Lead Scoring

Lead scoring is a system that assigns a numeric value (0--100) to each conversation, representing how likely the customer is to make a purchase or take a desired action. This helps you prioritize your time and attention on the most promising leads.

How Lead Scoring Works

Every conversation in the system has a lead score field that ranges from 0 to 100:

• 0 means the system has not yet detected any buying intent or engagement signals.
• 100 means the customer has demonstrated very strong purchasing intent.

The lead score is calculated and updated automatically by the AI as it interacts with customers. The score considers factors such as:

Factor	Impact
Product inquiries	Asking about specific products increases the score
Price questions	Asking about prices or discounts signals buying intent
Comparison requests	Comparing products indicates the customer is in a decision-making phase
Stock/availability checks	Checking whether items are in stock suggests readiness to buy
Repeated engagement	Multiple messages and return visits indicate sustained interest
Cart-related questions	Questions about ordering, shipping, or payment show strong intent
General browsing	Casual questions without specific product interest result in lower scores

Reading Lead Scores in the Conversation List

In the conversation list (left panel), each conversation card shows the lead score with color-coded text:

Score Range	Color	Meaning
70--100	Green	Hot lead -- This customer is showing strong buying intent. They are likely close to making a purchase decision. Prioritize these conversations.
40--69	Amber/Yellow	Warm lead -- The customer has shown moderate interest. They may need more information or a nudge to convert.
0--39	Gray	Cold lead -- The customer has shown minimal buying intent so far. They may be browsing, asking general questions, or just exploring.

The score is displayed in the format "Score: XX" on each conversation card.

Lead Score in Conversation Detail

When you select a conversation, the lead score is also displayed in the conversation header (top right area), using the same color coding. This gives you immediate context about the customer's engagement level before you start reading the message history.

Using Lead Scores to Prioritize

Lead scoring helps your team focus their efforts where they matter most. Here are some recommended strategies:

1. Prioritize Hot Leads

Conversations with scores of 70 or above should receive priority attention. These customers are actively considering a purchase and may need:

• Quick responses to final questions
• Help completing an order
• Personalized product recommendations
• Human support for complex requirements

2. Nurture Warm Leads

Conversations with scores between 40 and 69 represent customers who need more engagement. Consider:

• Proactively offering additional product information
• Sharing relevant promotions or discounts
• Answering comparison questions thoroughly
• Following up if they go quiet

3. Monitor Cold Leads

Conversations with scores below 40 are typically early-stage interactions. The AI chatbot handles these well on its own. However, you might:

• Let the AI handle routine responses
• Check periodically for any that might escalate
• Use these conversations to identify common questions for your knowledge base

4. Combine with Status Filtering

For the most effective workflow, combine lead scores with status filters:

• Filter by "Handed to Human" status to see conversations waiting for human response, then prioritize by lead score.
• Filter by "Active" status to monitor AI-handled conversations and take over high-scoring ones that might benefit from personal attention.

Info: Lead scores are updated dynamically as the conversation progresses. A cold lead can become a hot lead as the customer asks more product-specific questions. Check back regularly to see if scores have changed.

Lead Score vs. Message Count

The conversation list shows both the lead score and the message count. These provide complementary information:

• Lead score reflects the quality of the interaction -- how likely the customer is to convert.
• Message count reflects the quantity of interaction -- how many messages have been exchanged.

A conversation with many messages but a low lead score might indicate a customer asking general or off-topic questions. A conversation with few messages but a high lead score might indicate a customer who immediately asked about a specific product's price and availability.

Practical Example

Consider these three conversations:

Customer	Messages	Lead Score	Analysis
Alice	2	85	Asked about a specific product's price and stock. Hot lead -- consider taking over to close the sale.
Bob	15	30	Asked many general questions about your store, policies, and categories. Not yet focused on a specific purchase. Let the AI continue.
Carol	6	55	Compared two products and asked about features. Warm lead -- may benefit from a personalized recommendation.

In this scenario, you would prioritize Alice first (highest score), then Carol (moderate score with comparison behavior), and let the AI continue handling Bob.

Info: Lead scoring works best when your product catalog is fully set up and the AI chatbot has been properly configured in Chat Studio. The more context the AI has about your products and business, the more accurately it can assess customer intent.