Sales teams waste hours every week chasing prospects who will never buy. While your best representatives spend time qualifying cold leads, hot prospects slip through the cracks because no one got to them fast enough. AI lead qualification automation solves this problem by instantly analyzing every prospect and routing high-intent leads to your sales team while filtering out tire-kickers.
This technology transforms how service businesses identify serious buyers. Instead of manually reviewing each inquiry, AI systems evaluate behavioral signals, engagement patterns, and demographic fit in seconds. The result is a sales process where your team focuses exclusively on prospects ready to purchase, improving conversion rates and revenue without adding headcount.
What Is AI Lead Qualification Automation?
AI lead qualification automation uses machine learning to evaluate incoming prospects and assign quality scores based on their likelihood to convert. The system analyzes dozens of data points including website behavior, email engagement, response patterns, and demographic information to determine which leads deserve immediate attention.
Traditional qualification methods rely on sales representatives manually reviewing each inquiry, asking discovery questions, and making subjective judgments about prospect quality. This approach is slow, inconsistent, and prone to human error. One rep might disqualify a promising lead while another wastes time on someone with zero buying intent.
Automated lead scoring eliminates these problems by applying consistent criteria to every prospect. The AI evaluates engagement patterns like how many pages someone visited, which content they downloaded, how quickly they responded to emails, and whether their questions indicate genuine purchase interest. Behavioral analysis reveals intent signals that humans often miss, such as a prospect returning to your pricing page three times in two days.
Intelligent prospect prioritization means your sales team always knows which leads to contact first. High-scoring prospects trigger immediate alerts, while lower-quality leads enter nurturing workflows until they show stronger buying signals. This systematic approach ensures no hot lead waits more than minutes for a response, while your team avoids spending valuable time on prospects who need more education before they’re ready to buy.
Why Manual Lead Qualification Wastes Sales Resources
Time spent qualifying unqualified prospects directly reduces your conversion opportunities. When a sales representative spends 20 minutes on a discovery call with someone who lacks budget or authority, that’s 20 minutes they can’t spend closing a deal with a qualified buyer. Multiply this across your entire team and dozens of daily inquiries, and you’re losing thousands of dollars in productive selling time every week.
Inconsistent qualification criteria lead to missed revenue because different team members use different standards. One rep might require a prospect to have immediate budget availability before moving forward, while another pursues anyone who expresses interest. This variability means some qualified prospects get ignored while resources go to poor-fit leads. Without standardized evaluation, your pipeline fills with low-quality opportunities that clog your sales process.
Sales teams struggle to identify buying signals across multiple channels because prospects interact through website visits, form submissions, email replies, phone calls, and chat conversations. A lead might browse your pricing page, download a case study, and ask detailed questions about implementation timelines across three different touchpoints. Manual tracking can’t capture this complete picture, so representatives miss patterns that indicate high purchase intent.
Human bias affects lead prioritization decisions in ways that hurt performance. A rep might prioritize a friendly prospect who’s easy to talk with over a more qualified buyer with a brusque communication style. Personal preferences, fatigue, and mood influence judgment, creating inconsistency that damages conversion rates. These subjective factors mean your best opportunities don’t always receive the attention they deserve.
How AI Lead Qualification Works in Practice
Data Collection and Behavioral Tracking
AI captures interaction data from website visits, form submissions, email engagement, and chat conversations to build a comprehensive profile of each prospect. When someone lands on your site, the system tracks which pages they view, how long they spend on each page, whether they watch videos, and which calls-to-action they click. Form submissions provide demographic data like company size, industry, and job title, while email engagement reveals which messages generate opens, clicks, and replies.
The system monitors response times, question types, and engagement frequency to assess intent level. A prospect who replies to emails within minutes and asks specific questions about pricing and implementation shows higher intent than someone who takes days to respond with vague inquiries. Chat conversations reveal pain points, urgency, and decision-making authority through natural language analysis. Learn how AI lead response automation captures initial prospect data across all these channels.
Intelligent Scoring and Ranking
Machine learning algorithms assign qualification scores based on predefined criteria weighted according to their correlation with actual conversions. The AI might assign 20 points for visiting the pricing page, 15 points for downloading a case study, 30 points for requesting a demo, and 10 points for matching your ideal customer profile demographics. These weights come from analyzing your historical conversion data to identify which behaviors most strongly predict purchases.
