How to Solve Unclear Deal Prioritization

Sales teams face challenges with deal prioritization, resulting in missed opportunities and resource inefficiency. Reps pursue unlikely deals while overlooking high-potential prospects. This issue costs revenue, slows growth, and frustrates salespeople. AI-powered workflows within a GTM AI Platform provide deeper deal insights, enable effective prioritization, and increase closed business, ultimately accelerating GTM Velocity.

How to Solve the Problem of Unclear Deal Prioritization

Address unclear deal prioritization with two key workflows:

1. AI Deal Scoring (part of the Deal Coaching package)
2. Lead Scoring (part of the Inbound Lead Processing package)

These workflows deliver data-driven insights for deal and lead prioritization, maximizing impact and efficiency. They analyze use case fit, buyer engagement, budget, timeline, and lead quality to focus efforts on revenue-generating opportunities.

1. AI Deal Scoring Workflow

AI Deal Scoring evaluates each deal based on sales data and objectives analysis, revealing deal health and momentum.

Inputs Needed: Deal data (like sales call transcripts)

What the Workflow Does:

• Step 1: Analyzes deal data using AI models trained on historical sales data
• Step 2: Generates an AI-powered deal score indicating likelihood and value of the deal closing
• Step 3: Extracts key information such as use cases, buying signals, budget, timeline, and stakeholders involved
• Step 4: Enhances CRM data with insightful AI-generated deal annotations
• Step 5: Ranks deals by score to clarify prioritization based on revenue potential

Output: A prioritized list of deals ranked by AI-generated scores, with enriched CRM data providing key deal factor insights.

This workflow evaluates qualification criteria automatically, enabling sales teams to prioritize deals effectively. It assesses strategic fit, buyer engagement, budget, timeline, and need, directing efforts to revenue-driving opportunities.

2. Lead Scoring Workflow

Lead Scoring prioritizes inbound leads based on ideal customer profile fit and engagement level.

Inputs Needed: Data from Contact Research.

What the Workflow Does:

• Step 1: Analyzes demographic data like company size, buyer role, and industry to determine ICP fit
• Step 2: Analyzes behavioral data like website visits, content downloads, and email opens to determine engagement level
• Step 3: Combines demographic and behavioral scores into an overall lead score
• Step 4: Dynamically ranks and prioritizes leads based on their scores
• Step 5: Identifies leads with the highest purchase intent based on scoring criteria

Output:

• Dynamic scoring of leads based on fit with Ideal Customer Profile (ICP) criteria (company size, level of seniority, etc.)
• Identification of leads with the highest intent to buy

This workflow analyzes demographic and behavioral data to identify leads most likely to convert to opportunities and customers. Sales reps prioritize engagement based on lead scores.

Putting Them Together

AI Deal Scoring and Lead Scoring, as part of a cohesive GTM AI strategy, provide sales teams significant advantages:

• Reps prioritize inbound leads and active deals using data-driven insights
• Personalized outreach and engagement scales more effectively
• Sales teams operate efficiently by focusing on high-potential deals and leads
• Win rates increase as teams allocate resources to opportunities with the highest revenue potential

AI brings scientific precision to deal prioritization, improving sales team results and efficiency while reducing GTM Bloat. Reps gain clarity on time allocation, managers coach more effectively, and leaders forecast with increased confidence.

Why Copilots Aren't Sufficient

AI copilots like ChatGPT assist with certain sales tasks but lack deep analysis capabilities for sales-specific data such as CRM information and engagement metrics. Automating sales rep chat conversations alone fails to significantly impact results.

Solving the deal prioritization problem at scale requires purpose-built AI workflows for the sales process, integrated into a GTM AI Platform. Copilots also fall short in providing actionable insights and enabling improved sales behaviors across entire teams.

Looking Ahead

With AI-powered deal and lead prioritization established, sales teams can further improve GTM Velocity through AI in other areas:

• Opportunity-based forecasting predicts deal amounts and timing
• Rep performance optimization provides insights into rep activity and coaching needs
• Automated renewal risk detection identifies and mitigates churn risks proactively
• AI-enhanced sales playbooks equip reps with data-driven talk tracks and objection handling

AI workflows across the entire sales cycle maximize revenue efficiency and scale growth effectively. Insights generated in one process area, such as deal scoring, flow seamlessly to other departments, driving alignment and results across the go-to-market organization.

Related Resources

If you found this guide helpful, then you might also be interested in the following resources:

• AI for Lead Scoring: Learn how AI can help you automatically score and prioritize your leads for more efficient sales prospecting.
• Sales Pipeline Management: Discover best practices and strategies for effectively managing your sales pipeline to drive more revenue.
• How Companies Can Eliminate Bloat and Improve CAC with Chris Walker: In this podcast episode, Chris Walker shares insights on using AI to streamline go-to-market efforts and reduce customer acquisition costs.

By leveraging AI-powered workflows for deal scoring, lead prioritization, and other key sales processes, your team can gain the insights needed to focus on the right opportunities and close more deals faster. To see how Copy.ai's GTM AI Platform can help you increase GTM Velocity, book a demo today.