Customizing Qualification Criteria

Qualification criteria tell Synaptiq what makes a lead worth pursuing. The AI uses these criteria to steer conversations, ask the right discovery questions, and assign a qualification score to every lead it interacts with.

What Qualification Criteria Are

Each criterion represents a piece of information your sales team needs before they can determine whether a lead is a good fit. Synaptiq's AI agent naturally weaves questions into the conversation to uncover this information without making the visitor feel like they're filling out a form.

When enough criteria are satisfied, the lead crosses the qualification threshold and your team gets notified.

Default Criteria: BANT

Every new Synaptiq workspace starts with the BANT framework -- a proven B2B qualification model. You'll find these four criteria pre-configured under Admin Dashboard > Settings > AI Configuration > Qualification Criteria.

Budget

Does the prospect have the financial resources for your solution? The AI explores this by asking about current spending, expected investment range, or whether a budget has been allocated -- never by bluntly asking "What's your budget?"

Default weight: 25% Example discovery prompts the AI might use:

• "Are you currently paying for a solution in this area?"
• "Do you have a rough sense of what you'd like to invest?"

Authority

Is the person a decision-maker, or will they need to involve others? The AI gauges this through questions about their role, team structure, and approval process.

Default weight: 20% Example discovery prompts:

• "Who else on your team would be involved in evaluating this?"
• "What does the decision process typically look like at your company?"

Need

Does the prospect have a genuine problem your product solves? This is often the most important criterion. The AI probes for pain points, current workarounds, and the impact of the problem.

Default weight: 30% Example discovery prompts:

• "What challenge brought you to our site today?"
• "How are you handling this currently?"

Timeline

How soon does the prospect need a solution? A lead with an urgent timeline is more valuable than one casually researching for next year.

Default weight: 25% Example discovery prompts:

• "Is there a timeline you're working toward?"
• "When would you ideally have something in place?"

MEDDIC Framework (Growth Plan)

Growth plan workspaces can switch to MEDDIC — a more rigorous qualification framework used by enterprise sales teams. MEDDIC captures deeper buying signals than BANT and works best when your average deal size is over $10K.

To switch frameworks, go to AI Configuration > Qualification Criteria > Framework and select MEDDIC.

Metrics

What measurable business outcomes does the prospect expect from your solution? The AI probes for quantifiable goals — revenue targets, time savings, cost reductions.

Default weight: 20% Example discovery prompts:

• "What would success look like in the first 90 days?"
• "Are there specific numbers your team is working toward?"

Economic Buyer

Who has the final purchasing authority? MEDDIC distinguishes between the champion (your day-to-day contact) and the economic buyer (the person who writes the check).

Default weight: 15% Example discovery prompts:

• "Who typically approves software purchases at your company?"
• "Is this decision ultimately yours, or does it need sign-off from someone else?"

Decision Criteria

What factors will the prospect weigh when choosing a solution? Understanding their rubric lets the AI position your product against what matters most to them.

Default weight: 15% Example discovery prompts:

• "What are the most important things you'll evaluate when making this decision?"
• "Beyond price, what would make one solution clearly better than another for your team?"

Decision Process

What are the steps the prospect goes through to make a purchase? Knowing the process helps predict timeline and identify potential blockers.

Default weight: 15% Example discovery prompts:

• "Walk me through how a decision like this typically gets made at your company."
• "Are there any procurement or legal steps involved?"

Identify Pain

What is the specific, quantifiable problem your product solves for this prospect? MEDDIC emphasizes identifying the "implicated pain" — connecting their challenge to business consequences.

Default weight: 20% Example discovery prompts:

• "What's the cost of not solving this problem in the next 6 months?"
• "How is this issue affecting your team today?"

Champion

Is there an internal advocate who will drive the buying process on your behalf? A strong champion significantly increases close rate.

Default weight: 15% Example discovery prompts:

• "Who else on your team would find value in this?"
• "Is there someone internally who'd be your internal champion for getting this approved?"