AI identifies patterns that indicate purchase readiness by comparing current prospect behavior with thousands of past leads. If your data shows that prospects who visit your pricing page three times within a week convert at a 40% rate, the system flags similar behavior in new leads. The algorithms detect subtle combinations of actions that humans would never notice, like prospects who read customer testimonials immediately after viewing product features being 60% more likely to buy.
Automated Routing to Sales Teams
High-quality leads receive immediate attention from sales representatives through automated alerts and task assignments. When a prospect’s score crosses your threshold for qualification, the system sends a notification to the appropriate rep with a summary of the prospect’s interactions, pain points, and readiness indicators. This happens in real-time, ensuring hot leads get contacted within minutes instead of hours or days.
Lower-priority prospects enter nurturing workflows that provide educational content, answer common questions, and gradually build trust until buying signals strengthen. These prospects might receive a series of emails explaining your solution, case studies demonstrating results, and invitations to webinars or demos. The AI continues monitoring their engagement, automatically upgrading their priority when behavior indicates increased readiness.
Integration with CRM systems ensures seamless handoff between qualification and sales activities. When the AI qualifies a lead, it creates or updates the CRM record with qualification scores, interaction history, and next-step recommendations. Discover AI CRM automation for streamlined lead management that keeps your entire team synchronized.
Key Benefits of AI Lead Qualification for Service Businesses
Increased Sales Team Productivity
Sales representatives focus on prospects most likely to convert instead of wasting time on low-quality inquiries. When your team knows every lead they contact has been vetted by AI and meets your qualification criteria, they can skip lengthy discovery processes and move straight to solution discussions. This focused approach means each rep handles more qualified conversations per day.
Reduced time wasted on tire-kickers and unqualified inquiries frees up capacity for revenue-generating activities. Instead of spending 30% of their day determining whether prospects are worth pursuing, representatives dedicate that time to closing deals, building relationships with qualified buyers, and following up with opportunities in their pipeline. This efficiency directly translates to more closed deals per rep.
Improved quota attainment through better pipeline management happens because representatives work pipelines filled exclusively with qualified opportunities. When 80% of your pipeline consists of prospects who meet your ideal customer profile and show buying intent, conversion rates naturally increase. Sales forecasting becomes more accurate, and managers can coach to specific skill gaps rather than wasting time reviewing unqualified leads.
Faster Time to Conversion
AI instantly identifies hot leads requiring immediate follow-up, cutting response times from hours to minutes. When a prospect submits a form at 3 PM asking about pricing and implementation timelines, the AI recognizes these as strong buying signals and immediately alerts your sales team. This speed advantage matters because studies show that contacting a lead within five minutes makes them 21 times more likely to qualify than waiting 30 minutes.
Automated prioritization eliminates manual review delays that cause missed opportunities. Without AI, an inquiry might sit in a queue for hours while a rep finishes other tasks, reviews the submission, researches the company, and decides whether to make contact. AI qualification happens in seconds, ensuring every qualified prospect receives immediate attention. Explore AI appointment booking automation to convert qualified leads faster by letting prospects schedule calls instantly.
Consistent Qualification Standards
Eliminating subjective judgment from lead evaluation means every prospect receives fair assessment based on objective criteria. Whether a lead comes in Monday morning or Friday afternoon, from a friendly prospect or someone who asks challenging questions, the AI applies identical standards. This consistency removes the variability that causes qualified buyers to slip through while poor fits consume sales resources.
Applying uniform criteria across all prospects creates predictable pipeline quality. When you define your ideal customer profile and buying signals, the AI evaluates every inquiry against these standards without deviation. This standardization makes it easier to forecast conversions, plan resource allocation, and identify which marketing channels generate the best leads.
Scaling qualification process without additional headcount becomes possible because AI handles volume that would require hiring multiple junior sales representatives. A business receiving 500 monthly inquiries would need several people just for initial qualification using manual methods. AI processes the same volume instantly while maintaining consistent quality, allowing you to grow lead generation without proportional increases in sales headcount.
Better Resource Allocation
Marketing budgets focus on channels generating qualified leads rather than raw lead volume. When AI qualification reveals that LinkedIn ads produce leads scoring 80% higher than Facebook ads, you can shift budget accordingly. This data-driven approach ensures marketing spend targets audiences most likely to convert, improving overall ROI and reducing cost per acquisition.
