Overview

Lead scoring is a vital tool in modern sales and marketing, providing a method to rank prospects based on their potential value to the business. This feature allows sales teams to prioritise leads, ensuring that they focus their efforts on the most promising opportunities. Property Shell's lead scoring capabilities can significantly enhance your sales process by systematically evaluating and categorising leads.

Benefits of Lead Scoring

1. Improved Efficiency: Focus on leads that are more likely to convert, reducing time spent on less promising prospects.
2. Better Sales Outcomes: Increase conversion rates by targeting high-potential leads with tailored marketing efforts.
3. Data-Driven Decisions: Use objective criteria to evaluate leads, minimizing guesswork and bias in the sales process.
4. Optimized Resource Allocation: Allocate resources more effectively by understanding which leads require immediate attention.

Activating Lead Scoring

To enable lead scoring in Property Shell, you must contact your account manager. This feature is not activated by default, and your account manager will assist you in setting it up to meet your specific needs.

Viewing The Score of Leads

After you've enabled and configured lead scoring, you will see a new column under the leads table called "Score". This will represent the score of the lead, based on the configurations of the scoring you need.

You can also view this score when you open up an individual lead record under the status field.

Setting Up and Configuring Lead Scoring

To set up lead scoring, simply head to Settings > Lead Scoring to find the settings and configurations needed to set up lead scoring.

Lead scoring in Property Shell is configured in three main steps:

1. Simple Existence Checks
2. Count Metrics
3. Metrics with Points that Decay Over Time

Each step helps you to build a comprehensive lead scoring system tailored to your sales strategy.

1. Simple Existence Checks

This step involves adding points based on the existence of specific data within the lead's profile.

• Example: Assign points if an email address, phone number, or company name is present in the lead’s profile.

Steps to Configure:

1. Go to the Lead Scoring section in Property Shell.
2. Choose the data fields you want to check (e.g., Email, Phone Number).
3. Assign points for each data field that exists.
4. Save your settings.

2. Count Metrics

Count metrics calculate points based on the number of occurrences of specific items. This allows you to assign value to leads based on their engagement or specific behaviors.

• Example: Assign points for each interaction a lead has with your marketing materials, such as opening emails, clicking links, or attending webinars.

Steps to Configure:

1. Go to the Lead Scoring section in Property Shell.
2. Choose the data fields you want to check under the count metrics section
3. Assign points for each data field that exists.
4. Save your settings.

3. Metrics with Points that Decay Over Time

This step involves setting up points that decrease over time, ensuring that recent activities are weighted more heavily than older ones. This helps maintain the relevance of the lead score.

Steps to Configure:

1. Go to the Lead Scoring section.
2. Find the section at the bottom of the settings that has the decay metrics
3. Save your settings.

Understanding Decay Function in Lead Scoring

In lead scoring, a decay function helps to ensure that the scores of leads decrease over time, reflecting the diminishing value of older interactions. This concept is particularly useful for keeping your focus on the most recent and relevant lead activities. We do this by defining the half life of each event. 

What is a Half-Life in Lead Scoring?

A half-life is the time it takes for the score of a lead's activity to reduce by half. By setting a half-life for the score, you create a system where older activities gradually lose their impact, keeping your lead scores dynamic and up-to-date. 

For example, an email received may count as 10 points with a half life of 20 days.

• The initial score shall be 10 points when the email is received
• After 20 days, the email will be worth 5 points
• After 40 days, the email will be worth 2.5 points
• After 60 days, the email will be worth 1.25 points
• The lead score metric will continue to be halve every 20 days from this point forward

AI And Sentiments of Emails and SMS for Lead Scoring

Sentiment analysis is a powerful tool that leverages natural language processing (NLP) and machine learning to determine the emotional tone behind text in emails and SMS messages. By analysing sentiments, the system can gauge the positivity, neutrality, or negativity of a lead's communication. This information can then be integrated into lead scoring to provide a more comprehensive assessment of a lead's potential value.

How Sentiment Analysis Works

1. Data Collection: The system collects text data from emails and SMS messages exchanged with leads.
2. Text Processing: The collected text is processed to remove irrelevant information and normalise the data (e.g., converting all text to lowercase, removing punctuation).
3. Sentiment Detection: Using NLP algorithms, the system analyses the text to detect sentiments. It identifies words and phrases that indicate positive, negative, or neutral emotions.
4. Scoring: Each message is assigned a sentiment score based on its detected emotion. Positive messages increase the score, while negative messages decrease it.

Using Sentiment Analysis for Lead Scoring

Incorporating sentiment analysis into lead scoring can enhance the accuracy and effectiveness of your sales strategy. Here’s how it can be used:

1. Positive Interactions: Leads that frequently send positive emails or SMS messages can be scored higher, indicating their increased engagement and satisfaction.
2. Negative Interactions: Leads that express dissatisfaction or negative sentiments can be scored lower, prompting the sales team to address their concerns promptly.
3. Trends Over Time: Monitoring sentiment trends over time can help identify shifts in a lead’s attitude, allowing for timely interventions and personalized follow-ups.

Consider a lead who sends an email expressing satisfaction with a project. The sentiment analysis system detects positive words such as "happy," "impressed," and "satisfied," assigning a high sentiment score to the email. This positive interaction boosts the lead’s overall score, indicating a higher likelihood of conversion. Conversely, if a lead sends multiple messages expressing frustration or disappointment, the negative sentiment scores reduce their overall lead score, signaling the need for immediate attention to address their issues.

By analysing the sentiments of emails and SMS messages, you can begin to provide a deeper understanding of lead engagement and satisfaction. This nuanced insight allows for more accurate lead scoring, enabling your sales team to prioritise leads effectively and tailor their approach to meet each lead’s needs.  

Summary

By setting up lead scoring in Property Shell, you can transform your sales process, making it more efficient and focused on the most promising leads. Remember to contact your account manager to enable this feature, and follow the steps outlined above to configure your lead scoring system effectively.