Glossary

Leads identified as likely to convert based on marketing interactions.

Leads vetted and ready for sales engagement.

Leads showing buying intent based on product usage.

Awareness stage focused on attracting leads.

Consideration stage focused on lead nurturing.

Decision-making stage focused on conversions.

Cost incurred to acquire a lead.

Growth rate of qualified leads month-over-month.

Percentage of users who click on links or ads.

Percentage of leads completing a desired action.

Ranking leads based on engagement and readiness.

Behavioral signals indicating buying intent.

Focused marketing targeting high-value accounts.

Building relationships to move leads through the funnel.

Proactive outreach via email, calls, or ads.

Attracting leads through content, SEO, and organic channels.

Creating awareness and interest in products/services.

Content or offers used to attract prospects.

Using multiple platforms (email, social, SMS) to engage leads.

Reaching out to leads without prior interaction.

Prospects that have shown interest in your product or services.

Automated, scheduled email sequences to nurture leads.

The journey a prospect takes from initial contact to conversion.

Audiences similar to existing leads, identified through analytics.

Decrease in lead quality over time due to inactivity.

Re-engaging old or disqualified leads for future opportunities.

Using data on actions (site visits, downloads) to personalize outreach.

Adding missing details to leads using third-party data providers.

Software for managing customer data, relationships, and sales pipelines.

Organizing and storing customer details.

Tracking opportunities through sales stages.

Steps defining progress in the sales process.

Automatically assigning leads to sales reps based on rules.

Enhancing CRM data with additional insights.

Logging emails, calls, and meetings.

Managing deals and their progression.

Automation for tracking and renewing contracts.

Average revenue generated per customer.

Total revenue a customer generates.

Percentage of customers lost in a given period.

Percentage of customers retained.

Satisfaction levels measured via surveys.

Measures customer loyalty and referral likelihood.

Average value per transaction.

Commitments regarding service quality and timelines. Win Rate – Percentage of closed-won opportunities.

Precision of sales forecasts versus actual results.

Unified data profiles combining sales, marketing, and support data.

Assigning scores based on engagement and demographic fit.

Sharing credit for deals across multiple sales reps or teams.

Mapping revenue contributions to marketing touchpoints. Field Mapping – Aligning CRM fields across integrations.

CRM extensions for storing unique data beyond standard contacts and deals.

Removing duplicates and enriching incomplete data.

Organizing and distributing resources for sales teams.

Managing contract templates, approvals, and signatures.

Predicting revenue outcomes based on pipeline data.

Predictable yearly revenue from subscriptions.

Recurring revenue earned monthly.

Average value of signed deals, often annualized.

Total deal value, including renewals and upsells.

Percentage of sales targets achieved.

Speed at which deals move through the pipeline.

Total value of contracts signed in a given period.

Revenue generated from upsells and cross-sells.

Assessing reasons behind won or lost deals.

Structuring commissions and incentives tied to performance.

Organizing sales territories for optimal performance.

Setting revenue targets for teams and individuals.

Coordinating transitions between sales, marketing, and success teams.

Uncaptured revenue due to inefficiencies, errors, or process gaps.

Processes ensuring teams are aligned before launching campaigns.

Comparing forecast changes across time periods.

Reducing the sales cycle by eliminating bottlenecks.

Evaluating deals based on profitability, discounts, and pricing models.

Managing renewals, upgrades, and downgrades.

Data-driven recommendations for growth opportunities.

Bots automating repetitive tasks.

Data-driven insights and predictions.

Automating actions like follow-ups and approvals.

Automating pricing and proposal generation.

Notifications based on predefined rules or events.

Forecasting trends using historical data.

AI-driven tools for lead qualification and customer service

AI-based adjustments to pricing strategies.

Alerts for issues in data transfer or integrations.

AI-based evaluation of customer tone and feedback.

Tracking compliance with service agreements.

Automating alerts for contract renewals.

Keeping data consistent across platforms.

Pricing models tied to product usage.

AI forecasts of customer retention risks.