Lead scoring

Last updated April 06, 2024

Lead scoring is a methodology used to rank prospects against a scale that represents the perceived value each lead represents to the organization.^[1] The resulting score is used to determine which leads a receiving function (e.g. sales, partners, teleprospecting) will engage, in order of priority.

Lead scoring models incorporate both explicit and implicit data. Explicit data is provided by or about the prospect, for example - company size, industry segment, job title or geographic location.^[2] Implicit scores are derived from monitoring prospect behavior; examples of these include Web-site visits, whitepaper downloads or e-mail opens and clicks.^[3]^[4] Additionally, social scores analyze a person's presence and activities on social networks.^[5]

Lead Scoring allows a business to customize a prospect's experience based on his or her buying stage and interest level and greatly improves the quality and "readiness" of leads that are delivered to sales organizations for followup.

Key Benefits

When a lead scoring model is effective, the key benefits are:

Increased sales efficiency and effectiveness: Lead scoring focuses sales attention on leads that the organization deems most valuable, ensuring that leads that are unqualified or have low perceived value are not sent to sales for engagement.
Increased marketing effectiveness: A lead scoring model quantifies for marketers what types of leads or lead characteristics matter most, which helps marketing more effectively target its inbound and outbound programs and deliver more high-quality leads to sales.
Tighter marketing and sales alignment: Lead scoring helps strengthen the relationship between marketing and sales by establishing a common language with which marketing and sales leaders can discuss the quality and quantity of leads generated.^[6]
Increase in Revenue: Lead scoring also ensures that sales goes first for leads that are qualified by their scores. The probability of a lead with higher scores closing is higher than one with a lower score. This indirectly contributes to a growth in revenue as well.

Lead Scoring Methodologies

Various lead scoring methodologies are employed:

Ideal Customer Profile (ICP): uses attributes of known contacts to decide to score (e.g. job title, company size) and allows an organization to focus their efforts on leads that represent their ideal customer. An example would include Hubspot's^[7] lead scoring system that bases lead scoring on the values of various fields within the CRM.
Lamb or Spam: most often employed by small businesses who do not have a clear ideal customer profile (ICP), the lamb or spam model consists of filtering out low-quality leads and surfacing high-potential leads. Low-quality leads are identified by online businesses by personal email address domains (gmail, hotmail, yahoo) or temporary email generators used to send email spam or sign up anonymously. High-quality leads are identified by their corporate email domains as well as firmographic data points such as job title and company size.^[8]
Rule-Based: these lead scoring models assign point values to a lead's firmographic & behavioral attributes. Point thresholds are set for a lead to be considered a good or bad fit.^[9] There are rule based scoring solutions built into larger marketing automation platforms, as well as add-ons which act as complements to CRM's such as lead scoring solutions for Salesforce CRM.^[10]
Predictive Lead Scoring: predictive lead scoring models use machine learning to generate a predictive model based on historical customer data augmented by third party data sources. The approach is to analyze past lead behavior, or past interactions between a company and leads, and find positive correlations of such data to a positive business outcome (for instance, a closed deal).

Businesses iterate on existing methodologies and change methodologies in an effort to better prioritize sales engagement. As businesses grow in headcount & the number of products they sell, predictive lead scoring methodologies are generally favored for their ability to ingest new customer data routinely and evolve its predictions.^[11]

Predictive Lead Scoring

With machine learning, lead scoring models have evolved to include components of predictive analytics, generating Predictive Lead Scoring models. Predictive Lead Scoring leverage first party data - such as internal marketing, sales & product data - as well as third party data - such as data enrichment & intent data - in order to build a machine learning model of the ideal customer profile. Predictive Lead Scoring models can also be used to identify, qualify & engage product-qualified leads based identifying statistically differentiating elements in historical user behavior which best predicts whether a user will spend above a certain threshold.^[12]

Predictive Lead Scoring is particularly beneficial for SaaS businesses, which have a high Customer lifetime value & a plethora of customer data. Predictive lead scoring models enable businesses to identify high-value prospects early in the buyer journey, creating a FastLane experience for prospects predicted to be a good firmographic & behavioral fit.