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Adding Custom Criteria

BANT works well as a baseline, but your business likely has qualification signals specific to your market. To add a custom criterion:

1. Go to AI Configuration > Qualification Criteria.
2. Click + Add Criterion.
3. Fill in the following fields:

Field	Description
Name	Internal label (e.g., "Team Size", "Tech Stack Fit")
Description	Tell the AI what this criterion measures and why it matters
Weight	Percentage of total score this criterion can contribute
Discovery prompts	2-3 sample questions the AI can draw from to uncover this information
Scoring rubric	Define what a low, medium, and high score looks like for this criterion

Example: Adding "Company Size" as a Criterion

• Name: Company Size
• Description: We sell to mid-market and enterprise. Companies under 50 employees are rarely a fit.
• Weight: 15%
• Discovery prompts: "How large is your team?" / "Roughly how many people are at your organization?"
• Scoring rubric:
  • Low (1-2): Under 50 employees
  • Medium (3-4): 50-200 employees
  • High (5): Over 200 employees

After adding the criterion, Synaptiq redistributes the remaining weight across your existing criteria proportionally. You can also manually adjust all weights -- just make sure they add up to 100%.

Weighting and Scoring

Each criterion is scored on a 1-5 scale based on the information the AI gathers during conversation. The weighted scores combine into a single Qualification Score from 0 to 100.

How scoring works:

Qualification Score = SUM(criterion_score / 5 * criterion_weight * 100)

For example, with default BANT weights:

Criterion	Weight	Score (1-5)	Contribution
Budget	25%	4	20.0
Authority	20%	3	12.0
Need	30%	5	30.0
Timeline	25%	2	10.0
Total	100%		72.0

The AI assigns scores in real time as it gathers information. If a criterion hasn't been explored yet, it contributes 0 to the total until the AI collects enough signal.

Setting the Qualification Threshold

The qualification threshold is the minimum score a lead must reach before Synaptiq marks it as "Qualified" and triggers your notification workflow.

Navigate to AI Configuration > Qualification Criteria > Threshold and set a value between 0 and 100.

Recommended thresholds by use case:

• High-volume, lower touch: 40-55. Casts a wide net. Good if your sales team can handle more volume and wants to engage earlier.
• Balanced: 55-70. A solid middle ground for most B2B sales teams.
• High-value, selective: 70-85. Only surfaces leads that hit most criteria. Best for enterprise sales with limited rep capacity.

You can change the threshold at any time. Existing leads are not re-scored -- only new conversations use the updated threshold.

How the AI Asks Discovery Questions Naturally

Synaptiq does not fire questions in sequence like a form. Instead, it uses the discovery prompts you provide as inspiration and adapts based on conversation context.

The AI follows these principles:

1. Lead with value. Before asking a qualifying question, the AI provides something useful -- an answer to the visitor's question, a relevant insight, or an acknowledgment of their situation.
2. One question at a time. The AI never stacks multiple qualifying questions in a single message.
3. Context-aware sequencing. If the visitor mentions they're "evaluating options for Q3," the AI recognizes that timeline information was volunteered and scores it without asking a separate timeline question.
4. Graceful fallback. If a visitor declines to answer or deflects, the AI moves on and tries to circle back later in a different way. It never insists.
5. Natural transitions. Questions are introduced with conversational bridges: "That makes sense -- and just so I can point you in the right direction..." rather than abrupt pivots.

Viewing Qualification Scores on Leads

Every lead in your dashboard shows its qualification score and a breakdown of how each criterion was scored.

Go to Dashboard > Leads and click any lead to open the detail view. The Qualification tab displays:

• Overall score with a visual progress bar
• Per-criterion scores showing what the AI gathered and how it scored each one
• Evidence snippets -- the exact messages from the conversation that informed each score
• Confidence indicator -- whether the AI had strong, moderate, or weak signal for each criterion

You can manually override any criterion score if you have additional context the AI didn't capture. Manual overrides are logged and flagged so your team maintains audit visibility.

Filtering by Qualification Score

On the Leads list, use the Score filter to surface leads above a certain threshold, or sort by score to prioritize your outreach. Combine with status filters (e.g., "Qualified + Uncontacted") to build focused work queues for your reps.