Sales coaching targets specific qualification gaps identified through AI analysis. If the system shows that 40% of manually-disqualified leads actually met qualification criteria, you know your team needs training on recognizing buying signals. Conversely, if representatives spend excessive time on leads the AI flagged as low-priority, coaching can address why they’re ignoring the data. See how AI sales funnel automation optimizes the entire conversion process from initial contact through closing.
What Lead Qualification Criteria Does AI Evaluate?
Demographic fit including company size, industry, location, and decision-maker role forms the foundation of qualification. The AI checks whether a prospect works at a company matching your target market. A roofing company selling to homeowners would flag a lead as qualified if they own a residential property in the service area, while disqualifying renters or commercial property managers. For B2B businesses, the system verifies whether the contact holds decision-making authority and whether their company size aligns with your solution.
Behavioral signals such as page visits, content downloads, and email opens reveal engagement level and interest depth. Someone who visits your pricing page, downloads a buyer’s guide, and reads three blog posts about implementation best practices shows significantly higher intent than a prospect who only viewed your homepage. The AI tracks these behaviors across time to identify patterns, such as prospects who engage heavily over a short period typically being ready to buy soon.
Engagement quality including question complexity and response speed indicates seriousness and urgency. A prospect asking detailed questions about specific features, integration capabilities, and implementation timelines demonstrates more genuine interest than someone with vague inquiries. Response speed matters because prospects who reply quickly to your outreach and ask follow-up questions are typically closer to a purchase decision than those who take days to respond.
Budget indicators and timeline urgency help prioritize prospects most likely to close soon. When someone asks about pricing, payment terms, or contract length, they’re signaling budget consideration. Questions about “how quickly can we get started” or “what’s your implementation timeline” indicate urgency. The AI flags these signals and prioritizes prospects showing both budget awareness and timeline pressure.
Pain point alignment with your solution capabilities ensures your team focuses on prospects you can actually help. If your HVAC company specializes in residential installations and a prospect asks about commercial building systems, that’s a poor fit despite showing buying intent. The AI evaluates whether the prospect’s stated needs, challenges, and requirements match what your business delivers, preventing your team from pursuing opportunities they can’t win.
AI Lead Qualification for Different Industries
Service-Based Business Applications
Roofing companies identifying homeowners with urgent repair needs benefit from AI that detects language indicating immediate problems. When a prospect mentions “leak,” “storm damage,” or “missing shingles” in their inquiry and asks about “emergency service” or “how soon can you come out,” the AI recognizes high urgency and priority. The system also evaluates property ownership, location within the service area, and whether previous interactions suggest genuine interest versus quote shopping. Learn about roofing AI automation solutions that combine qualification with customer communication.
HVAC contractors prioritizing seasonal maintenance prospects use AI to identify homeowners approaching optimal service timing. The system tracks when prospects last received service, analyzes whether they own systems due for seasonal maintenance, and monitors engagement with content about efficiency and preventative care. When someone downloads a seasonal maintenance checklist in April or October and matches the demographic profile of maintenance agreement customers, the AI flags them for immediate contact. Explore HVAC AI automation capabilities that qualify leads based on seasonal patterns.
Real estate professionals qualifying serious buyers versus browsers rely on AI to distinguish between casual property lookers and motivated purchasers. The system analyzes search behavior patterns, such as viewing multiple properties in a specific price range and neighborhood, returning to view the same listings repeatedly, and asking specific questions about closing timelines and financing. Prospects who schedule property tours, request comparative market analysis, or inquire about mortgage pre-approval receive higher qualification scores. Discover real estate AI automation tools that identify ready-to-act buyers.
Professional Services Applications
Law firms identifying clients with specific legal needs use AI to evaluate case fit, urgency, and prospect seriousness. When someone describes a legal situation in detail, asks about attorney experience with similar cases, and inquires about consultation availability, these signals indicate a qualified prospect. The AI also evaluates whether the described legal issue matches the firm’s practice areas, helping attorneys avoid time-wasting consultations outside their expertise. See law firm AI automation in action for case evaluation and client qualification.
