Lead scoring is the method of assigning points to contacts or potential prospects based on how closely they resemble your ideal customer profile. The higher the lead score, the more likely the lead is to be a good fit for your product or service.
💡Understanding Lead Scoring
Lead scoring is commonly used by marketing and sales teams to sort through their contact database and reroute the highest quality leads to the sales department immediately–significantly improving their sales funnel.
By implementing a lead scoring system, your sales team can de-prioritize low-quality leads and prioritize leads who have the highest chance of converting. This, in turn, can help to align sales and marketing efforts in a more measurable way.
A lead scoring process can be done through scoring software or manually through spreadsheets–however, the latter can be quite tedious and requires daily maintenance to be accurate.
Predictive lead scoring software takes this one step further and applies big data and machine learning algorithms to scoring to find the right combination of behaviors and data points of existing and potential customers. Then, these attributes are automatically matched and ranked to those of new leads.
Once your scoring system is in place, you can then use your marketing automation tool to send your qualified leads to your team to start the sales process and move leads down your sales funnel.
Through lead scoring, your sales and marketing departments can assign point values to your contacts based on how closely they resemble your ideal customer. Lead scores are then used to determine which contacts should be handled immediately by your sales team and which should go through marketing automation and nurturing campaigns.
Sales and marketing teams who implement a lead scoring system into their process typically see a higher conversion rate, a faster sales funnel, and a higher interest level than those who do not.
While scoring leads manually can be a time-consuming and frustrating endeavor, using good lead scoring software will allow you to uncover high-quality leads and revenue opportunities hidden at this very moment in your database.
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What is lead scoring?
Lead scoring is a methodology that sophisticated GTM teams use to rank and prioritize leads. The final score is usually determined by a lead’s behavior, like their interactions with the company’s website and emails, as well as demographic and firmographic information.
In a data-driven context, lead scoring also helps all teams assess the quality of their initiatives (i.e., how effective marketing campaigns are) using an analytical approach to evaluate and rank prospects and lead sales reps to relevant leads faster.
Lead scoring also identifies the best customers, allowing segmentation and targeted outreaches for upsell, cross-sell, and churn reduction.
Why is lead scoring important?
Scoring is not just beneficial to your sales team. Rather, lead scoring is an important process for any sales and marketing team in that it can streamline the way that marketing pre-qualifies leads and make any sales team more efficient by following up with the right leads at the right time.
When setting up a lead scoring system, you’ll be able to:
- Increase conversion rate. By finding qualified leads when they’re ready to buy, your sales team can boost conversion rates and achieve revenue goals.
- Shorten sales cycle. By knowing when to reach out to your best leads, your lead scoring efforts will allow you to close more deals faster.
- Break down sales and marketing silos. Having a lead scoring system in place assists your sales and marketing teams in deciding precisely how a lead handoff should happen.
- Easily identify the ROI of marketing campaigns. By learning what resonates more with your high-converting prospects, you’re equipped to create better content to convert more leads.
The reality is that no two leads are exactly the same. This is where lead scoring comes into play–it helps to surface differences, enabling sales reps to know who they’re communicating with and whether they should have that conversation at all.
Types of lead scoring
When using manual scoring, sales and marketing teams manually assign point values or scores to leads based on their potential to convert into customers. This method can be time-consuming, subjective, and prone to errors.
Predictive lead scoring, on the other hand, is a data-driven approach that applies big data and machine learning algorithms to scoring to find the right combination of behaviors and key attributes of existing and potential customers in real time. Then, these attributes are automatically matched and ranked to those of new leads.
AI & Lead scoring
Breadcrumbs leverages a machine learning-assisted approach for lead scoring, which combines the power of AI algorithms with human expertise. This unique approach enhances the accuracy and effectiveness of scoring by leveraging the insights and intuition of experienced sales professionals.
Whether you’re using manual or predictive scoring, there are different outputs you can get:
- Numeric. Your score is a number on a scale of 1-100, with higher numbers being better and lower numbers being worse. The score is usually a mix of firmographic data and engagement data. With a single numerical point value, it’s difficult to understand how many points are fit and how many are activity.
- Hot vs. Cold. Your score is represented by hot peppers or flames, whereas cold could be an icicle. The more hot items you have, the hotter your contact is, and vice versa. It usually only focuses on behavior and activity and doesn’t consider the fit of the contact.
- Co-dynamic. Your score is broken out into both a letter and a number and typically has 16 possible variations. The letter represents the fit of the contact and is articulated as either A, B, C, or D–where A is the highest fit, and D is the lowest. The number represents activity and is articulated as either 1, 2, 3, or 4–where 1 is the highest level of engagement and 4 is the lowest.
