AI vs. Traditional Lead Scoring: Key Differences
- Henry McIntosh
- 1 day ago
- 18 min read
Which lead scoring method is best for your business? It depends on your goals, resources, and sales complexity.
Here’s the gist:
Traditional lead scoring relies on fixed rules (e.g., job titles, company size) and manual updates. It’s simple, transparent, and works well for smaller teams or stable markets.
AI-powered lead scoring uses machine learning to analyse large datasets, adjusting scores in real-time. It offers higher accuracy, works at scale, and handles complex sales cycles better.
Quick Overview:
Traditional Scoring: Easier to set up, but less precise. Requires frequent manual updates and struggles with large datasets or changing buyer behaviour.
AI Scoring: More accurate and efficient. Handles complex variables and adapts automatically, but needs clean, robust data and higher upfront investment.
Key takeaway: If your organisation deals with high lead volumes, multi-stakeholder sales, or frequent market changes, AI scoring is a smarter choice. For smaller teams or simpler pipelines, traditional scoring may suffice for now. Aim for a phased approach: refine your current system before transitioning to AI.
Using AI for Lead Scoring (AI for Business People Series)
Traditional Lead Scoring: How It Works
Traditional lead scoring assigns points to prospects based on their characteristics and actions. This straightforward, rule-based method has long been a staple for B2B lead qualification, especially among UK marketing teams that need a clear and auditable way to decide which leads deserve immediate attention from sales.
Here’s how it works: marketing and sales teams create an Ideal Customer Profile (ICP) and set up a scoring system where certain attributes and behaviours either add or subtract points. For example, points might be added for prospects in senior roles, from large companies, or those who show high levels of engagement. On the flip side, points could be deducted for signals that suggest a poor fit.
Once set up in a CRM, scores are updated in real time as new data comes in. When a lead's score crosses a specific threshold (say, 70 points), it becomes a marketing-qualified lead (MQL) and is passed to the sales team. The system relies on simple "if/then" logic: if a prospect meets a criterion, then points are added.
Rule-Based Scoring Models
Traditional scoring models follow a strict rule-based framework. Fit criteria - such as demographic and firmographic data - determine how closely a lead matches the organisation’s best customers. In the UK B2B space, this often includes factors like job title (e.g., director, CFO, IT lead), company size (measured by employee count or annual turnover in GBP), industry sector (e.g., financial services, technology, manufacturing), and location.
Behavioural data adds another layer, measuring how engaged and interested a prospect is. This might include actions like opening emails, clicking links, downloading content (e.g., whitepapers or case studies), visiting key pages (like pricing or demo pages), submitting forms, attending webinars, or participating in events. Actions that signal stronger purchase intent - such as requesting a demo or contacting sales directly - are typically given higher scores.
Negative scoring is also common. For example, points may be deducted for contacts identified as students, competitors, or consultants, or for prospects who haven’t engaged in months. This helps filter out leads unlikely to convert, saving sales teams from wasting time.
One of the strengths of rule-based models is their transparency. Every point is assigned based on a clear rule, making it easy to explain the scoring process to stakeholders or train new team members. If a sales rep questions why a lead scored highly, the marketing team can point to specific factors, such as the lead’s seniority, company size, or demonstrated engagement through actions like webinar attendance or content downloads.
However, this simplicity comes with a downside. These models rely on human judgement to decide which attributes and behaviours matter most, often based on past experience rather than thorough data analysis. If these assumptions are off - or if buyer behaviour changes - the model can quickly become outdated. Regular reviews and manual updates (often every quarter) are needed, which can be a time-consuming process, especially for organisations managing large volumes of leads across multiple channels.
Research shows that businesses using lead scoring can boost marketing-driven revenue by 20–30% compared to those without it [1][6]. However, traditional rule-based models often achieve only 60–70% accuracy in predicting which leads will convert [4][5]. This highlights a key limitation: static rules struggle to account for the complexities of modern B2B buying, especially in sectors like financial services or enterprise technology, where buying decisions often involve multiple stakeholders and lengthy sales cycles.
Strengths and Limitations of Traditional Methods
Despite its limitations, traditional lead scoring has its advantages. For smaller or mid-sized UK B2B teams, it’s relatively quick to implement. A marketing manager can set up a scoring framework in their CRM and start prioritising leads without needing advanced technical expertise or data science support.
