Conversica named a Leader in The Forrester Wave™: Conversation Automation Solutions for B2B, Q1 2024

Top Use Cases: Improve the Quality of Leads Passed to Sales

Conversica

Improving lead quality
Improving lead quality
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Best PracticesBuilding Pipeline
Published 01/25/24
4 minutes read

Last week, we explored one of our top use cases: following up on inbound leads. Our second installment in our exploration of top-performing use cases covers what happens with those leads after you follow up. Top Use Case #2 is all about improving the quality of the leads that get passed to Sales—essentially, moving inbounds further through the funnel before a rep touches them.

The Problem with Poor Lead Quality

Poor lead quality is often a symptom of a high volume of raw inbound leads. Frontline Sales and Marketing teams burn countless hours sifting through an overwhelming number of leads, many of which do not align with the ideal customer profile or have a genuine interest in the product or service offered. Aside from wasting resources, low-quality leads result in lower conversion rates and reduced revenue generation.

This leads to:

  • Lower conversion rates: Frontline Sales and Marketing teams spend time sifting through a mountain of less engaged, lower-quality leads, resulting in lower conversion rates and wasted resources.
  • Sales cycle time: The overall productivity of Front Line Sales and Marketing Teams reduced while the sales cycle length increased.
  • Decreased revenue: Pursuing low-quality leads to smaller deals, missed upselling opportunities, and, ultimately, reduced revenue generation for the company.
  • Higher churn rates: Acquiring customers without properly qualifying their needs and interests often results in a poor fit, leading to increased customer dissatisfaction and higher churn rates.
  • Strained customer relationships: Engaging with uninterested or mismatched leads can result in unsatisfactory experiences for both the sales team and the prospects, negatively impacting the company’s brand and reputation.

The Handraiser-Only Principle

Conversica’s Revenue Digital Assistants generate high-quality Conversation-Qualified Leads while streamlining the lead qualification process. RDAs take on the work of engaging every lead on the list, eliminating the junk and low-value leads. This is a huge step beyond traditional MQL scoring models—there’s no confusion when a lead says “yes, I’d like to have a call with a rep.” You’re only passing handraisers to Sales, not leads that you’ve guessed are ready.

Reps are freed from countless hours of call and email grinding, so they focus only on “Conversation qualified” leads the RDA has uncovered.

Conversation Qualified: Also referred to as a "hot lead." When a lead indicates to a Revenue Digital Assistant that they're ready to move to the next step in the Sales process (generally a call with a Sales rep), they become "Conversation Qualified."

Pre-Qualify All MQLs Before Sending to Sales

Conversation Qualified Leads (CQLs) represent a huge step-change in the approach to lead qualification. Unlike traditional inbound leads, which typically come from prospects showing initial interest through actions like content downloads or form submissions, CQLs engage in meaningful dialogue with Revenue Digital Assistants. This results in a more refined level of lead pre-qualification, confirming interest and validating fit before the handof to Sales.

definition of conversation qualified

The key advantage of CQLs over traditional inbound leads lies in the early qualification process facilitated by the RDA. Traditional leads require front-line teams to invest significant time in manual efforts to gauge their potential, whereas with CQLs, RDAs shoulder a significant portion of the qualifying effort. This streamlines the process and ensures Marketing teams are passing on leads of the highest quality, increasing conversion rates and boosting pipeline. No more missed or underworked MQLs, no more anemic SQL/SQO rates.

Relevant Conversations

Depending on the source of your leads, how you want to structure the qualification conversation might be different. For that reason, we’ve got three different skills ideal for improving lead quality.

  1. Qualify Prospects: The first point of contact to engage prospects who submit a demo request or contact us form, or leads who reach a threshold and become Marketing Qualified Leads. Common uses include:
    • Conversationally qualify prospects with questions to confirm fit and then route appropriately 
    • RDAs collect and confirm the correct information from leads for follow-up
  2. Reactivate Interest: Use what you know about a lead to construct a tailored and helpful conversation to warm a lead back up, no matter where they were in the funnel; early interest to open opps. Common uses include:
    • Programmatically engage any MQL that went silent 
    • Segment leads based on their last action & provide them with a new, highly relevant asset
    • Provide personalized offers based on historical activities
  3. Winback Former Customers: Restart the conversation with former customers by highlighting new product advancements & customer successes to demonstrate value unique to that account. Common uses include:
    • Directly address the reason a customer left with a personalized outreach featuring recent successes & features
    • Target accounts with tailored offers & promotions to incentivize customer return

Value of Improved Lead Quality

Conversational AI-powered lead qualification use cases efficiently pre-qualify your leads to validate fit and move the lead toward the next step in their journey.

  • Collect Key Info for Sales
    Ask questions that help route, disqualify, or gather insights for a productive first call.
  • Confirm When Lead is Ready
    Take the burden off Sales to find out if the lead is a good fit and ready for next steps.
  • Fool-Proof Your Sales Cycle
    Not only does Conversica schedule meetings, it follows up to confirm leads connect with the reps.
  • Eliminate Missed Opportunities
    If polite persistence fails, Conversica reaches back out based on a predefined timeframe to try again.
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