Lead scoring

What is lead scoring?

Lead scoring is a method used in marketing and sales to assess and prioritize potential customers (leads) based on their likely interest and willingness to buy. The purpose of lead scoring is to identify the most qualified and promising leads in order to optimize marketing and sales efforts and maximize the conversion of leads into customers.

In lead scoring, each lead is assigned a score based on various criteria and behaviors that indicate interest and purchase intent. These criteria can include demographic data, website interactions, email open and click rates, content downloads, social media engagement, and other relevant activities. By assigning scores based on these criteria, one can rank the leads according to their relative quality and prioritize those considered most likely to convert into paying customers.

Lead scoring can be carried out using marketing automation systems and customer relationship management (CRM) systems. The results can then be used to customize marketing and sales activities. For example, the highest scoring leads can be forwarded to the sales department for personalized follow-up, while those with lower scores can continue to be processed with automated marketing campaigns.

By using lead scoring, companies can streamline their sales process and focus their resources on the most promising and qualified leads. It also helps to increase collaboration between marketing and sales by giving both departments a clearer understanding of which leads should be prioritized and which require further processing to convert into customers.

What can a lead scoring model look like?

A lead scoring model can look different depending on the company's needs and the specific criteria deemed relevant for assessing a lead's quality and willingness to buy. Here are some common components and steps that can be included in a lead scoring model:

Identification of relevant criteria: First of all, you need to identify the factors and behaviors that are considered important to assess the quality of the lead. This can include demographic data (e.g. industry, company size), behaviors (e.g. website visits, content downloads), interactions (e.g. email opens, clicks) and other engagement indicators (e.g. social media, webinar participation). These criteria should be relevant to your company's products or services and the buying process.

Weighting and scoring: Each criterion is assigned a weighting based on its importance in indicating a high-quality lead. For example, downloads of the company's demo report may be more significant than visiting the home page of the website. A scoring system is used to assign points to each criterion based on its relevance and importance. The scores can be absolute or relative, depending on how important the criteria are in relation to each other.

Scale and thresholds: A scale is established to define different levels of lead quality. For example, the scale can be 1-100, with higher scores indicating a higher quality of the lead. A threshold is defined to determine when a lead is considered qualified enough to be transferred to the sales department or for further processing.

Data modeling and algorithms: Depending on the amount of data and the complexity of your lead scoring model, you can use different algorithms to analyze and calculate the lead score. The larger and market-leading system providers offer an AI solution where the scoring model is based on previous leads converted into customers. The AI is also continuously updated and thus always stays current with what criteria are currently most important. 

Validation and adjustment: After the model has been created (if it is static and not AI), it is important to validate its performance by using historical data and comparing the assigned scores with actual conversion results. The model can be adjusted based on the results of the validation to improve its efficiency and accuracy.

What are the pitfalls of lead scoring?

While lead scoring can be a powerful method for prioritizing leads and optimizing marketing and sales efforts, there are some potential pitfalls to be aware of:

Subjective assessments: If the criteria and weights used in the lead scoring model are subjective or not sufficiently based on data and facts, the assessment of the quality of the leads can be distorted. It is important to have a clear and objective process for establishing relevant criteria and weighting them correctly.

Incomplete or inaccurate data: If the lead scoring model is based on incomplete or inaccurate data, it can lead to incorrect assessments of the quality of the leads. It is important to have accurate and up-to-date data sources and to regularly clean and update data to avoid basing assessments on inaccurate information.

Static model: A static lead scoring model may be limited in its ability to adapt to changing market conditions and buying behavior. Customer behaviors and preferences can change over time, and it is important to regularly evaluate and adjust the lead scoring model to ensure it remains effective and current.

Ignoring qualitative data: Lead scoring often tends to focus on quantitative data such as clicks and downloads, but it can be important to also include qualitative data to get a more holistic assessment of the lead's interest and engagement. For example, feedback from the sales team or other interactions that cannot be measured quantitatively can provide valuable insights.

Neglecting human judgment: Lead scoring should be a combination of automated algorithms and human judgment. Relying entirely on an automated model can lead to important signals and context being overlooked. Human insight and judgment can add a deeper understanding and can complement the automated assessments.

It is important to be aware of these pitfalls and to continuously monitor and improve the lead scoring model to ensure its effectiveness and reliability.

Examples of tools in which you can use lead scoring:

Salesforce: Salesforce is one of the best-known CRM platforms and offers built-in lead scoring capabilities. You can create custom fields, scoring rules, and automated workflows to assess and rank leads based on their activities and characteristics. Salesforce also offers a solution for scoring leads with AI, called Einstein Lead Scoring. 

HubSpot: HubSpot is a comprehensive marketing and sales platform that includes lead scoring tools. You can use HubSpot's lead scoring features to assign scores to leads based on behaviors, interactions, and characteristics, as well as create segmentation and automated workflows based on the score results.

Marketo: Marketo is another leading marketing and automation platform that offers lead scoring capabilities. With Marketo, you can define scoring rules and assess leads based on their engagement and behaviors, then use the results to prioritize leads and customize marketing campaigns.

SharpSpring: SharpSpring is a comprehensive marketing automation and CRM platform that includes lead scoring features. You can set score values and rules to assess leads based on interactions, behaviors, and other relevant factors. The results can be used to generate automated sales follow-ups and prioritize leads.

These are just a few examples of systems and tools that offer lead scoring features. There are also other CRM and marketing automation systems that can be customized to suit your specific needs and business requirements.