The success of Predictive Lead Scoring models is measured by their ability to identify a subset of prospective buyers who will account for a significant portion of sales opportunities. This is expressed in the following way:

X% of leads represent Y% of conversions

Optimal performance of a predictive lead scoring model sees X approaching 0, Y approaching 100 & conversions defined as a bottom-of-funnel metric such as opportunity created or opportunity won.

Related Research Articles

Customer relationship management (CRM) is a process in which a business or other organization administers its interactions with customers, typically using data analysis to study large amounts of information.

Personalized marketing, also known as one-to-one marketing or individual marketing, is a marketing strategy by which companies leverage data analysis and digital technology to deliver individualized messages and product offerings to current or prospective customers. Advancements in data collection methods, analytics, digital electronics, and digital economics, have enabled marketers to deploy more effective real-time and prolonged customer experience personalization tactics.

Database marketing is a form of direct marketing that uses databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing.

Email marketing is the act of sending a commercial message, typically to a group of people, using email. In its broadest sense, every email sent to a potential or current customer could be considered email marketing. It involves using email to send advertisements, request business, or solicit sales or donations. Email marketing strategies commonly seek to achieve one or more of three primary objectives: build loyalty, trust, or brand awareness. The term usually refers to sending email messages with the purpose of enhancing a merchant's relationship with current or previous customers, encouraging customer loyalty and repeat business, acquiring new customers or convincing current customers to purchase something immediately, and sharing third-party ads.

In marketing, lead generation is the process of creating consumer interest or inquiry into the products or services of a business. A lead is the contact information and, in some cases, demographic information of a customer who is interested in a specific product or service.

Lead management is a set of methodologies, systems, and practices designed to generate new potential business clientele, generally operated through a variety of marketing campaigns or programs. Lead management facilitates a business's connection between its outgoing consumer advertising and the responses to that advertising. These processes are designed for business-to-business and direct-to-consumer strategies. Lead management is in many cases a precursor to sales management, customer relationship management and customer experience management. This critical connectivity facilitates business profitability through the acquisition of new customers, selling to existing customers, and creating a market brand. This process has also accurately been referred to as customer acquisition management.

Customer analytics is a process by which data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics. This information is used by businesses for direct marketing, site selection, and customer relationship management. Marketing provides services in order to satisfy customers. With that in mind, the productive system is considered from its beginning at the production level, to the end of the cycle at the consumer. Customer analytics plays an important role in the prediction of customer behavior.

Oracle CRM is a customer relationship management system created by Oracle Corporation. It includes a number of different cloud applications that can be deployed together or used individually to analyze customer data and help companies connect and manage sales, marketing, and customer support.

HubSpot, Inc. is an American developer and marketer of software products for inbound marketing, sales, and customer service. HubSpot was founded by Brian Halligan and Dharmesh Shah in 2006.

A contact manager is a software program that enables users to easily store and find contact information, such as names, addresses, and telephone numbers. They are contact-centric databases that provide a fully integrated approach to tracking all information and communication activities linked to contacts. Simple ones for personal use are included in most smartphones. The main reference standard for contact data and metadata, semantic and interchange, is the vCard.

Salesforce Marketing Cloud is a provider of digital marketing automation and analytics software and services. It was founded in 2000 under the name ExactTarget. The company filed for an IPO in 2007, but withdrew its filing two years later and raised $145 million in funding. It acquired CoTweet, Pardot, iGoDigital and Keymail Marketing. In 2012, it raised $161.5 million in an initial public offering, before being acquired by Salesforce for $2.5 billion in 2013. ExactTarget was renamed Salesforce Marketing Cloud in 2014 after its acquisition by Salesforce.

The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.

Healthcare CRM, also known as Healthcare Relationship Management, is a broadly used term for a Customer relationship management system, or CRM, used in healthcare.

Optimove is a privately held company that develops and markets a Relationship Marketing software as a service (SaaS). Optimove's product has a Customer Data Platform at its core and applies algorithmic optimization to autonomously improve multichannel campaigns. The company serves various industries, including retail, eCommerce, travel and hospitality, gaming, and financial services.