Another key benefit is its clarity. If a sales rep questions a lead’s high score, the marketing team can refer to clear, predefined criteria, such as the prospect’s seniority, company size, or engagement activities like downloading content or attending webinars. This transparency helps align marketing and sales teams, ensuring smoother handoffs.
Traditional scoring also works well for niche campaigns, where tailored rules can prioritise specific contacts. However, it requires ongoing maintenance to keep up with changing markets, products, and buyer behaviours. A scoring model that worked six months ago might not reflect today’s engagement signals, making regular updates essential.
Studies suggest that sales teams can spend 25–30% of their time on poor-quality leads when scoring models are too simplistic or misaligned with actual buyer journeys [3][2]. Scalability is another challenge. While a small set of criteria is manageable, a complex model with hundreds of variables across different regions and buyer personas can quickly become overwhelming. Additionally, traditional scoring can unintentionally embed biases. For example, overvaluing senior titles like C-suite roles might cause marketing teams to overlook mid-level managers who often champion deals, leading to missed opportunities.
CRM vendors report that refining traditional scoring models - by adding negative scoring for disengaged contacts or adjusting weights based on pipeline data - can improve the sales acceptance rate of marketing-qualified leads by 10–20 percentage points [1][6]. For UK organisations in regulated industries such as financial services, insurance, or technology, traditional lead scoring also provides a compliance advantage. With every rule explicitly defined and based on consented first-party data, it’s easier to demonstrate adherence to GDPR and PECR requirements.
Traditional lead scoring remains a solid starting point for B2B teams formalising their lead qualification processes, working in stable markets, or needing a compliance-friendly system. For more complex sectors with longer sales cycles and nuanced buyer behaviours, specialist agencies like Twenty One Twelve Marketing can help refine scoring models, integrate multiple data sources, and establish governance to ensure the framework stays relevant as market conditions evolve.
This foundational approach sets the stage for understanding why AI-powered predictive scoring is becoming increasingly popular in today’s fast-changing B2B landscape.
AI-Powered Predictive Lead Scoring: A Modern Approach
AI-powered predictive lead scoring is changing the way B2B organisations in the UK identify and prioritise potential customers. Instead of relying on outdated point systems or subjective judgement, this method uses machine learning to analyse vast amounts of historical and real-time data. The goal? To estimate how likely a lead is to convert into a paying customer [1]. For industries like financial services or enterprise technology, where buying decisions are complex, this approach provides a smarter, more dynamic alternative to traditional methods. Unlike static scoring systems, AI evolves alongside market changes, making it a powerful tool for modern sales and marketing teams.
How AI Predictive Scoring Works
The process begins with data collection. AI models pull information from sources like CRM systems, marketing platforms, website analytics, email interactions, and third-party intent providers. Using this data, the AI identifies patterns and attributes that are common among leads that have successfully converted in the past. This is a major departure from traditional scoring, which often relies on arbitrary assumptions to assign points.
Once trained, the AI assigns probability-based scores to leads, updating them automatically as new data becomes available. For instance, if a prospect visits a pricing page, downloads a whitepaper, or engages with an email campaign, their score adjusts accordingly. Unlike traditional systems that might only track a handful of criteria, AI can analyse hundreds of variables at once, uncovering complex patterns and relationships that would be impossible to detect manually.
In UK B2B markets, particularly in sectors like financial services, AI models can combine behavioural data with intent signals from external sources. For example, it can identify what topics a company is researching or which technologies it’s evaluating. This capability brings an added layer of precision to lead qualification, enabling sales teams to focus on the most promising opportunities.
Advantages of AI in Lead Scoring
One of the biggest benefits of AI-powered lead scoring is its accuracy. By learning from new data and analysing a wide range of variables, these models often outperform traditional rule-based systems. In fact, some vendors report that organisations using AI scoring see conversion rate improvements of 20–30% [7].
Real-time updates are another standout feature. Unlike traditional systems that require manual adjustments or operate on fixed schedules, AI models refresh automatically. This means sales and marketing teams always have up-to-date insights, allowing them to act quickly when a lead shows interest.
Scalability is also a major advantage. Traditional systems can struggle to handle growing datasets or expanding market segments. In contrast, AI models can evaluate leads across multiple products, regions, and customer types without needing separate rule sets. For UK businesses looking to grow, this flexibility is invaluable.
