Learn a step-by-step process to define your Ideal Customer Profile by analyzing closed-won deals. Use firmographic data to find patterns and create.

Companies with a strong Ideal Customer Profile (ICP) achieve up to 68% higher account win rates. The most effective methodology is to analyze your closed-won deals from the last 12-18 months, not just your largest customers, but your most profitable and efficient ones. By clustering these accounts on firmographic, technographic, and behavioral data, you can build a data-backed profile that sales teams will actually use to prioritize accounts and shorten sales cycles.

TL;DR

Companies with a well-defined ICP see up to 68% higher win rates and can reduce customer acquisition costs by as much as 50%.
The global market for data enrichment solutions, used to add firmographic detail, was valued at USD 2.8 billion in 2025 and is projected to reach USD 6.1 billion by 2033.
The analysis starts by exporting closed-won deals from your CRM, focusing on metrics like deal size, sales cycle length, and retention, not just contract value.
Key firmographic data points for analysis include company size, industry, revenue, geography, and technology stack.
Use the resulting ICP to find lookalike accounts with data providers like Apollo.io or ZoomInfo, but be aware of data gaps for local SMBs.

Why Your 'Best' Customers Might Not Be Your 'Ideal' Customers

Your largest contract is not your ideal customer if it took 18 months to close and required significant custom engineering. The most valuable accounts are not always the biggest; they are the most efficient to acquire and serve. Long B2B sales cycles, often involving multiple decision-makers and legal reviews, directly erode profitability by increasing customer acquisition costs. One analysis of B2B sales cycles found that deals stagnating past 90 days have a low probability of ever closing, consuming sales resources that could be allocated to better-fit opportunities. This is why focusing on contract value alone is misleading. A $200,000 deal that demands 18 months of sales effort, custom development, and high-touch support may be less profitable than a $50,000 deal that closes in 45 days and uses your standard product. The latter represents a repeatable, scalable sales motion, whereas the former is a one-off resource drain. Relying on a few large, demanding clients also introduces significant business risk, as they can hold negotiating leverage and divert your product roadmap away from the broader market's needs. A more accurate definition of an 'ideal' customer is one that gets significant value from your product, stays with you, and is profitable to your business.

The best source for a data-backed ICP is a cohort of 20 to 30 recent closed-won deals that were efficient to close, are profitable to serve, and exhibit high retention. Analyzing only wins is a common mistake; the analysis must filter for outcomes. The goal is to isolate the shared attributes of your healthiest accounts, not just your average customer. To achieve this, you must rank existing customers on specific, quantifiable business metrics rather than relying on intuition. Key metrics for this analysis include time-to-close, which measures sales cycle efficiency; initial support costs in the first 90 days, which indicate how easily customers achieve value; and Net Revenue Retention (NRR) at 12 months, which confirms the customer's long-term success and expansion potential. According to a 2026 report from Sage, a high NRR (typically above 100% for SaaS) is a primary indicator of strong customer retention and successful upselling. By scoring your last 12-24 months of closed deals on these efficiency and profitability metrics, you can identify a top-performing cohort that represents a true source of truth for your ICP.

An analysis of only your top 5-10 largest customers often creates a profile that is too narrow, causing you to miss broader market opportunities. This approach, while seemingly logical, introduces significant bias and risk. A narrow ICP can lead to diluted marketing efforts and may stifle a sales team's ability to close deals that fall just outside the rigid definition. Furthermore, research from Gartner suggests that simple firmographic segmentation limits effectiveness and causes companies to miss profitable opportunities. For example, if your top five customers are all enterprise-level firms in one specific vertical, you might incorrectly conclude that this is your only viable market. This ignores potentially lucrative segments of mid-market companies or adjacent verticals that could be a better fit in terms of sales cycle length and profitability. This is a form of organizational loss aversion, where the fear of excluding any potential customer leads to overly broad or narrowly skewed profiles that serve no one well. According to a report from McKinsey & Company, organizations that leverage customer analytics more broadly are 23 times more likely to outperform competitors in new customer acquisition, underscoring the cost of a limited analytical approach.