Dental practices qualifying patients seeking specific treatments analyze inquiry content to distinguish cosmetic patients from general care seekers. Someone asking about “teeth whitening,” “veneers,” or “smile makeover” with questions about “before and after photos” and “payment plans” shows high qualification for cosmetic services. The AI prioritizes these prospects differently than general cleaning inquiries, routing them to appropriate team members with specific expertise. Explore dentist AI automation solutions that qualify patients by treatment type.
Med spas distinguishing consultation-ready prospects from information gatherers track engagement with treatment-specific content and pricing inquiries. When a prospect views multiple treatment pages, watches procedure videos, reads patient testimonials, and asks about consultation scheduling, they’re demonstrating serious interest. The AI also evaluates whether questions indicate realistic expectations and treatment suitability, helping providers focus on qualified candidates. Learn about med spa AI automation systems that qualify prospects by treatment readiness.
How AI Qualification Integrates with Follow-Up Automation
Qualified leads trigger personalized nurturing sequences tailored to their specific interests and qualification level. A highly-qualified prospect might receive an immediate invitation to schedule a consultation call, while a moderately-qualified lead enters a three-email sequence addressing their specific questions and concerns. The AI selects appropriate content based on which pages the prospect viewed, what questions they asked, and which pain points they mentioned.
Unqualified prospects receive educational content until readiness improves, keeping your business top-of-mind without consuming sales team time. These prospects might get weekly emails with helpful tips, case studies, and educational resources that address common objections and questions. The AI monitors their engagement with this content, watching for signals that indicate increasing interest or changing circumstances that might improve qualification. Discover AI follow-up automation strategies that nurture prospects at every qualification level.
AI adjusts communication frequency based on engagement levels to avoid overwhelming unengaged prospects while staying present with active ones. A prospect who opens every email and clicks multiple links might receive messages twice weekly, while someone showing minimal engagement drops to monthly touchpoints. This dynamic approach optimizes response rates and prevents unsubscribes from excessive contact.
The system automatically re-qualifies leads as new data becomes available, upgrading or downgrading priority based on changing behavior. A prospect initially scoring low might visit your pricing page, download a case study, and request a demo three months after their initial inquiry. The AI detects this behavior change, recalculates their qualification score, and alerts your sales team that a previously unqualified lead now shows high buying intent. This continuous evaluation ensures no opportunity slips through because someone wasn’t ready during their first interaction.
Common Lead Qualification Mistakes AI Prevents
Overlooking warm leads buried in high-volume inquiry streams happens constantly with manual qualification. When your business receives 50 daily inquiries, even diligent sales representatives miss signals indicating a prospect is ready to buy. An inquiry that looks generic at first glance might contain subtle language showing urgency, or a prospect might have visited your site five times before submitting a form. AI catches these patterns that humans overlook during quick manual reviews.
Following up too slowly with high-intent prospects costs conversions because buying windows close quickly. A prospect researching solutions might contact three competitors in one afternoon. If your team takes six hours to respond while a competitor replies in 15 minutes, you’ve likely lost that opportunity. AI qualification identifies hot leads instantly and triggers immediate alerts, ensuring your team contacts high-intent prospects before competitors do.
Treating all leads with identical outreach approaches ignores the reality that different prospects need different conversations. Someone asking detailed technical questions requires a different response than a prospect with basic awareness-level inquiries. AI qualification segments leads by readiness level, pain points, and interests, allowing personalized outreach that addresses their specific situation rather than generic sales pitches.
Failing to recognize buying signals in conversational data leaves money on the table because prospects reveal intent through subtle language. Phrases like “we’re looking to move quickly,” “budget has been approved,” or “I need to show my boss options by Friday” indicate immediate opportunity. AI trained on your historical conversion data learns which phrases correlate with closed deals, flagging these signals for immediate sales attention.
Inconsistent qualification across different team members creates pipeline chaos where some representatives pursue poor fits while others ignore qualified prospects. One rep might consider any inquiry from a certain industry worth pursuing, while another focuses exclusively on specific company sizes. AI eliminates this variability by applying uniform standards regardless of which team member initially receives the lead.
Measuring the ROI of AI Lead Qualification
Conversion rate improvements from qualified leads provide the clearest ROI indicator. Before AI qualification, your team might convert 8% of all inquiries into customers. After implementation, conversion rates on AI-qualified leads might reach 25% while unqualified leads convert at 2%. This data shows the AI accurately identifies prospects worth pursuing and allows you to calculate revenue impact based on your average deal size.