AI also excels at uncovering hidden insights. By analysing complex patterns in the data, it not only improves lead scoring but also provides actionable intelligence for broader sales and marketing strategies. Plus, its ability to learn continuously ensures that the model stays relevant as market conditions and buyer behaviours shift.
Challenges of AI Adoption
Despite its many benefits, implementing AI-powered lead scoring comes with challenges. One of the biggest hurdles is data quality. AI models need large amounts of clean, consistent data to function effectively. For organisations with fragmented CRM systems or inconsistent data entry, building an accurate model can be difficult.
Smaller B2B teams, especially those operating in niche markets, may face additional issues due to limited historical data. Without a robust dataset, the model’s predictions may lack reliability. On top of that, the cost of AI tools and the technical expertise required to implement them can be barriers for mid-sized businesses still fine-tuning their marketing operations.
Another concern is the risk of bias. If historical data includes biases - such as favouring certain types of leads for reasons unrelated to their actual potential - AI models can unintentionally reinforce these patterns. Building trust in AI is also a challenge. Sales teams accustomed to simple, rule-based systems may find it hard to embrace scores generated by complex algorithms, especially if the reasoning behind them isn’t transparent.
Integration can be another sticking point. Connecting AI scoring tools to existing systems like CRMs, marketing platforms, and website analytics requires careful planning. For organisations in regulated sectors such as financial services, ensuring compliance with GDPR and PECR adds another layer of complexity.
That said, as data infrastructure improves and AI tools become more accessible, adoption is likely to accelerate. Agencies like Twenty One Twelve Marketing can play a key role in this transition, helping UK businesses design effective data strategies, align AI models with account-based marketing efforts, and refine scoring systems over time. For organisations with long sales cycles and complex decision-making processes, these partnerships can make a real difference.
The question for most UK B2B teams isn’t whether to adopt AI-powered lead scoring, but when and how to do it in a way that complements their existing processes. By weighing the benefits against the challenges, businesses can determine the best path forward to improve their lead qualification and conversion rates.
Key Differences Between AI and Traditional Lead Scoring
Now that we've looked at how both methods operate, it's time to compare them side by side. The differences between AI-powered and traditional lead scoring go beyond the technical - they reshape how UK B2B teams identify, prioritise, and engage with potential customers. These distinctions directly affect conversion rates and operational efficiency, making it essential for organisations to understand which approach aligns best with their goals, resources, and growth plans.
Accuracy and Efficiency Comparison
Traditional lead scoring relies on fixed rules and manual updates. Scores are assigned based on predefined criteria, such as job roles or company size, and require regular human intervention to stay relevant. When market trends or buyer behaviours shift, these models can quickly become outdated, limiting their effectiveness.
AI-powered scoring, on the other hand, uses machine learning to continuously update scores based on probabilities. It pulls from a diverse range of data - website activity, email engagement, CRM history, and even third-party intent signals. Unlike traditional methods, AI doesn't just focus on a handful of factors; it analyses hundreds of variables simultaneously.
This dynamic approach translates to significantly higher predictive accuracy. While traditional models often hover around 60–70% accuracy, AI models typically reach 80–90%. For UK B2B sales teams handling high-value accounts, this increased precision reduces the risk of chasing unqualified leads (false positives) or missing out on promising opportunities (false negatives).
Here’s a quick comparison of key dimensions:
Dimension | Traditional Lead Scoring | AI-Powered Lead Scoring |
Data Inputs | Static attributes (e.g., job role, company size) | Behavioural signals, CRM data, intent patterns |
Update Frequency | Manual (weekly/monthly) | Continuous (real-time or near real-time) |
Predictive Accuracy | 60–70% | 80–90% |
Time to Score a Lead | Hours to days | Seconds to minutes |
Manual Effort Required | High (rule creation and updates) | Low (automation after setup) |
Impact on Response Times | Slower due to batch processing | Immediate follow-up enabled by real-time scoring |
For UK marketing leaders, these improvements have real financial implications. A modest 5–10% boost in conversion rates could add tens or even hundreds of thousands of pounds to annual revenue, depending on deal size.