How to Pull and Analyze Closed-Won Data with SQL

The initial step in building a data-backed ICP is to export a comprehensive dataset of all closed-won deals from your CRM for the last 12 to 24 months. This raw data forms the bedrock of your analysis, so precision is paramount. Your export must include core firmographic details such as industry, company size, and revenue, alongside deal-specific metrics like Annual Contract Value (ACV), close date, and sales cycle length. According to a 2026 guide from HubSpot, reviewing your CRM for customers with the highest revenue and lowest churn is the first step to creating an evidence-based ICP. Many CRM platforms, including Close, allow for filtered exports to isolate only "All Won" opportunities, ensuring you start with the correct subset of accounts. Neglecting this foundational data export often leads to an ICP built on assumptions rather than revenue reality. A common mistake is targeting by company size without validating the actual revenue contribution from different size bands, a pitfall that a clean data pull helps to avoid. This detailed dataset, covering everything from financials to timelines, is what you will feed into your SQL analysis to uncover the true patterns of your most successful customers.

With your closed-won data prepared, the next step is to apply RFM analysis using SQL to segment and score your customers based on their purchasing behavior. RFM stands for Recency, Frequency, and Monetary value, a data-driven technique that groups customers based on how recently they bought, how often they buy, and how much they spend. This method is highly effective for identifying your most valuable customer groups, moving beyond simple firmographics to understand behavioral loyalty and value. A 2025 analysis from MCP Analytics highlights that B2B companies with recurring revenue models find RFM particularly valuable for account prioritization, as it helps sales teams focus their attention where it matters most. By assigning a score to each customer on a scale of 1 to 5 for each of the three dimensions, you can quickly identify your "Champion" customers, those who score highly across the board. This quantitative approach replaces guesswork with a structured framework, allowing you to systematically categorize accounts and pinpoint the characteristics of those who are not just customers, but true partners driving your business forward.

The final stage involves writing and executing SQL queries to translate your RFM model into actionable customer segments, with the ultimate goal of defining your ICP. A foundational query for this process would focus on monetary value, such as: SELECT customer_id, SUM(order_value) AS total_value FROM purchase_history GROUP BY customer_id ORDER BY total_value DESC;. This query aggregates the total spending for each customer, allowing you to rank them from most to least valuable. From there, you can expand the query to include recency and frequency calculations, often using window functions like NTILE() to create scoring buckets. The objective is to isolate the common attributes shared across the top 20% of your most valuable and efficient customers, as this cohort represents the blueprint for your ideal customer. Research shows that companies with a well-defined ICP, derived from this type of rigorous analysis, achieve significantly higher win rates and shorter sales cycles. This SQL-driven segmentation is not a one-time project; your ICP should be treated as a living document, refined quarterly with fresh CRM data to ensure it accurately reflects your current best customer profile.

What Firmographic and Technographic Data Points Matter Most?

Building a data-backed Ideal Customer Profile begins with eight core firmographic attributes: company size, annual revenue, industry, location, technology stack, growth rate, funding status, and organizational structure. These points provide the foundational layer for identifying your total addressable market before layering on more nuanced data. While seemingly basic, these attributes are critical; for example, a company's employee count often signals its operational complexity and budget thresholds. However, collecting and maintaining this information is a significant challenge, as B2B data can decay by over 22% annually. This reality has fueled the growth of the data enrichment solutions market, which was valued at USD 2.8 billion in 2025 by Grand View Research and is projected to grow at a CAGR of 10.1% through 2033. Investing in data enrichment tools is therefore not a luxury but a necessity to ensure the firmographic data powering your sales and marketing engines is accurate enough to be actionable, preventing the costly mistake of building a strategy on an outdated or incomplete market view.

While firmographics define the addressable market, technographic data provides the critical context needed to qualify it, revealing a company's existing technology stack and operational maturity. This data, which details the software a company uses for its CRM, marketing automation, or cloud infrastructure, is essential for determining compatibility and timing. For instance, knowing a prospect uses Salesforce but lacks a sales engagement platform allows for hyper-specific messaging that addresses a clear product gap. This level of insight is crucial because, according to a 2025 report from 6sense, 95% of winning vendors were already on a buyer's initial shortlist, underscoring the need to deliver relevant messages early. Technographic data enables this by helping teams prioritize accounts using complementary or competing technologies, which is particularly relevant given that Gartner research indicates over half of all high-impact technology purchases in recent years were driven by the need to replace an existing solution. By analyzing a prospect's tech stack, sales teams can move beyond generic pitches and engage in conversations centered on integration, displacement, and specific technical pain points.

