How to do Lead Scoring & Lead Qualification

Stop chasing unqualified leads! Score & qualify leads to convert prospects faster.

Lead scoring and qualification are crucial components of a successful sales and marketing strategy.

They help you and your organization prioritize efforts, allocate resources efficiently, and focus on leads that are more likely to convert into customers.

Getting started with Lead Scoring and Qualification

Clearly define your ICPs and Personas

Before you start prospecting, you need to know who you are looking for.

At the macro level, your ICP is a description of the type of company or account that is most likely to buy from you, based on factors such as industry, size, location, budget, needs, and goals.

By creating an ICP, you can narrow down your target market and focus on the most relevant and qualified prospects.

At the micro level, your personas are detailed representations of the specific individuals within the target companies who are key decision-makers or influencers in the purchasing process.

Assign Lead Scores based on Profile and Behaviors

A lead scoring system is a way of assigning numerical values to leads based on their level of interest and fit for your solution.

It helps you prioritize and segment your leads based on their readiness to buy and their potential value.

You can use different criteria to score your leads, such as demographic data, behavioral data, engagement data, and firmographic data.

For example, you can assign higher scores to leads who have visited your pricing page, downloaded your ebook, or requested a demo.

Traditional vs Predictive Lead Scoring

Traditional lead scoring and predictive lead scoring are both methods used in sales and marketing to evaluate and prioritize leads based on their likelihood to convert into customers. However, they differ in their approaches and the factors they consider.

  1. Traditional Lead Scoring:
    • Rule-Based Approach: Traditional lead scoring relies on predetermined rules and criteria set by your sales and marketing team. These rules are often based on historical data and the subjective judgment of your teams.
    • Static and Limited: The criteria used in traditional lead scoring tend to be static and may not adapt well to changes in the market or customer behavior. It typically involves a set list of attributes, such as company size, industry, and engagement level.
    • Manual Adjustment: It often requires manual adjustments and updates as the organization gathers more data and insights. This can be time-consuming and may not capture real-time changes effectively.
    • Relies on Explicit Data: Traditional lead scoring primarily relies on explicit data provided by leads, such as form submissions, website visits, and email interactions.
  2. Predictive Lead Scoring:
    • Data-Driven and Machine Learning: Predictive lead scoring leverages advanced analytics and machine learning algorithms to analyze vast amounts of data. It considers both explicit and implicit data, including historical customer behavior and external data sources.
    • Dynamic and Adaptive: Predictive lead scoring models are dynamic and can adapt to changing market conditions and evolving customer behavior. They continuously learn and improve over time as more data becomes available.
    • Automated Insights: It automates the process of identifying patterns and predicting lead behavior, reducing the need for manual intervention. This results in more accurate and timely lead prioritization.
    • Incorporates Behavioral Signals: Predictive lead scoring considers not only demographic and firmographic data but also behavioral signals, such as social media activity, content consumption, and online behavior.

How to do Traditional Lead Scoring

Define your Lead Qualification Criteria by:

  • Reviewing your lifecycle stage definitions in your CRM, or buyer personas. This is based on intuitive data.
  • Talk to your customer-facing teams (Sales & CS teams). This is based on qualitative data.
  • Speak with customers directly. This is qualitative data.
  • Review your analytics. You will get both quantitative and qualitative data, depending on the measurement tool.

Data types / attributes to focus on:

  • Demographic traits
    • Location
    • Company size
    • Industry
    • Job title
  • Online behavior
    • Website visits
    • Email engagement
    • Form submissions
    • Event attendance

For lead scoring, you need 3 fields; Data type / attribute, Criteria (qualification criteria), Score (or weight)

Implement attributes and values into HubSpot score property (qualifies leads based on custom criteria).

Determine the score threshold for a Sales Qualified Lead. e.g. 75 in a score scale of 0-100.

Create an active list with this lead scoring system (and qualification criteria). e.g. SQLs w/ HubSpot score 75+

Predictive Lead Scoring

Traditional lead scoring methods often rely on predefined criteria and manual assessments, leading to inefficiencies and subjectivity.

Predictive Lead Scoring, on the other hand, leverages machine learning algorithms to analyze vast datasets and identify patterns that correlate with successful conversions.

By considering a multitude of factors, both explicit and implicit, this approach goes beyond basic lead scoring, providing a more nuanced and accurate prediction of a lead's potential.

In summary, this is what predictive lead scoring is about:

  • Uses machine learning to read data and identify the best leads.
  • Determines commonalities between both existing customers, and leads who don’t close.
  • Develops a formula to sort contacts based on their probability of becoming customers within a given time frame. Uses a Likelihood to Close property to assign lead scores.

Now after assigning qualification scores to your leads, what next? Well yes, you guessed right. Send them to the right reps. This is lead routing.

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