In this article, we take an in-depth look at what is lead scoring, how to determine lead score, and why this data is so important in the relay race involving your marketing and sales teams.
We’ll also show you how to automate the lead scoring process, so that the response to leads can be prioritized by your sales team based on propensity to purchase.
What is lead scoring?
In the context of sales, lead scoring refers to the process of enhancing or improving the quality of a sales lead. This can involve adding new information to the lead, such as contact details, or verifying and updating existing information.
The goal of lead scoring is to make sure that the leads passed on to sales teams are as accurate and up-to-date as possible, increasing the likelihood of converting them into customers.
Lead scoring factors to consider.
There are many factors that can be considered when determining a lead score, which can vary depending on the company and industry. Here are some common factors that may be used in lead scoring:
Demographic information: job title, company size, industry, location.
Behavioral information: website activity, email engagement, social media engagement, content downloads.
Firmographic information: company revenue, employee count, industry verticals, technographics
Engagement history: how many times the lead interacted with the brand, forms submitted, landing pages visited.
Sales readiness: if the lead shows clear signs of readiness to buy.
Score Decay: if the lead has not been active for a specific period, lead score will decrease.
The weight assigned to each factor and the numerical values used to represent different levels of engagement or fit can be adjusted to suit the specific needs of the company. It is also important to note that a lead score is not a definite indication of the probability of converting a lead into a customer, it's a representation of the current state of the lead, and it should be considered as a tool to prioritize leads and guide the sales team on the next steps.
Lead scoring best practices.
Define your buyer personas: Having a clear understanding of your target audience will help you identify the most important criteria for scoring leads.
Identify key criteria: Consider factors such as job title, company size, industry, budget, and level of engagement with your company to determine what makes a lead more valuable.
Use a consistent scoring system: Use a standardized method for assigning scores to leads based on the criteria you have identified. This will ensure that all leads are scored objectively and consistently.
Regularly review and adjust your scoring system: Your lead scoring system should be reviewed and updated regularly to ensure that it remains relevant and accurate. This will help you avoid missing out on valuable leads.
Integrate lead scoring with your marketing automation platform: Your lead scoring system should be integrated with your marketing automation platform to ensure that the scores are updated in real-time and that leads are automatically prioritized based on their scores.
Consider both explicit and implicit data: Lead scoring should consider both explicit data, such as job title and company size, as well as implicit data, such as website activity and email engagement, to give a more complete picture of each lead's potential.
Collaborate with sales: Lead scoring should be a collaborative effort between marketing and sales. The sales team can provide valuable insights into what makes a lead more likely to convert, which can inform the criteria used for scoring
By following these best practices, you can create a lead scoring system that is effective, efficient, and helps you prioritize your sales efforts.
Predictive lead scoring model.
Predictive lead scoring is a method of assigning a score to leads based on their likelihood of becoming customers. It uses statistical models and machine learning algorithms to analyze data from various sources, such as website activity, engagement with marketing materials, and demographic information, to predict which leads are most likely to make a purchase.
The goal of predictive lead scoring is to identify the leads that are most likely to convert, so that sales and marketing teams can prioritize their efforts and allocate resources more effectively.
Here are some benefits of using predictive lead scoring:
Increased efficiency: Predictive lead scoring helps sales and marketing teams prioritize their efforts by identifying the leads that are most likely to convert. This allows teams to focus their time and resources on the most promising leads, increasing the efficiency of their efforts.
Improved lead quality: Predictive lead scoring can help improve the quality of leads by identifying those that are most likely to convert, rather than simply those that fit certain demographic criteria.
Data-driven decision making: Predictive lead scoring relies on data and machine learning algorithms, providing a data-driven approach to lead scoring that eliminates subjectivity and bias.
Increased ROI: By prioritizing the most promising leads, predictive lead scoring can help increase the return on investment (ROI) of sales and marketing efforts.
To get the most out of predictive lead scoring, it is important to use a robust and reliable machine learning platform that is capable of processing large amounts of data and providing accurate predictions.
Additionally, it is important to regularly review and adjust your predictive models to ensure that they remain relevant and accurate. By using predictive lead scoring, you can improve the efficiency and effectiveness of your sales and marketing efforts, and increase the ROI of your marketing campaigns.
How to develop your lead scoring algorithm.
A lead scoring algorithm is a mathematical model used to assign a score to leads based on their likelihood of becoming customers. The score is calculated using a set of weighted criteria, such as demographic information, website activity, and engagement with marketing materials.
The purpose of a lead scoring algorithm is to help sales and marketing teams prioritize their efforts by identifying the leads that are most likely to convert.
There are several different approaches to developing a lead scoring algorithm, including:
Rule-based scoring: A rule-based scoring algorithm assigns scores to leads based on predefined criteria, such as job title, company size, or engagement with marketing materials.
Statistical scoring: A statistical scoring algorithm uses statistical models to analyze data from multiple sources, such as website activity, demographic information, and engagement with marketing materials, to determine the likelihood of conversion.
Machine learning-based scoring: A machine learning-based scoring algorithm uses machine learning algorithms to analyze data and make predictions about which leads are most likely to convert.
Automated lead scoring system.
Automating lead scoring involves setting up a system that can assign scores to leads based on predefined criteria and rules. Here are some steps you can take to automate lead scoring:
Set up a lead scoring system: There are various software solutions available, such as marketing automation or customer relationship management (CRM) systems, that can help you automate lead scoring.
Create scoring criteria: Create a set of rules or criteria that determine how leads will be scored based on their engagement and fit with your company. For example, a lead who has completed a form on your website or downloaded a whitepaper might be assigned a higher score than a lead who has only visited your website.
Assign scores to leads: Once the system is in place, leads will automatically be assigned scores based on the criteria you've established. You can set up alerts to notify your sales team when a lead reaches a certain score threshold.
Monitor and adjust the system: Over time, you'll want to monitor the performance of your lead scoring system and adjust the criteria and scoring values as needed to ensure that it remains effective.
Integrate your lead scoring platform with your CRM: To make the most of the enriched leads, they should be integrated with your CRM, so the sales team can take the additional qualifying data into account when prioritizing and following up on leads.
It is also important to note that lead scoring should be a continuous process, and should be adjusted based on the results and data from your sales and marketing activities.
Lead scoring software and tools.
There are many different lead scoring software and tools available that can help automate the process. Some popular lead scoring software include:
Marketing automation platforms: These platforms can track and enrich leads based on lead qualification, website activity, email engagement, and other behaviors. Examples include Lucep, Marketo, Eloqua, Pardot, and Hubspot.
CRM systems: Many CRM systems, such as Salesforce and Dynamics, include lead scoring functionality that can help you prioritize leads based on their engagement and fit with your company.
AI-driven lead scoring: There are some start-ups and providers that use artificial intelligence to help score leads, analyzing big data from various sources, including social media, company's website, CRM, and other platforms.
Lead scoring software: There are standalone software tools, such as LeadScore that are specifically designed for lead scoring.
Spreadsheet-based lead scoring: Some companies may prefer to use simple spreadsheets or google sheets to keep track of the lead data and update each lead manually with additional qualifying data.
Each of these tools has its own capabilities and limitations and It's important to select the one that best fits your company's needs and budget, and can integrate with other tools you already use.