The co-dynamic approach to scoring
In order to have the most comprehensive lead scoring strategy, we recommend using predictive lead scoring with a co-dynamic approach.
Predictive scoring tools like Breadcrumbs also add recency and frequency data points into the lead score equation, which allows you to find qualified leads at the exact time they’re ready to purchase and results in converting leads at a faster rate.
Let’s see each of these factors and how lead scoring works when using a co-dynamic approach:
Fit and Activity
‘Fit‘ refers to how well a prospect matches your ideal customer profile (ICP). This is determined by explicit data gathered directly from the leads through lead generation or contact forms–demographic and firmographic information like job title, industry, or revenue.
For B2B software companies targeting mid-sized tech companies, a CTO from a tech company with 200 employees would be a strong fit. The closer a contact aligns with your buyer persona, the higher their fit score, indicating a higher likelihood of conversion based on their profile alone.
On the other hand, ‘Activity‘ is about what the contact does. It involves tracking and scoring a lead’s behavior or engagement with your company. This is determined by implicit data, which includes actions like email opens, website visits, content downloads, etc.
A contact who visits your pricing page often may receive a high engagement score, indicating buying intent. Likewise, a contact who downloads a whitepaper may receive a higher lead score compared to someone who only visits a blog post.
In an effective scoring model, ‘Fit’ and ‘Activity’ scores are combined to give a comprehensive view of a lead’s potential.
A tool like Breadcrumbs provides a clear breakdown of what fit and intent data was used to generate every score, offering deep insights into the quality of leads entering and progressing through your sales funnel.
Recency and Frequency
‘Recency‘ is based on recent engagement with your business. This could include visiting your website, clicking on email links, downloading resources, or any other interaction. The principle is simple: Contacts who have recently engaged are more likely to be interested in your products or services compared to those who haven’t engaged for a while.
A prospect who visited your pricing page yesterday would receive a higher score than someone who last visited a month ago. The more recent the activity, the higher the score, signifying increased interest and a greater chance of conversion.
‘Frequency‘ is about how often leads interact with your brand. It measures how many times a prospect engages with your business. Prospects who frequently visit your website, open your emails, or interact with your content are likely more interested than those who rarely engage.
A prospect who visits your blog every week and regularly downloads resources would be assigned a high score. This frequent engagement suggests a deep interest in your content and a higher potential for conversion.
A comprehensive lead scoring model should consider both recency and frequency. A prospect who recently and frequently engages with your brand is likely highly interested and potentially ready to make a purchase. By factoring in these aspects, businesses can prioritize their leads effectively, focusing on those most likely to convert.
‘Time Decay‘ in lead scoring refers to the decrease in the value of a lead’s action over time. The underlying principle is that the more recent an action, the more relevant it is, leading to higher scores. Conversely, as time passes without any new engagement from the lead, the score for past actions decreases.
If a lead visited your pricing page two weeks ago but hasn't engaged since, their interest in your product or service might be waning. Their score for that action would decay over time, indicating they may be less likely to convert than a more recent prospect.
Time Decay is useful for tracking and scoring a lead’s activity data. When combined with frequency and recency of engagement, it allows you to maintain accurate and up-to-date scores. This approach not only helps your marketing and sales teams identify the engaged leads but also enables you to spot prospects whose interest might be diminishing.
Implementing Time Decay in your lead scoring model involves regularly updating lead scores based on their ongoing activity. A scoring system like Breadcrumbs can automatically adjust scores over time, ensuring that your lead scoring models remain accurate and reflective of each lead’s current level of interest.
What are lead scoring criteria?
Lead scoring criteria are a set of customer data points that you use to create a lead scoring model and rank leads. This can include demographic, firmographics, activity, intent, and behavioral data.
While you may think you need many scoring criteria to create your model, the truth is that you can get started with just a few pieces of contact data that already exist in your database.
Demographic and firmographic data
Demographic data is information about a particular person, while firmographic data is information about a company or organization. Examples are:
- Job title. Job titles can be crucial to identify people within the company who may have the authority to push the opportunity and conversations forward.
- Industry. Industry helps with aligning your product and/or service fit to companies that have shown success.
- Company size/revenue. Company size and revenue can be used to assign scores to prospects.
Engagement and activity data
This data includes how engaged users are with your website or product and what activities they are doing that signal interest and buying intent (i.e., view pricing or product pages, open emails, and so on.) Examples are:
- Online and offline activities. Online and offline activities help to identify what someone is interested in (i.e., by visiting high-value pages.) You can also use this data to measure how engaged someone is on your website or app.
- Email opens and clicks. Email opens and clicks are a great way to measure someone’s interest in your products or services. You can use this data to assign scores based on the number of times an email is opened, as well as what links were clicked in each email.
