Lead scoring is the method of assigning points to contacts or potential prospects based on how closely they resemble your buyer persona. 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 shortening the sales cycle.
By implementing a lead scoring system, your sales team can de-prioritize low-quality leads and prioritize leads with the highest chance of converting. This, in turn, can help to align sales and marketing efforts in a more measurable way.
Lead scoring can be done through lead 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. It uses big data and machine learning algorithms 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 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 database typically see a higher conversion rate, shorter sales cycle, 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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- Why Is Lead Scoring Important?
- What Is Predictive Lead Scoring?
- What Are Lead Scoring Models, And How Are Leads Scored?
- What Should Be The Lead Scoring Criteria For Lead Scoring Models?
- What Are Lead Scoring Best Practices?
- How Do You Build An Effective Lead Scoring Process?
- What Is Lead Handoff?
- What Is Data Quality For Lead Scoring?
- What Is Data Enrichment?
- What Is Contact Scoring?
Why Is Lead Scoring Important?
Lead scoring is not just beneficial to your sales team. Instead, it is an essential process for any sales and marketing teams in that it can streamline the way that marketing pre-qualifies leads and make your 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 your conversion rate. By finding more qualified leads the moment they’re ready to buy, your sales team will have a better chance of increasing your conversion rate and meeting your revenue goals.
- Shorten your sales cycle. By knowing when to reach out to your best leads, your 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 team in deciding precisely who, when, and how a lead handoff should happen.
- Easily identify the ROI of marketing campaigns. Learning what resonates more with your high-converting prospects ensures you’re equipped to create better content to convert more leads.
All in all, no two leads are precisely the same (even if they may look identical). This is where lead scoring comes into play–it helps to isolate and promote these differences so a salesperson better understands who they will be communicating with and whether they should have that conversation.
What Is Predictive Lead Scoring?
Predictive lead scoring is a data-driven approach that applies big data and machine learning algorithms to lead scoring to find the right combination of behaviors and critical attributes of existing and potential customers. Then, these attributes are automatically matched and ranked to those of new leads.
One of the benefits of using a predictive model is that you’ll be able to scale your lead scoring efforts quickly and, at the same time, increase lead quality by leveraging all the data you have available in your CRM, marketing tools, and product analytics tools.
Also, predictive lead scoring works in real-time: if your data changes, so do the predictions. This means that you’ll be able to create effective marketing campaigns based on the data you have in your database right now.
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 significantly impact scoring!
What Are Lead Scoring Models, And How Are Leads Scored?
A scoring model is a scale you use for scoring leads and works by ranking your leads based on specific criteria you set. A higher lead score means the contact is more likely to be a better fit for your offering than a contact with a low score.
Scoring criteria can include firmographic data (company size and revenue), demographic data (job title, location), and offline or online activity (website activity, product usage, demos, etc.) Contact information can be gathered by lead generation forms, data enrichment services, or imported from product data.
These contacts are then ranked using one of three traditional lead scoring model types.
- Lead Scoring Model #1–Numeric Lead Score Output. Your score output is a number, usually on a scale of 1-100. The higher the number, the higher your score. Conversely, the lower the number, the lower the score. The score is usually a mix of fit (demographics and firmographics) and activity.
- Lead Scoring Model #2–Hot Vs. Cold. Your score output is usually 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. This type of scoring will only focus on behavior and activity and doesn’t consider the fit of the contact.
- Lead Scoring Model #3–Co-Dynamic Lead Scoring. Your score is broken 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, 4–where 1 is the highest level of engagement and 4 is the lowest.
To have the most comprehensive lead scoring strategy, we recommend using the co-dynamic lead scoring model–however predictive lead scoring tools can sometimes end up as black boxes where you cannot control your scoring models.
Tools like Breadcrumbs take a hybrid approach by combining the power of AI and allowing you to manipulate the models in real-time as your business changes priorities and goals.
What Should Be The Lead Scoring Criteria For Lead Scoring Models?
Lead scoring criteria is a set of data points you use to create a lead scoring model and rank leads. This can include demographic, firmographic, activity, and behavioral data.
While you may think you need many lead 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.
There are two categories of lead scoring criteria that you can use to create a lead scoring model:
Demographic Or Firmographic Data
Demographic data is information about a particular person (usually in place for B2C scoring models), while firmographic data is information about a company or organization (used mainly in B2B.)
- Job title, aka the who. Job titles can be crucial to identify people within the company that may have the authority to push the opportunity and conversations forward.
- Industry, aka your ICP. Industry helps align your product and/or service fit to companies that have shown success.
Engagement Or 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.)