Scalability and Flexibility
Beyond accuracy, scalability is another area where AI-powered scoring stands out. Traditional models struggle as organisations grow. Larger lead volumes, expanding data sources, and more complex campaigns demand constant rule adjustments and manual data reconciliation. What works for a small pool of leads often becomes unmanageable at scale, leading to slower qualification times.
AI systems, however, are designed for growth. They seamlessly integrate data from CRM platforms, marketing tools, website analytics, and third-party sources, handling thousands - or even hundreds of thousands - of leads without requiring additional staff. This scalability is particularly valuable for fast-growing B2B organisations expanding across regions, product lines, or customer segments.
Flexibility is another advantage. Traditional systems require manual reconfiguration whenever the target customer profile changes. For example, shifting focus from SMEs to enterprise accounts or targeting a new industry like financial services might take weeks or months to adjust. By contrast, AI models can be retrained with new data, quickly adapting to changes like a product launch or a shorter sales cycle. This adaptability is crucial in fast-moving markets where buyer behaviours and competitive dynamics shift frequently.
For UK businesses in niche B2B sectors - such as specialised financial services or enterprise technology - precision is critical. Lead volumes may be smaller, but deal values are often much higher. Partnering with agencies like Twenty One Twelve Marketing, which focuses on complex B2B markets, can help create tailored AI models that integrate predictive insights into campaigns targeting high-value accounts.
Cost and Resource Requirements
Costs and resource demands also differ significantly between the two methods. Traditional scoring might seem low-cost initially, but hidden labour expenses can add up. For many mid-sized UK B2B teams, tasks like data preparation, rule updates, and alignment meetings consume hours each week, creating a high cumulative cost.
AI-powered scoring requires a higher upfront investment. Software licensing, data integration, and initial model setup can cost anywhere from a few thousand to tens of thousands of pounds annually. However, once implemented, AI drastically reduces manual effort by automating data ingestion, scoring, and insight generation. This frees up teams to focus on higher-value activities like strategy, content creation, and direct engagement with priority accounts.
Long-term, AI often delivers a better return on investment. Improved accuracy and fewer false positives can significantly boost revenue per sales team member. For instance, if a 10-person sales team, each generating £500,000 annually, achieves a 10% improvement in conversion rates, the additional pipeline value could be substantial.
Traditional models typically rely on in-house capabilities, with marketing and sales teams managing rules. AI scoring, however, benefits from the involvement of data specialists or external partners for setup and monitoring. Many modern platforms simplify this process with user-friendly interfaces, enabling UK teams to manage AI models without deep technical expertise after the initial setup.
Deciding between traditional and AI methods - or even a hybrid approach - requires a clear assessment of current capabilities and future needs. Factors like lead volume, data quality, tech stack maturity, sales capacity, and budget all play a role. A phased approach can work well: refine traditional scoring first, then pilot AI models on a subset of leads to measure improvements. Defining success metrics - such as increased qualified opportunities, conversion rates, or revenue in GBP - and aligning sales and marketing on score usage will ensure the chosen method delivers tangible results, not just a new dashboard.
Which Lead Scoring Method is Right for Your Business?
Deciding between AI-powered and traditional lead scoring isn't a straightforward choice. It all depends on your organisation's current setup - your infrastructure, the complexity of your sales process, and the resources you can allocate. Picking the wrong method can lead to wasted investment and missed opportunities. This analysis builds on earlier comparisons of model accuracy, scalability, and cost.
Factors to Consider
Start by evaluating your lead volume and data readiness. Traditional scoring might work for smaller lead volumes, but when you're handling thousands of leads monthly across various channels, managing it manually becomes overwhelming. AI models, however, rely on clean, historical data - at least 6–12 months of reliable CRM information - to identify meaningful patterns.
Rich and accurate data signals are crucial. AI thrives on diverse inputs like web activity (page views, downloads), email engagement, event participation, product usage (for SaaS businesses), and firmographic details (such as industry and company size). The more comprehensive your dataset, the sharper the AI's predictions.
Budget considerations also come into play. Traditional scoring often appears more affordable upfront, involving internal resources and occasional consultancy fees. But over time, hidden costs can pile up - teams spending hours maintaining rules, debating thresholds, and reconciling data. On the other hand, AI-driven scoring requires a larger initial investment, including licensing fees, integration efforts, and data clean-up. However, for industries like UK enterprise software or professional services, where the value per converted lead is high, the returns often justify the expense. AI solutions can boost conversion rates by 20–25% and shorten sales cycles, leading to noticeable revenue gains.