Over-reliance on a single data point, such as employee count, creates a distorted and unreliable customer profile; a truly actionable ICP is a composite of 8 to 12 distinct firmographic and technographic attributes. Combining multiple data points allows you to move from a broad description like "tech companies" to a precise, searchable definition like "B2B SaaS companies in the EU with 100-500 employees using Salesforce but not a sales engagement platform." This layered approach, starting with broad firmographic filters and then applying technographic criteria, improves targeting precision and prevents wasted effort on accounts that are a poor technical fit. A common best practice is to analyze your top 20% of customers by lifetime value and retention, documenting the firmographic and technographic attributes that appear in 60% or more of those accounts. This methodology, which contrasts with simply building a wishlist of ideal traits, ensures your ICP is grounded in the real-world characteristics of your most successful partnerships, creating a reliable model for account scoring and prioritization.

Vendor	Primary Data Types	Key Differentiator	Example Integration / Use Case
ZoomInfo	Firmographics, Technographics, Intent Data, Org Charts	Largest B2B contact database with deep CRM and MAP integrations.	Enriching Salesforce records with verified mobile numbers and intent signals from GTM Studio.
Demandbase	Firmographics, Intent Data, Behavioral Data	AI-driven GTM platform that triangulates data from over 40,000 sources.	Identifying accounts in active buying cycles using 810K+ intent keywords for ABM campaigns.
Bombora	Intent Data (Company Surge®), Firmographics	Co-op model sources content consumption data from thousands of B2B publisher websites.	Using Company Surge® signals within HubSpot to trigger workflows when a target account researches a specific topic.
6sense	Firmographics, Technographics, Predictive Intent Data	Processes over 1 trillion daily signals into AI-powered buying stage predictions.	Prioritizing accounts for sales outreach based on 6AI's prediction of their stage in the buying journey.
Cognism	Firmographics, Verified Mobile Numbers, Technographics	Focus on GDPR compliance and human-verified mobile data for European markets.	Building compliant outbound campaigns targeting European companies with high-accuracy direct dials.
Clearbit (by HubSpot)	Firmographics, Technographics, Contact Data	Native enrichment directly inside the HubSpot CRM ecosystem with no extra integration.	Automatically populating new lead records in HubSpot with full company and contact details upon form submission.

Defining Disqualifiers: Who You Should NOT Sell To

Defining who you should not sell to is a more powerful lever for improving sales productivity than only defining who you should. While an ideal customer profile (ICP) provides a target, a negative profile, or set of disqualifiers, gives sales teams the explicit permission to walk away from time-consuming, low-probability deals. According to Salesforce's State of Sales survey of 7,775 professionals, sales representatives spend just 28% of their week actively selling, with the other 72% consumed by administrative tasks, research, and internal meetings. A significant portion of this non-selling time is wasted pursuing prospects who were never a good fit. By creating firm disqualification criteria, organizations can reclaim a substantial number of these hours. For example, a 2026 Gartner survey of 210 sales leaders found that AI tools save sellers an average of 4.8 hours per week, but 72% of organizations fail to reinvest that time into high-value activities. A clear set of disqualifiers acts as a guardrail, ensuring this newly freed capacity is channeled toward accounts that match a proven success profile, directly addressing the productivity paradox where technology saves time but commercial results do not improve.

Your closed-lost deals are the most honest and underutilized data source for building these critical disqualification criteria. While closed-won analysis reveals patterns of success, it suffers from survivorship bias, showing only the accounts that made it through your process and ignoring the larger pool that did not. Analyzing your last 20 to 30 closed-lost opportunities alongside your wins provides a crucial comparative lens. This process involves exporting deal data from your CRM, including firmographics, the stage reached before the loss, and, most importantly, the closed-lost reason. Often, this data is messy because reps select vague reasons like "no decision" to close out deals quickly. However, a systematic review can uncover patterns that are otherwise invisible. For instance, you may find a specific competitor's product, recently installed, is a deal-breaker, or that companies below a certain employee count consistently lack the budget. One framework suggests sorting losses into three buckets: wrong fit, wrong time, or wrong execution, with only the "wrong fit" category directly informing your negative ICP. This prevents you from incorrectly excluding a good segment just because of a single poor sales execution.