- Recency, aka the when. Recency will tell you how recently your prospects have interacted with your company.
- Frequency, aka how often. Frequency will count how many times leads raise their hand at what you are proposing to them.
- Online and offline activities, aka the what. Both online and offline activities help to identify what someone is interested in (i.e., by visiting high-value pages) and how they like to consume their information.
What Are Lead Scoring Best Practices?
There are a few key points to ensuring your lead scoring model creates accurate (and actionable results.
- Create an informed (and accurate) lead scoring model. When creating a scoring model, many overlook the most fundamental rule of the process–prioritizing the criteria that really matter. Creating a buyer persona that fully encompasses your ideal customer will be the key to success. Using existing customer data and information from a successfully closed deal can help you qualify leads more tactically.
- Create models with a fit and engagement score. Strong lead scoring models take the demographics/firmographics into consideration and the activity and frequency of a contact interaction. Someone who responds to emails promptly and has frequently visited your pricing page in the past few days will have a higher likelihood of purchasing from you than someone who converted on a lead magnet but hasn’t opened any other messages.
- Automate your process. While spreadsheets are a necessary business tool, creating scoring models in them is far from ideal and can lead to lackluster results (and expensive mistakes). Spreadsheets need to be updated manually, and working with a large database of leads means that your scoring model will never be fully accurate or up to date. Investing in a system built specifically to score leads in a timely manner can make or break your lead scoring process.
How Do You Build An Effective Lead Scoring Process?
The first step in creating a comprehensive lead scoring system is to define your point values and model criteria. The easiest way to do this is to work with your sales reps and marketing team to determine which characteristics typically indicate a higher intent to purchase.
These indicators can be found in various ways–by looking at the demographics and firmographics of your current customers, reviewing the online behavior of these contacts, and conducting customer interviews with your existing customers.
After you’ve created your scoring model and assigned point values, you can then move on to ranking the leads in your database. Many marketing automation tools have scoring systems built into their product. Still, these tools are usually pre-built and don’t offer the flexibility of a co-dynamic scoring method we mentioned above.
After you have your lead scores, you can apply them (manually or otherwise) to the leads in your database.
What Is Lead Handoff?
Lead handoff is the process of handing over leads generated by marketing to sales so that the sales team can nurture, qualify and convert them into paying customers.
It’s crucial that sales, marketing, and customer support are fully aligned in the lead handoff process; any disconnect between targeting, marketing, and sales qualification, will compromise the entire value chain.
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.
To understand what should move qualified leads from one stage to the next, you need to understand which customers add the most value (=revenue) to your business over the long term.
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 lead scoring tool that can connect the dots between all your data sources and give you actionable insights into revenue acceleration opportunities.
What Is Data Quality For Lead Scoring?
Data quality for lead scoring involves ensuring the data you input into your lead scoring models is clean, standardized, and scorable contributing to more effective lead scoring models and a smoother lead handoff process.
In the context of lead scoring, the quality of the data you input into your scoring models is crucial. High-quality data helps you find out where prospects are in the customer journey and let the sales team know if they are ready to buy and the right time for the lead handoff.
Some of the best practices to evaluate the quality of the data you’re providing your lead scoring models include:
- Cleaning your data. While there is no such thing as perfect data, all you need to implement lead scoring is clean, relevant, and up-to-date data. A tool like Breadcrumbs Reveal dives deep into your CRM or marketing automation software 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’d 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 must ensure that you have enough records with the data you want to score against.
What Is Data Enrichment?
With data enrichment, companies gather information about their customers and leads by matching the data in their database with that from third parties. A data enrichment process produces updated, enriched data about contacts with relevant information.
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%, 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, and complying with GDPR, CASL, and CAN-SPAM.
Data enrichment tools draw data from various sources, including third-party sources, into a single stream. This provides a direct and detailed analysis, data that would not have been obtained from primary sources alone.
Some popular tools include Clearbit, InsideView, and Zoominfo. You can check our review on data enrichment tools here.
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 leads (read: 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 data ( job title, no. of employees, industry) and also factor in the following time-sensitive scoring criteria:
- Engagement data is the types of interactions a contact has had with your business.
- Recency data tells you how recently a contact took a specific action.
- Frequency data refers to how often an individual contact engages with your business.
- Monetary data reveals how much a particular contact spends as a customer.
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.
We’ll combine data from all of your touchpoints (your CRM, marketing automation tools, and existing product usage tools) to give you a comprehensive view of what activities and behaviors are driving behavior.
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).
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Start closing better deals faster, expanding into your customer base and holding on to customers longer (we do retention too)!