb, Lusha and Cognism
• Meeting scheduling signals captured in scheduling software (Calendly, Chili Piper)
• …

There are thousands of sources of data that can be captured (keeping in mind accumulating data for the sake of data is useless) that all require cross-functional or stakeholder management.

Revenue Operations will be tasked with setting up a Business Requirement Document in order to scope out the business impact, value, technical need, training need, reporting, timeline, change management, stakeholders and future state while driving the management of peers to drive the execution of that document.

They are capable of translating the business idea into tangible value and language understandable for technical, product and sales partners.

2. Revenue Operations Lead Scoring Model © SlideFill 2023

* SEP = Sales Execution Platform
* SIP = Sales Intelligence Platform

Future prediction: client engagement and CRM updates

The future is very exciting. In-call (meeting) client engagement has been a very tough nut to crack, especially linking back meeting conversations to your CRM. Sales people are in general not too keen on taking in-depth notes.

With Generative AI making its way to sales processes, there is a tremendous opportunity to summarize calls at scale (through for example Gong in combination with GenAI) and push that information to your CRM, using text mining to update opportunities, objection reasons and other dull administrative tasks.

* L&D = Learning and Development

Before you head to the conclusion of this article, you can find answers on frequently asked questions related to Revenue Operations and Lead Scoring.

Regardless of the size of your business, you should always prioritize getting the most return (lead scoring) for the lowest cost (lead routing).

If you are looking for three step you can do today:

Look at your existing business and decide which attributes of your existing, well-performing, clients you would be able to find in net new leads. Define different internal, future and external signals that can be used to replicate success in the future.

Estimate the volume of leads adhering to these attributes and map out your current set of resources (headcount, hours, material, language coverage, regional presence etc.). Create a table of ‘input’ and ‘output’ and decide how the two of them should line up to each other.

Educate your resources to handle the highest priorities first and create a scalable system in order to work prioritized leads through lead scoring in a favorable order keeping in mind your lead scores, lead routing and load balancing. Build integrations in order to allow for automation in the scoring and assignment process.

This expert article is part of a free eBook and 7-part series addressing multiple aspects of how to do scaled business in today’s economy:

1. Lead Scoring and the impact on revenue
2. From reporting to revenue generating insights
3. Automation of revenue generating activities
4. Closing the service gap through data
5. Making marketing initiatives measurable
6. Customization of content at scale
7. Revenue Operations and generative AI

Thank you for reading.

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Views are personal and not affiliated to employers.
No generative AI was used to write the article.
All examples are illustrative and fictional.