Common disqualifiers are often rooted in firmographic, geographic, or technographic realities that make a successful partnership impossible. Firmographic disqualifiers are the most common, such as company size or annual revenue below a threshold that can support your pricing model. For example, Salesforce, despite its broad market, focuses its core ICP on companies with 100-5,000 employees, creating an implicit boundary. Geographic limitations are another clear disqualifier; if you cannot legally or logistically serve a region, any prospect there should be immediately excluded. Technographic criteria are increasingly critical, as the existing technology stack can either enable or block a sale. A prospect who installed a direct competitor's platform within the last six months, for example, is highly unlikely to switch, making them a poor target for immediate outreach. Other disqualifiers can include a lack of purchasing authority in your contact or an unwillingness to participate in the required buying process, both of which signal a deal that is unlikely to close successfully. An ICP that fails to document these negative criteria is only half-finished.

An ICP that is too broad is not a functional tool for a sales team; it is a decorative document that leads to wasted effort and predictable revenue stalls. A profile defined as 'any B2B SaaS company' provides no actionable guidance, forcing reps to chase low-probability deals and making it impossible to create resonant messaging. The primary function of a strong ICP is to create focus, and this focus is achieved through specificity, which is sharpened by explicit disqualifiers. When an ICP is vague, sales and marketing teams often become misaligned, with marketing generating leads that sales teams quietly deprioritize because they know they will not convert. This misalignment has a direct financial impact; according to research cited by one agency, companies with poor sales and marketing alignment see 27% slower profit growth over three years. By documenting clear disqualifiers, such as 'we do not sell to companies with fewer than 50 employees' or 'we do not target companies in the tobacco industry,' you provide the guardrails necessary for efficient growth and prevent your pipeline from being filled with accounts that look active but will never generate revenue.

From Profile to Pipeline: Activating Your ICP

Activating an Ideal Customer Profile (ICP) must culminate in a tangible Target Account List (TAL), not a theoretical slide deck. The entire purpose of analyzing closed-won deals is to produce a list of specific companies that sales and marketing will actively pursue. This transition from profile to pipeline is where strategy becomes execution. According to a 2025 guide from Intent Amplify, this process involves filtering the total addressable market down to accounts matching the new ICP criteria, then layering on real-time buying signals to prioritize them. A best practice is to treat the TAL as a dynamic asset, not a static document; one methodology suggests refreshing roughly 20% of the list quarterly to incorporate new insights and adapt to market changes. This operational step requires aligning the entire go-to-market team, from marketing, which will focus its budget on these accounts, to sales development representatives (SDRs) who will dedicate their prospecting time to them. The goal is to create a unified front where all resources are aimed at a specific, agreed-upon set of high-value accounts that are most likely to become a company's next best customers.

With a data-backed ICP, the next step is building lookalike lists of new prospects using B2B data providers. Platforms like ZoomInfo, Apollo.io, and Cognism are designed for this purpose, allowing revenue teams to translate ICP firmographics and technographics into searchable filters to generate new target accounts. ZoomInfo, for example, is positioned for enterprise teams needing the broadest possible data coverage across North America, while Apollo.io is often favored by startups and small businesses for its combination of a contact database and sales engagement tools at a lower entry cost. Cognism differentiates itself with a focus on GDPR-compliant data for European markets and phone-verified mobile numbers, which it claims can help sales teams reach 87% of their prospecting lists. The core function of these tools is to take the attributes of a company's most successful customers, such as industry, size, and technology stack, and find other companies that match. This dramatically expands the targetable market beyond a team's existing network and provides the raw material for outbound sales campaigns and account-based marketing programs.

A significant capability gap exists when targeting local small and medium-sized businesses (SMBs), as major B2B data providers offer poor coverage for owners of businesses like plumbers, salons, and construction contractors. One 2026 analysis noted that providers like Apollo and ZoomInfo build their databases by scraping sources that favor corporate footprints, such as LinkedIn, SEC filings, and press releases. This methodology renders a five-person HVAC company whose owner is not on LinkedIn effectively invisible. This data gap creates a stark difference in lead acquisition costs. For B2B technology and professional services, the average cost per lead (CPL) can range from $150 to over $500. A 2026 report found the blended CPL for technology and financial services to be $464 and $554, respectively. In contrast, leads for local services are substantially cheaper; a 2026 analysis of Google Local Services Ads found the blended cost for home service contractors was $53 per lead, with trades like plumbing and electrical averaging between $35 and $75. This disparity highlights that while enterprise data platforms are powerful for corporate targets, they are the wrong tool for reaching the main street economy, where different channels and cost structures prevail.

