Build custom Fit & Activity scores using any data point to provide foundational insights on potential customer engagement and conversion likelihood.
About the Growth Play
Customer qualification scoring helps you to prioritize leads and identify high-potential customers by assessing their fit and activity levels. By leveraging custom Fit & Activity scores, you can gain insights into potential customer engagement and conversion likelihood. This use case is crucial for optimizing marketing and sales efforts, ensuring that resources are focused on the most promising leads, and enhancing customer segmentation through Fit & Activity analysis.
Benefits
Improved lead prioritization: Focus on leads with the highest potential for conversion.
Targeted marketing campaigns: Segment customers based on behavior and fit for personalized interactions.
Enhanced customer insights: Understand customer engagement and conversion likelihood through detailed scoring.
Efficient resource allocation: Allocate sales and marketing resources to leads with the highest scores.
Informed decision-making: Utilize data-driven insights to refine marketing and sales strategies.
Increased conversion rates: Implement strategies that align with top leads' identified needs and behaviors.
How it works
Step 1: Create a Qualification Agent
Start by navigating to the Agents section and select Create Agent. Next, choose Qualification Agent, define the fit criteria by identifying key customer attributes such as company size or job title, and assign weights to each based on how strongly they indicate conversion potential.
Enhance the agent's robustness by adding at least three fit categories using the Add category option, each with its own set of conditions and corresponding weight.
Example Fit Agent for Marketing Qualified Leads(MQL)
Step 2: Define the activity criteria
Build an effective activity-based qualification agent by selecting key activity events such as logins, signups, or purchases that signal engagement. Prioritize these actions based on their relevance to different buyer personas.
Assign weights to each event to reflect its importance in evaluating lead quality. Incorporate a decay factor so that the impact of an activity decreases over time unless repeated—ensuring the agent reflects current engagement rather than outdated behavior.
Example Activity Agent for MQL
Step 3: Normalize and calculate scores
Intempt automatically normalizes the assigned weights, so there's no need to ensure they add up to 100% — the system adjusts them proportionally. Based on the defined fit and activity criteria, the platform calculates a score for each lead and categorizes them into "low", "medium", or "high" qualification levels, providing a clear view of lead quality.
Step 4: Utilize scores for segmentation and engagement
Segment customers: Use the calculated Fit & Activity scores to create customer segments. For example, group customers into segments like High Fit & High Activity for targeted marketing campaigns.
Implement targeted strategies: Develop personalized engagement strategies based on the segments. For instance, high-fit and high-activity leads can receive premium offers, while lower scores might receive nurturing campaigns to increase engagement.
Step 5: Track and refine
Continuously monitor the effectiveness of your segments and engagement strategies. Adjust the fit and activity criteria to improve lead qualification and conversion rates.
Step 6: Use the scores in journeys
To build a personalized engagement flow:
Create a journey in the Journeys section.
Set triggers based on Qualification scores—for example, use a high activity score to initiate a nurturing sequence.
Define conditions within the journey (e.g., send targeted emails to leads with high fit but low activity).
Align actions with score levels: high-scoring leads might receive special offers, while lower-scoring ones get educational content.
Regularly monitor the journey's performance and refine strategy based on what drives the best engagement.