Sales cycle length reduction happens because representatives spend less time on discovery and qualification conversations. When the AI has already verified that a prospect meets your criteria, sales calls can focus on solution fit and closing rather than determining whether the person is worth pursuing. Businesses commonly see 20-30% shorter sales cycles after implementing AI qualification because prospects reaching sales conversations are further along in their buying journey.
Cost per qualified lead versus cost per raw lead reveals marketing efficiency improvements. You might spend $50 per raw lead from a certain channel, but if only 10% of those leads qualify, your actual cost per qualified lead is $500. AI qualification provides accurate data showing which channels generate the lowest cost per qualified lead, enabling smarter budget allocation that improves overall marketing ROI.
Sales team time savings and productivity gains translate directly to revenue capacity. If AI qualification saves each representative five hours weekly previously spent on manual evaluation, that’s 260 hours annually per rep. Multiply this by your team size and average hourly revenue production to calculate capacity gains. A 10-person team saving five hours weekly creates 2,600 annual hours of additional selling time worth hundreds of thousands in revenue.
Revenue attribution to AI-qualified versus manually-qualified prospects shows whether the technology actually identifies better opportunities. Track closed deals by qualification method to compare win rates, deal sizes, and sales cycle lengths. Most businesses find that AI-qualified leads close at higher rates with larger deal values because the system identifies prospects with genuine need, budget, and authority more accurately than manual methods.
How to Implement AI Lead Qualification Successfully
Define clear ideal customer profile criteria before implementing AI qualification so the system knows what to look for. Document your best customers’ common characteristics including industry, company size, job titles, geographic location, and specific needs your solution addresses. Include disqualifying factors like prospects outside your service area, those seeking services you don’t offer, or companies too small to afford your solution. This profile becomes the foundation for AI evaluation.
Map your existing qualification process and pain points to identify what the AI needs to improve. Document how long manual qualification currently takes, where leads get stuck, which criteria cause the most disagreement among team members, and what buying signals your top performers recognize that others miss. This analysis reveals specific problems the AI should solve and provides baseline metrics for measuring improvement.
Integrate AI qualification with current CRM and sales tools to avoid creating disconnected systems. The AI should automatically create or update CRM records when qualifying leads, sync qualification scores and interaction history, and trigger appropriate workflows in your existing tools. See AI sales automation integration options that connect qualification with your complete sales tech stack.
Train AI models on historical conversion data so the system learns what actually predicts purchases in your business. Upload records of past leads including their demographic information, interaction history, qualification decisions, and ultimate outcomes. The AI analyzes this data to identify patterns correlating with conversions, learning which characteristics and behaviors most strongly predict success. More historical data produces more accurate qualification.
Monitor qualification accuracy and adjust scoring parameters based on real results. Track whether AI-qualified leads actually convert at expected rates, review leads the AI disqualified to check for false negatives, and gather sales team feedback on qualification quality. Use these insights to refine scoring weights, add new criteria, or adjust thresholds. Qualification accuracy improves over time as the system learns from ongoing results.
Provide sales team training on working with AI-prioritized leads so representatives understand how to use qualification data. Explain what different qualification scores mean, how to interpret the AI’s reasoning, and when human judgment should override the system. Train team members to provide feedback on qualification accuracy so the AI continues learning. Adoption improves when your team trusts that the technology helps them sell more effectively.
Future of AI Lead Qualification Technology
Predictive analytics forecasting lead conversion probability will advance beyond current scoring methods. Future systems will analyze thousands of variables including market conditions, seasonal patterns, competitive activity, and economic indicators to predict not just whether a lead might convert, but when they’re most likely to buy and what factors will influence their decision. This precision allows perfectly timed outreach and personalized messaging that addresses specific concerns before prospects raise them.
Natural language processing understanding prospect sentiment will evaluate emotional tone and confidence level in communications. The AI will detect when a prospect’s questions shift from exploratory to decisive, recognize frustration indicating they’re close to abandoning their search, and identify enthusiasm suggesting immediate buying intent. This emotional intelligence helps sales teams adjust their approach based on prospect mood and confidence, improving connection and conversion rates.