- Downloads and registrations. When someone downloads a whitepaper or registers for an event, they’re engaging with your brand, so they should be given a higher score. This is especially true for customers who have actively opted in to receive your emails or notifications.
How to create a lead scoring model
The first step of creating a comprehensive scoring system is to define your point values and model criteria. The old-school way to do this is to work with your sales reps and marketing team to figure out which characteristics typically indicate a higher intent to purchase.
These indicators can be found in a variety of ways–by looking at the demographics and firmographics of your current customers, reviewing the online behavior of these contacts on your marketing analytics, conducting customer interviews with your existing customers, and looking at your best-closed deal(s).
This is time-consuming and difficult to implement. Using a data-driven automated approach, such as the one we have built Breadcrumbs on, will save you time and still give you all the information and control you need to make critical business decisions.
I can tell you from experience, you likely already have all the information you need to create a lead scoring model, but that information is scattered across multiple data sources. By unifying this data and applying machine learning algorithms, you can quickly build a scoring system that predicts customer intent and helps you make more efficient business decisions.
It’s easy, it’s quick, and you can start for free:
- Connect your data sources with Breadcrumbs
- Run a Reveal analysis to find the attributes and actions that indicate a higher intent to buy among your best customers
- Automatically build your scoring model with Copilot with those attributes and actions
Want to see Breadcrumbs in action? Book a 30-minute demo with a revenue expert.
The 15 golden rules to score leads
There are a few key points to ensuring your lead scoring models qualify leads efficiently and create accurate and actionable results.
5 rules for building your scoring model
- Have both a fit side and an activity side for your model. It matters not only who your prospects/customers are but also how they engage with your brand and product.
- 3-5 categories for both sides of the model is a good start. How do you know which ones to pick? Reveal will get you started by identifying the most impactful fields.
- Your fit categories should align with your ICP. Aim for a 60%+ hit rate: You can achieve it by looking for data in more than one field or having overlapping categories.
- Your activity categories should align with your customer journey and timing that predicts conversion. Pro-tip: Consider the length of your sales process and align to that.
- The sum of your categories and the “best match” in each of those categories must reach 100%. You can also go over 100% in some cases.
5 rules for implementing your scoring model
- A high-performing model generally has 5-15% of scored contacts in the top-left quadrant. You can easily adjust weighting through the slider bars for grades.
- Validate that the contacts in the top-left quadrant look like the ones you would like and expect to see in that group with Explore. If not, tweak the model accordingly.
- Monitor the model performance weekly to ensure that data flows properly, both for data ingestion and workflow triggers in your GTM stack.
- Share the model with your key GTM stakeholders. If they have different ideas they think should be tested, you can experiment in a few clicks and compare them against each other.
- Consider building different models for different objectives, products, motions, and/or geographies. The more specific you can be, the higher the performance you’ll see.
5 rules for measuring the impact of your scoring model
- Validate that different cohorts behave in a materially different way when it comes to conversion downstream. You want to see a linear decline in conversion from As to Ds.
- High-quality leads should increase over time. You can also throttle the volume of leads passed to sales based on strategic needs.
- Monitor velocity. Typically, in transactional sales, you want to see your time-to-close decrease. In consultative sales, the goal should be to have a better ClosedWon-to-ClosedLost ratio.
- Value-wise: the better the fit, the more likely you can sell at a higher price point. Once again, validate that different cohorts of contacts behave differently on this, too.
- A high-performing model will impact any of the above dimensions: value, velocity, volume, or conversion. The more dimensions impacted, the more lift compounds.
About the quality of your data
The quality of the data you input into your lead scoring models is crucial. High-quality data helps you find out where prospects are in the customer journey and let sales teams know if they are ready to buy and the right time for the lead handoff.
While there is no such thing as perfect data, here are some of the best practices to evaluate the quality of the data you’re providing your scoring models include:
- Cleaning Your Data. You don’t need flawless data; all you need to implement scoring is need clean, relevant, and up-to-date data. Use Breadcrumbs Reveal to surface the state of data infrastructure, identify gaps in the data collection strategy, and highlight correlations between contact fields and their likelihood of becoming paying customers.
- Standardizing Your Data Capture. After you capture lead information, we recommend a consistent system to collect data in your CRM integrations. It may be helpful to set up programs in your MAP to “standardize” the data sets, which helps to limit any manual data cleaning you may need to do when data quality issues arise through inconsistent data.
- Scoring What You Have Data On. It is often overlooked to check which fields you have data from, but it is essential to determine how you weigh your lead scores. You need to ensure that you have enough records that have the data you want to score against.