Your sales pipeline goals and SLAs (service level agreements) are another key consideration. If your team struggles to meet response-time SLAs, wastes time on low-quality leads, or sees only a small percentage of marketing-qualified leads turn into opportunities, AI's real-time prioritisation can make a big difference. On the other hand, if your main challenge is aligning on basic definitions and processes, a traditional model with clear SLAs might be the better short-term solution.
The complexity of your sales cycle is also important. For businesses dealing with long decision-making processes involving multiple stakeholders, traditional models often fall short. AI can analyse signals from various contacts within the same account, identifying accounts with higher chances of progression. This makes AI especially effective for complex B2B sales where understanding group behaviour is key.
A hybrid approach can also work well. For example, if compliance or leadership requires hard rules - like prioritising leads from certain industries or deal sizes - combine rule-based thresholds with AI scoring. This allows you to maintain control while benefiting from AI's predictive capabilities, offering a balanced solution.
Tailored Solutions for Niche Markets
In niche sectors like financial services or technology, there are additional considerations such as data privacy, explainability, and regulatory compliance. Traditional models are often easier to audit because their rules are transparent. AI models, while powerful, may need extra governance and bias monitoring to meet regulatory standards.
That said, these industries often exhibit highly specific behaviours and extended sales cycles that traditional models can't address effectively. AI models trained on sector-specific data can improve win rates while staying compliant. The key is proper oversight and clear documentation of how decisions are made.
For businesses targeting senior leaders or hard-to-reach sectors, precision is non-negotiable. Whether you use traditional or AI scoring, your method must support highly targeted, account-based strategies that resonate with niche audiences.
For UK B2B companies requiring this level of precision, Twenty One Twelve Marketing offers tailored strategies designed for complex sales environments. Their expertise in mapping buyer journeys, identifying predictive signals, and creating effective scoring models ensures lead scoring aligns with your goals. Whether you need rule-based systems or AI-ready solutions, their approach is well-suited to the unique challenges of UK markets.
If you're planning to transition from traditional to AI-powered scoring, a phased approach can help reduce risks. Start by documenting your current scoring model and cleaning your CRM data. Then, pilot an AI solution alongside your existing system for three to six months. Compare conversion rates and gather feedback from your sales team. During this period, engage sales early, provide clear instructions on using AI scores, and refine the model as needed. This step-by-step approach helps ensure the new system delivers measurable improvements before a full rollout.
Keep an eye on metrics like marketing-qualified lead to opportunity conversion rate, opportunity to closed-won rate, average deal size, sales cycle length, and sales acceptance rate. Benchmark your current performance and review these metrics quarterly to determine if adjustments are needed. Successful AI implementations often lead to double-digit gains in conversion rates and shorter sales cycles, but results will vary based on your starting point.
Ultimately, the best lead scoring method is the one that fits your current capabilities while paving the way for future growth. Some businesses may find traditional scoring sufficient for now, evolving towards AI as their data and infrastructure mature. Others, managing high volumes and complex sales cycles, may see immediate benefits with AI. The key is an honest evaluation of your current position and the pipeline growth you aim to achieve.
Conclusion
Selecting the right lead scoring method can significantly influence how your business prioritises leads, allocates resources, and drives pipeline growth. AI-powered predictive lead scoring stands out for its ability to deliver more accurate results through continuous learning, automate large-scale data processing, and quickly adapt to shifts in buyer behaviour. Unlike traditional models that require frequent manual updates, AI offers a more dynamic and efficient approach.
That said, AI isn’t the best fit for every business from the outset. In environments rich with data, AI-powered lead scoring tends to outperform traditional methods, offering more precise predictions, faster lead prioritisation, and greater scalability - especially when sufficient historical data is available to train the model. However, for smaller teams, early-stage companies, or organisations with limited data and budgets, rule-based scoring may still be the better choice. Its simplicity and ease of maintenance make it a practical starting point before transitioning to AI.
For UK B2B marketers, the decision hinges on evaluating data maturity and internal resources. If your CRM and marketing automation systems are well-structured, with clear definitions of marketing-qualified and sales-qualified leads, and you have enough historical data to train a reliable AI model, then AI scoring could unlock substantial benefits. On the other hand, businesses with smaller datasets, fragmented tools, or limited analytics capabilities might find a rule-based model more suitable initially, transitioning to AI as their processes and data quality improve.