Multi-touch attribution across complex buyer journeys will reveal how various interactions combine to create qualification. Instead of simple scoring based on individual actions, future AI will understand that a prospect who attended a webinar, then downloaded a case study, then visited pricing pages shows different intent than someone completing these actions in reverse order. This sequential analysis provides deeper insight into buying process stages and optimal next steps.
Real-time qualification updates as prospects engage will eliminate static scores that don’t reflect changing circumstances. The AI will continuously analyze new interactions, updating qualification status instantly when behavior indicates shifting priority. A prospect might jump from moderate to high qualification during a single website session if they visit multiple high-intent pages and submit a demo request.
Integration with voice and video interaction analysis will extend qualification beyond written communications. Future AI will analyze sales call recordings to evaluate prospect engagement, question quality, and verbal buying signals. Video meeting analysis will assess body language, facial expressions, and participation level to gauge genuine interest versus polite disengagement. These capabilities will provide sales teams with real-time coaching during conversations, suggesting questions to ask or concerns to address based on prospect signals.
Frequently Asked Questions
What is AI lead qualification automation?
AI lead qualification automation uses machine learning to evaluate incoming prospects and assign quality scores based on their likelihood to convert. The system analyzes behavioral data, demographic information, and engagement patterns to determine which leads deserve immediate sales attention and which need additional nurturing before they’re ready to buy.
How does AI determine if a lead is qualified?
AI determines lead qualification by analyzing dozens of data points including website behavior, email engagement, response patterns, demographic fit, and interaction history. The system compares these factors against your ideal customer profile and historical conversion data to calculate a qualification score. Machine learning algorithms identify patterns that indicate purchase readiness, such as specific page visits, question types, and engagement frequency that correlate with closed deals.
Can AI lead qualification work for small businesses?
Yes, AI lead qualification works effectively for small businesses by automating time-consuming evaluation tasks that would otherwise require dedicated staff. Small businesses benefit particularly from AI qualification because it allows them to compete with larger competitors on response speed and consistency. The technology scales to handle varying lead volumes without requiring additional headcount as the business grows.
How accurate is AI at qualifying sales leads?
AI qualification accuracy depends on the quality of historical data used for training and how well you define your ideal customer profile. Most businesses see 80-90% accuracy after proper implementation and ongoing refinement. The system becomes more accurate over time as it learns from results, adjusting scoring parameters based on which leads actually convert. This continuous improvement makes AI qualification increasingly reliable compared to manual methods.
Does AI lead qualification replace human sales judgment?
No, AI lead qualification enhances rather than replaces human sales judgment. The system handles initial evaluation and prioritization, freeing sales representatives to focus on relationship building and closing. Human judgment remains important for complex situations, reading subtle interpersonal signals, and making final decisions on borderline prospects. The best results come from combining AI efficiency with human expertise.
How long does it take to implement AI lead qualification?
Implementation typically takes two to four weeks depending on your existing systems and data availability. The process includes defining qualification criteria, integrating with your CRM and communication channels, training the AI on historical data, and testing accuracy before full deployment. Some businesses see value within days of implementation, though optimal accuracy develops over the first few months as the system learns from ongoing results.
What data does AI need to qualify leads effectively?n
AI needs historical lead data including demographic information, interaction history, qualification decisions, and conversion outcomes. The system also requires ongoing data from website analytics, form submissions, email engagement, chat conversations, and CRM activity. More comprehensive data produces more accurate qualification. At minimum, you need several months of lead history showing which characteristics and behaviors correlated with successful conversions.
Can AI qualification reduce sales costs?
Yes, AI qualification reduces sales costs by improving team efficiency and focusing resources on high-probability opportunities. Businesses typically see cost reductions through decreased time spent on unqualified prospects, improved conversion rates that lower cost per acquisition, better marketing spend allocation toward channels producing qualified leads, and increased revenue per sales representative that improves overall profitability.
AI lead qualification automation represents one of the most impactful investments service businesses can make in their sales process. By combining instant evaluation, consistent standards, and continuous learning, these systems help you respond faster to hot prospects, avoid wasting time on poor fits, and convert more leads into customers. GetDMFlow provides comprehensive AI qualification capabilities integrated with lead response, appointment booking, and follow-up automation to transform your entire sales funnel. When every inquiry receives immediate, accurate evaluation and appropriate follow-up, your business captures opportunities competitors miss while your sales team focuses exclusively on prospects ready to buy.
