DEALERSIP User Manual

Archiving a lead typically indicates that it is no longer actively pursued or relevant to the current sales process. Below are common reasons for archiving a lead:

• Unresponsive Leads: No engagement or response despite multiple follow-ups.
• Disqualified Leads: Leads that don't meet qualification criteria or fall outside the scope of your offerings.
• Duplicate Entries: Identified as duplicates, requiring consolidation into a single record.
• Lost Interest: Leads explicitly state they are no longer interested or have chosen a competitor.
• Invalid Contact Information: Incorrect or incomplete details, making communication impossible.
• Stagnant or Aged Leads: No progress or updates for an extended period, making the lead irrelevant.
• Business Decisions: Internal priorities or resource constraints lead to deprioritizing certain leads.
• Handled Elsewhere: Leads converted or managed outside the system to avoid duplication.
• Opt-Out Requests: Leads request to stop communication or unsubscribe from contact efforts.
• Seasonal or Temporary Inquiries: Leads tied to expired campaigns or one-time events.
• Misclassified Leads: Incorrectly entered leads, such as contacts marked as leads by mistake.

Archiving leads helps maintain a clean and focused pipeline, improve resource allocation, and prevent clutter in your CRM system.

How are archived leads handled by the Analytic?

Analytic includes or excludes archived leads depending on the requirement of a metric. For example, "Never Followed Leads" and "Top Active Leads" metrics do not include archived leads. On the other hand, the "Lead Distribution" metric includes archived leads so that managers can know how many leads are archived out of the total generated leads, allowing them to focus on lead quality. Similarly, the "User Performance" metric also includes archived leads because hiding a user's efforts for archived leads would underrate them, and sales managers wouldn't know how much effort is being wasted on archived leads. This approach ensures that analytics are meaningful, fair, and provide accurate insights based on the context of the data.