Qualify a B2B prospect with the BANT method (SaaS)
This AI prompt applies the BANT method to rigorously qualify a B2B SaaS prospect, taking into account sector specifics (long cycle, multiple decision-makers, budget).
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You are an expert in B2B prospect qualification, specialized in the SaaS ecosystem. Use the BANT method (Budget, Authority, Need, Timeline) to thoroughly analyze the following prospect and determine if they are eligible for a demo or closing. Context: [PROSPECT_SECTOR], size: [COMPANY_SIZE], contact position: [CONTACT_POSITION], expressed need: [EXPRESSED_NEED]. Goal: sell [SAAS_NAME] which solves [KEY_PROBLEM]. Sector constraints: in B2B SaaS, the sales cycle is long (3-9 months), the average ticket is high (€5k-€50k ARR), and the decision often involves multiple stakeholders (IT, business, management). Is the prospect truly solvent and ready to buy? For each letter of BANT, ask specific questions and evaluate a score from 1 to 10: Budget: does the prospect have a budget allocated, a dedicated P&L, or do they need to justify the purchase? Beware of "we'll see" or "no budget defined" because in SaaS, budget is often the number one killer. Authority: does the contact have decision-making power or do they influence the committee? Is an internal sponsor identified? In a structure of [COMPANY_SIZE], the buyer could be the CEO, VP Marketing, or Head of Product. Need: is the need critical and urgent? Does it correspond to an existing pain (revenue loss, inefficiency) or is it a nice-to-have? Check if the prospect has already tried alternative solutions (competitors, build vs buy). Timeline: does the project have a deadline, a planned launch? In B2B SaaS, a horizon >6 months without allocated budget is a red flag. Provide a final recommendation: 'Hot' (score 30-40), 'Warm' (20-29), 'Cold' (<20) and a concrete next action (e.g., send a proposal, schedule a POC, send a customer case). Be ruthless: do not qualify non-solvent prospects.
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Why this prompt works
<p>This prompt is designed for B2B SaaS sales and marketing teams. It forces the AI to adopt a strict qualification posture by integrating real SaaS sales constraints: long cycles (3-9 months), high tickets (€5k-€50k ARR), involvement of multiple stakeholders (IT, business, management).</p><p>To use it:</p><ul><li><strong>Prepare the variables</strong>: fill in the sector, company size, contact position, expressed need, your SaaS name, and the key problem solved.</li><li><strong>Run the prompt</strong>: copy-paste into an AI chatbot (ChatGPT, Claude, etc.) and replace the variables with your real data.</li><li><strong>Analyze the output</strong>: the AI returns a BANT score out of 40, a status (Hot/Warm/Cold) and a recommended next action. Use this result to prioritize your pipeline.</li></ul><p>Application example: a prospect contacts your marketing analytics SaaS. You enter "e-commerce", "50 employees", "Head of Marketing", "ineffective campaign tracking", "AnalyticsPro", "cross-channel reporting". The AI evaluates Budget at 6 (no allocated budget but a marketing envelope), Authority at 7 (sole decision-maker on their budget), Need at 9 (strong pain), Timeline at 5 (desire within 3 months) → score 27, status Warm, recommended action: send an e-commerce case study and schedule a discovery demo.</p>
Use Cases
Expected Output
A BANT score (0-40), a status (Hot/Warm/Cold), and a concrete action recommendation (e.g., send a proposal, schedule a POC).
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