Marketing automation refers to software platforms and technologies designed for marketing departments and organizations automate repetitive tasks and consolidate multi-channel interactions, tracking and web analytics, lead scoring, campaign management and reporting into one system. It often integrates with customer relationship management (CRM) and customer data platform (CDP) software.

Social selling is the process of developing relationships as part of the sales process. Today this often takes place via social networks such as LinkedIn, Twitter, Facebook, and Pinterest, but can take place either online or offline. Examples of social selling techniques include sharing relevant content, interacting directly with potential buyers and customers, personal branding, and social listening. Social Selling is gaining popularity in a variety of industries, though it is used primarily for B2B (business-to-business) selling or highly considered consumer purchases. C2C companies have been using social selling techniques since far before the Internet existed. B2B and B2C companies are now adopting many of those techniques as they are translated to social media platforms.

Lead validation is the process by which sales leads generated by internet marketing campaigns are separated from other types of conversions. Lead validation is crucial for effective internet marketing management; without it, companies can neither accurately evaluate the results of, nor efficiently improve, their SEO, PPC, display advertising, email, content marketing and social media campaigns.

Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences.

Conversica is a US-based cloud software technology company, headquartered in San Mateo, California, that provides two-way AI-driven conversational software and a suite of Intelligent Virtual Assistants for businesses to engage customers via email, chat and SMS.

Oracle Advertising and Customer Experience (CX) is a suite of cloud-based applications offered by Oracle Corporation that includes tools for advertising, marketing, sales, e-commerce, customer service.

References

↑ "What is Lead Scoring?". TechTarget.
↑ "Introduction to Lead Scoring". act-on.
↑ DM News, “Lead Scoring by the Numbers”
↑ "A 10 Step Framework to Accurate Lead Scores". CRM Search. 30 March 2023.
↑ "A Quick How to Guide on Social Media Lead Scoring". TargetMarketing.
↑ "Basic Idea of Lead Scoring". Marketing Automation Insider. 22 June 2015.
↑ "Hacking Hubspot's Lead Scoring System". Matthew Deal. 2020-12-09. Retrieved 2019-03-01.
↑ Boogar, Liam (2019-01-29). "The Three Stages of Lead Scoring: Lambs, Ducks & Kudus". All others bring data. Retrieved 2019-03-01.
↑ "Predictive Lead Scoring: Why, How & Where". DigiTech-Media. 2023-12-01. Retrieved 2023-12-01.
↑ "Lead scoring solution for Salesforce Sales Cloud". SalesWings. 2019-10-10. Retrieved 2019-10-11.
↑ Boogar, Liam (2019-01-29). "The Three Stages of Lead Scoring: Lambs, Ducks & Kudus". All others bring data. Retrieved 2019-03-01.
↑ Brero, Francis. "Beyond MQLs & SQLs: How to Use Your Product to Qualify Leads". www.appcues.com. Retrieved 2019-03-01.

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.

[1] "What is Lead Scoring?". TechTarget.

[2] "Introduction to Lead Scoring". act-on.

[3] DM News, “Lead Scoring by the Numbers”

[4] "A 10 Step Framework to Accurate Lead Scores". CRM Search. 30 March 2023.

[5] "A Quick How to Guide on Social Media Lead Scoring". TargetMarketing.

[6] "Basic Idea of Lead Scoring". Marketing Automation Insider. 22 June 2015.

[7] "Hacking Hubspot's Lead Scoring System". Matthew Deal. 2020-12-09. Retrieved 2019-03-01.

[8] Boogar, Liam (2019-01-29). "The Three Stages of Lead Scoring: Lambs, Ducks & Kudus". All others bring data. Retrieved 2019-03-01.

[9] "Predictive Lead Scoring: Why, How & Where". DigiTech-Media. 2023-12-01. Retrieved 2023-12-01.

[10] "Lead scoring solution for Salesforce Sales Cloud". SalesWings. 2019-10-10. Retrieved 2019-10-11.

[11] Boogar, Liam (2019-01-29). "The Three Stages of Lead Scoring: Lambs, Ducks & Kudus". All others bring data. Retrieved 2019-03-01.

[12] Brero, Francis. "Beyond MQLs & SQLs: How to Use Your Product to Qualify Leads". www.appcues.com. Retrieved 2019-03-01.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]