The cost of activating an ICP through B2B data providers is best measured by the cost per usable contact, not just the platform's sticker price. Pricing models vary significantly: Apollo.io uses a transparent, per-seat model with monthly credit allowances, making it accessible for smaller teams with prices from $49 to $119 per user per month. In contrast, ZoomInfo and Cognism use a platform fee structure combined with per-user licenses. ZoomInfo's platform fees start around $14,995 annually and can rise to $39,995 or more, with seat add-ons costing an additional $1,500 to $2,500 per user. Cognism's entry point is often quoted around a $15,000 platform fee. However, published prices are misleading, as negotiation is common, and data accuracy directly impacts the true cost. For instance, Apollo's reported accuracy is around 65-70%, while Cognism claims a 98% connect rate on its premium phone-verified data. A B2B demand generation benchmark suggests a healthy CPL is 1-3% of the average deal size, meaning a $50,000 contract could justify a lead cost between $500 and $1,500. This context is crucial for calculating the expected return on investment from these powerful, but expensive, data platforms.

Vendor	Best For	Pricing Model	Reported Starting Price (Annualized)	Local SMB Coverage
ZoomInfo	Enterprise teams needing broad US data and intent signals	Annual platform fee + per-seat licenses	~$14,995 + seats	Poor
Apollo.io	Startups and SMBs wanting an all-in-one data and engagement platform	Per-seat, credit-based, with monthly or annual options	~$588 per user (Basic plan)	Poor to Fair
Cognism	Teams focused on compliant European outreach and phone-verified mobile numbers	Annual platform fee + per-seat licenses	~$15,000 + seats	Poor
Google Local Services Ads	Local service businesses (plumbers, HVAC, etc.) seeking direct inbound leads	Pay-per-lead	~$100-$1,000/mo budget recommended	Excellent
UpLead	SMBs prioritizing a 95% data accuracy guarantee	Per-seat, credit-based	~$1,188 per user	Poor
Lusha	Individual sales reps and small teams doing LinkedIn prospecting	Per-seat, credit-based	~$432 per user	Poor

How Often Should You Update Your ICP?

An Ideal Customer Profile is not a static artifact to be created once and archived; it is a living document that demands regular, scheduled reviews to maintain its accuracy and effectiveness. The most effective go-to-market teams treat their ICP like production code, subjecting it to a quarterly calibration process to ensure it reflects the current reality of the market, not the assumptions of a year ago. This quarterly review involves a systematic analysis of new closed-won and closed-lost deals, churn data, and direct feedback from sales and customer success teams to identify which attributes are actually predictive of success. The goal is to spot emerging patterns in your most successful customers and contrast them with the profiles of deals that were lost or customers who churned. While this sounds intensive, the re-validation process can be highly efficient once a baseline is established. For instance, some experts outline a focused, four-hour process for analyzing customer data and redefining the ICP, demonstrating that a refresh does not need to be a multi-month strategic project. This cadence ensures the ICP remains a reliable tool for prioritizing accounts and aligning the entire revenue organization, from marketing campaigns to sales qualification and customer onboarding.

The operational necessity of updating your ICP is underscored by the relentless pace of B2B data decay, which silently erodes the foundation of your sales and marketing efforts. Industry analysis indicates that B2B data decays at approximately 22.5% per year, meaning that by the end of a 12-month period, nearly one-quarter of the records in your CRM could be inaccurate. This decay is not just about bounced emails; it encompasses job changes, promotions, new phone numbers, company mergers, and technology stack updates. For instance, a 2025 analysis by ZeroBounce of over 11 billion email addresses confirmed that at least 23% of email lists degrade annually. The financial consequences of operating with this stale data are substantial. Gartner's research from 2025 estimates that poor data quality costs the average organization $12.9 million annually in the form of wasted marketing spend, inefficient sales cycles, and missed opportunities. This makes the quarterly refresh of both your ICP definition and the contact data you use to target it a critical revenue-generating activity, preventing sales teams from wasting their limited selling time, which according to Salesforce's 6th Edition State of Sales report (n=5,500 professionals) is already less than a third of their week, on prospects who no longer fit.