How many data points do you need?
You may be wondering if you have enough data. The reality is that all you need are 5 to 9 data points to build an excellent predictive scoring model.
One of the unthought-of things about this exercise is that you can frequently unearth activities and/or fit attributes on your lead that you didn’t know would have a significant impact on scoring!
What to do with lead scores?
The honest answer is it depends on what type of scoring approach you’re using. With a numerical output, where you assign points to specific attributes or actions, once your leads get to a point threshold (let’s say 50), they’re considered qualified and are sent over to the sales team.
The sales rep that gets a lead assigned, though, doesn’t know what made up the lead score (i.e., the lead interacts with your product vs. they’re from a company in your ICP) and can’t personalize their outreach.
If you’re using a co-dynamic approach, on the other hand, you have 16 different combinations of fit and activities (with A1 being the most qualified lead), and you’re able to use marketing automation to route each scored lead toward the right team or action based on the exact factors that made up their score.
Here’s an example when scoring leads for acquisition (the goal is to get them on a demo call) to see what I mean:
Scores: A1-2, B1-2, C1-2, D1-2
While you may be surprised to see D’s listed here, these contacts are highly engaged, and it’s not something to be taken lightly. You shouldn’t disqualify them entirely, even though they fall outside your ICP.
Direct these to your sales team with the highest priority—these leads are highly engaged and looking into your service right now.
The next segment can be a little trickier to tackle. These contacts aren’t completely interested, but they’re not ready to take the next step just yet. A good idea would be to get the contact to raise their hand and indicate if they’re ready for the next step.
Direct them towards content that’s more towards the bottom of the funnel—think case studies, competitor comparisons, etc. Getting these contacts to download or interact in any way with this type of content is a good sign that they’re getting closer to being ready to see your product in action.
At long last, we get to the bottom of the pack. While you may want to toss these leads and contacts altogether, fear not—these people just need a little more coaxing. The next set of tactics will focus on nurturing the contacts into the A3-D3 bracket.
If you have a BDR on your team, this is likely the type of contact they’d interact with first. Get a conversation going by reaching out and personalizing your messages to address their main pain points.
This approach can be used for different scoring goals (free trials, upsells, cross-sells, churn reduction, just to name a few.) What to see more? Let’s chat about it.
What is marketing to sales handoff?
Lead handoff is the process of handing over marketing leads to sales so that the sales team can nurture, qualify, and convert them into paying customers.
In order to optimize the marketing-to-sales handoff and ultimately increase conversions, you need to get clear on Sales and Marketing funnel terms, define handoff requirements, and analyze, review, and improve the process over time.
However, making sense of the data coming from different parts of your organization that often don’t communicate with each other (hello, silos!) is impossible to do at scale unless you use a tool that is able to connect the dots between all your data sources and give you actionable insights into revenue acceleration opportunities.
What is data enrichment?
Data enrichment works hand in hand with lead scoring by enriching existing customer scores with additional information that helps teams better target and personalize experiences for prospects. This data is used to review each lead’s profile and glean insights about their behavior, preferences, location, interests, and other factors that contribute to the decision-making process.
The key benefits of data enrichment are:
- Improved data quality and accuracy. Data decays relatively quickly (a Hubspot study found that email addresses decay at an average annual rate of 22.5% and phone numbers at 17%), which is why up-to-date, accurate data is so crucial.
- Optimized email marketing efforts. These include: reducing marketing costs by clearing inaccurate or false contacts, avoiding email blacklisting due to high bounce rates, as well as complying with GDPR, CASL, and CAN-SPAM.
What is contact scoring?
Contact scoring is a technique for ranking contacts in your database based on criteria such as company data and how recently they’ve engaged with you. It also helps you discover high-value prospects, re-engage clients you may cross-sell or upsell to, and gather inactive clients before they permanently go.
You can use contact scoring to:
- Score prospects (read: implement lead scoring).
- Upsell and cross-sell opportunities.
- Detect customers at risk of churning throughout the buyer’s journey.
The best contact scoring tools will look at an individual’s primary demographic and firmographic data ( job title, no. of employees, industry) and also factor in the following time-sensitive criteria, such as engagement data, recency and frequency of action, and time decay.
A contact-scoring software like Breadcrumbs leverages first-party and third-party data to crunch the numbers using machine learning to determine when you need to take action.
What’s the next step? Let’s talk about how to define your objectives and develop models that produce actionable scores you can use to drive processes and activities (i.e., in-app messages, email campaigns, sales outreaches), increase leads, and boost revenue acceleration.
Ready to accelerate your revenue?
Start closing better deals faster, expanding into your customer base and holding on to customers longer (we do retention too)!
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