Start by auditing your lead funnel to address any data issues and consider piloting AI scoring alongside your existing rules. By comparing metrics like conversion rates, sales acceptance, and pipeline velocity, you can assess the impact before a full rollout. Define success metrics upfront - such as higher opportunity creation rates, improved win rates, or faster time-to-first-contact - to evaluate whether AI is genuinely enhancing performance.
While AI-powered lead scoring may require a higher upfront investment, it often delivers strong returns by boosting conversion rates and increasing deal values. UK businesses should weigh ongoing licence fees and governance efforts against measurable benefits like increased pipeline value and reduced manual workload for sales teams.
In complex B2B sectors with lengthy sales cycles and multiple decision-makers - such as financial services or enterprise technology in the UK - AI-powered predictive scoring proves particularly valuable. It can detect nuanced intent signals across extended buying journeys, which rule-based models might overlook. Combining AI insights with input from sales and marketing teams ensures the scoring approach accounts for regulatory requirements, typical buying committee structures, and specific triggers like procurement stages or RFP timelines.
For businesses targeting niche markets with hard-to-reach senior decision-makers, precision is critical. Specialist agencies like Twenty One Twelve Marketing assist UK B2B companies in translating AI-powered lead scoring into actionable programmes by aligning data, technology, and go-to-market strategies.
It’s also vital to prioritise explainability. Teams need to understand the factors driving scores so they can refine and challenge them as needed. Successful adoption of AI requires careful change management, including training sales teams, updating service-level agreements, monitoring for biases, and maintaining human oversight. This ensures AI enhances decision-making rather than replacing human judgement.
While traditional lead scoring still has its place, the trend is clearly moving towards AI-enhanced models. These models integrate richer data, adapt continuously, and enable more targeted, account-based outreach. Treat lead scoring as an evolving process - starting with where you are today but aiming for AI-driven methods to strengthen your pipeline, accelerate sales cycles, and remain competitive in an increasingly data-driven landscape.
FAQs
How does AI-driven lead scoring boost conversion rates compared to traditional methods?
AI-driven lead scoring transforms how businesses identify and prioritise potential customers. By using advanced algorithms, it can process and analyse massive datasets quickly and with impressive accuracy. Unlike older methods that depend on manual effort and fixed criteria, AI adjusts in real-time, pinpointing top-quality leads with much greater precision.
This smarter approach helps sales teams work more efficiently by focusing on leads that are most likely to convert, saving both time and resources. Plus, AI's scalability means businesses can handle larger volumes of data without missing out on promising opportunities.
What challenges might businesses encounter when moving from traditional to AI-powered lead scoring?
Transitioning from traditional methods to AI-powered lead scoring isn't always a straightforward process. One of the main hurdles is data readiness. AI systems thrive on precise, clean, and well-organised data. Unfortunately, many businesses find they need to tidy up or restructure their data before they can even begin to implement these systems.
Then there's the matter of staff training. Teams need to get up to speed on how to use these AI tools and, more importantly, how to make sense of the insights they generate. Without proper training, even the most advanced tools can feel overwhelming or underutilised.
Another big challenge is the initial investment of time and resources. While AI-powered lead scoring can deliver long-term benefits like efficiency and scalability, the upfront work can be daunting. Setting up the system, ensuring it integrates seamlessly with current workflows, and tailoring it to meet specific business objectives all take time and effort.
That said, once these challenges are addressed, the rewards are hard to ignore. Businesses often see more accurate scoring, improved lead prioritisation, and smoother sales processes - making the initial effort well worth it.
When is it beneficial for a business to use a hybrid approach to lead scoring?
A hybrid approach to lead scoring blends the precision and scalability of AI-driven predictive models with the insight and judgement of human expertise. By combining these two methods, businesses can harness the best of both worlds, allowing data-driven insights to be enriched by industry-specific knowledge and human intuition.
This is particularly beneficial for companies in complex B2B markets or niche industries, where unique buyer behaviours or specialised sales processes might not be fully captured by AI alone. By integrating AI analytics with manual fine-tuning, businesses can create a more customised and effective lead scoring system. This not only helps in identifying high-value prospects but also ensures that resources are focused on the opportunities most likely to convert.
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