Beyond the scheduled quarterly reviews, certain strategic business events should trigger an immediate and mandatory re-evaluation of your Ideal Customer Profile. These triggers include major product launches, expansion into new geographic or vertical markets, or a sustained and otherwise unexplained drop in sales win rates. Each of these events fundamentally alters the context in which your company operates, potentially invalidating the core assumptions of your existing ICP. A new product, for example, might solve a completely different set of problems for a new type of buyer, while a new market introduces unique competitive pressures and buyer behaviors. A falling win rate is perhaps the most urgent signal, acting as a direct indicator that your sales team is misaligned with the market, either by targeting the wrong accounts or using messaging that no longer resonates. Given that organizations with a well-defined ICP can achieve up to 68% higher account win rates, ignoring such a drop is a significant risk to revenue. An immediate ICP review in these situations allows a company to quickly recalibrate its targeting and messaging, ensuring the go-to-market strategy adapts to the new reality before significant resources are wasted pursuing misaligned opportunities.

Frequently Asked Questions

What is an ideal customer profile (ICP)?

An ideal customer profile (ICP) is a detailed description of the type of company that will benefit the most from your product and provide the most value in return. It moves beyond broad descriptions by focusing on specific firmographic traits like industry, company size, and revenue that define customers most likely to convert and have a high lifetime value. This strategic tool guides your sales and marketing investments, ensuring you focus resources on prospects that are the best fit for your business. Ultimately, a well-defined ICP helps you find companies that are not just willing to buy, but also have the greatest potential for long-term strategic fit and scalability.

What is the difference between an ICP and a buyer persona?

The primary difference is that an Ideal Customer Profile (ICP) describes the perfect company to sell to, while a buyer persona describes the individual people within that company who make buying decisions. Your ICP answers the question, "Which companies should we pursue?" using firmographic data like industry, size, and location. In contrast, a buyer persona answers, "Who inside these companies influences the decision?" by focusing on an individual's job title, goals, and daily challenges. Using both is critical; the ICP identifies the right accounts, and personas guide the messaging to connect with the decision-makers inside them.

How do I get the firmographic data for my ICP analysis?

Firmographic data is collected from a mix of public and private sources, which you can use to build a data-driven ICP. Public sources include company websites, business registries, and annual financial reports, which provide details on location, leadership, and revenue. For more scalable and automated data collection, many businesses use third-party data providers and sales intelligence tools that aggregate company information from millions of records. You can also start by analyzing your existing customer base, pulling data from your CRM to identify common attributes among your most successful customers.

What are the most common mistakes when building an ICP?

The most common mistake when building an ICP is making it too broad, which fails to help sales and marketing teams prioritize their efforts. Another frequent error is relying on assumptions or a "wish-list" of dream clients instead of analyzing data from your actual most successful customers. Many teams also stop at basic firmographics like industry and company size, ignoring crucial technographic and behavioral data that reveal why a company is a good fit. Failing to account for customer profitability, not just revenue, can also lead you to target customers who are not actually driving sustainable growth.

How does an ICP reduce customer acquisition cost?

An Ideal Customer Profile (ICP) directly reduces customer acquisition cost (CAC) by focusing your sales and marketing resources only on prospects most likely to convert. This targeted approach prevents wasted spending on campaigns aimed at ill-fitting leads, which increases the overall efficiency of your marketing budget. By aligning sales efforts with a clear profile, sales cycles often shorten and win rates improve because the team is engaging with highly qualified leads from the start. Over time, this improved efficiency and higher conversion rate naturally lowers the average cost to acquire each new customer.

What SQL commands are useful for customer segmentation?

The GROUP BY clause is fundamental for customer segmentation in SQL, as it allows you to aggregate data for different customer groups, such as by location or purchase behavior. You can then use aggregate functions like COUNT() to calculate purchase frequency, SUM() for total spending, and MAX() to find the most recent purchase date for RFM (Recency, Frequency, Monetary) analysis. To create specific segments like "High-Value Customers," you can use CASE statements to classify customers based on calculated metrics. Combining these with JOIN allows you to merge data from multiple tables, like customer details and purchase histories, for a more complete analysis.

Last updated: November 2026

How to Build an ICP From Your Closed-Won Pipeline