Focus Your Sales Team on Leads That Actually Convert
A custom lead scoring algorithm prioritizes high-intent leads, letting a small team focus only on prospects likely to close. It stops reps from wasting time on junk leads and increases their follow-up speed on qualified opportunities.
Syntora develops custom scoring algorithms that help businesses prioritize sales leads based on their likelihood to convert. Syntora engineered the product matching algorithm for Open Decision, an AI-powered software selection platform, demonstrating expertise in custom logic and API development. This experience in AI and tailored solutions allows Syntora to design and implement effective lead qualification systems.
The complexity of building such an algorithm depends on factors like the number of data sources, the quality of historical CRM data, and the specific predictive features needed. Syntora has experience developing custom scoring logic, such as the product matching algorithm for Open Decision, an AI-powered software selection platform that uses Claude API and custom logic to match business requirements to software products. For a lead scoring system, Syntora would analyze your existing data landscape to define the specific engineering engagement required.
What Problem Does This Solve?
Small sales teams often start with their CRM's built-in scoring, like in HubSpot. This assigns static points for actions like opening an email (1 point) or filling a demo form (10 points). The system cannot learn from outcomes. It gives the same score to a high-fit lead from a target account and a student who downloaded a whitepaper, forcing reps to manually re-qualify every single lead.
This leads to workflows built with external tools to add more logic. A common setup involves a multi-step process that triggers on a new lead, uses a filter to check for a work email, enriches the contact, and then posts to a Slack channel. This can burn 4-5 tasks per lead. At 500 leads/month, that is 2,500 tasks and a bill that exceeds $120/month just to triage leads before a human even sees them.
The fundamental issue is that these tools are not built for probabilistic scoring. They operate on fixed rules. They cannot answer the most important question: based on the patterns of all our past closed-won deals, how likely is this new lead to convert? This requires a model that learns from data, not a branching path in a visual editor.
How Would Syntora Approach This?
Syntora's engagement would begin with a discovery phase to understand your data environment. This typically involves exporting 12-24 months of your CRM deal history using platforms like HubSpot or Salesforce API. We would then identify opportunities to enrich this with web analytics data, pulling page view and session data from tools such as Plausible or Google Analytics. This raw data would be loaded into a Python environment using pandas for cleaning and feature engineering, where the team would identify and develop predictive features relevant to your sales cycle, such as 'days since last visit' or 'viewed pricing page more than twice'.
Syntora would then train several machine learning models using scikit-learn. A gradient boosting model often provides strong performance for this type of prediction, as it can capture non-linear relationships and interactions that simpler point systems might miss. The model's objective would be to predict the probability of a lead converting to a 'closed-won' deal. The final model would be selected based on its predictive performance for identifying high-value leads.
The selected model would be packaged into a lightweight REST API using FastAPI. This API would expose an endpoint designed to accept lead data and return a calculated score. For deployment, Syntora typically uses serverless functions on AWS Lambda, which allows for cost-effective and scalable operations without constant server management. The infrastructure supporting this API can be defined as code, allowing for clear version control and straightforward updates.
Finally, Syntora would integrate the API with your existing CRM. This often involves configuring a webhook in systems like HubSpot or Salesforce that triggers when a new lead is created or updated. This webhook would send the lead's details to the FastAPI endpoint. The API would process the data, return the score, and Syntora would write this score back to a custom field within your CRM. This integration ensures your sales team receives lead scores automatically, fitting into their current workflow without disruption.
What Are the Key Benefits?
Scores in Milliseconds, Not Minutes
New leads are scored and routed in under 200ms, so your reps can follow up while the lead is still on your website.
Pay Once, Own It Forever
A one-time build cost replaces monthly per-seat SaaS fees. Your hosting on AWS Lambda costs less than $30/month, regardless of team size.
Your Code, Your GitHub, Your IP
You receive the full Python source code in your private GitHub repository, including a runbook for maintenance and future extensions.
Self-Tuning Model Accuracy
We build automated drift detection that triggers retraining on new data when performance drops, with Slack alerts for visibility.
Native Scores Inside Your CRM
The model writes scores directly to a custom field in HubSpot or Salesforce. No new dashboards or tools for your sales team to learn.
What Does the Process Look Like?
Week 1: Data Connection & Audit
You grant read-only access to your CRM. We deliver a data quality report identifying required cleanup before the build starts.
Week 2: Model Build & Validation
We train and test several models. You receive a validation report showing the top predictive features and expected accuracy on new leads.
Week 3: Deployment & Integration
We deploy the scoring API and configure the CRM webhook. You receive login credentials to the monitoring dashboard.
Post-Launch: Monitoring & Handoff
For 30 days, we monitor the model in production and tune as needed. We then deliver the full source code and maintenance runbook.
Frequently Asked Questions
- How is the project cost determined?
- Pricing is based on a fixed scope. The main factors are the number of data sources to integrate (e.g., CRM plus analytics) and the cleanliness of your historical data. A project using one CRM with consistent data is straightforward. Integrating three systems with sparse records requires more time. We provide a fixed-price quote after the initial discovery call at cal.com/syntora/discover.
- What happens if the scoring API goes down?
- The system is designed for graceful failure. If the API is unreachable, your CRM webhook will time out and a default score is applied so your workflow does not break. We use AWS CloudWatch for monitoring, which sends an alert if the API is down for more than 5 minutes. We typically restore service in under an hour. This is covered by the optional monthly maintenance plan.
- How is this better than using Salesforce Einstein?
- Salesforce Einstein requires huge data volumes, often 1,000+ closed deals, before it even activates. Small teams rarely have this. It is also a black box; you cannot see why it scored a lead a certain way. Our build works with as few as 200 closed deals, and we provide a transparent model where you own the code and can see the top predictive factors for every score.
- Can we see *why* a lead got a certain score?
- Yes. We can include a feature that writes the top three reasons for a score to a note field in your CRM. For example: 'High score because: visited pricing page 3x, job title is VP, company size is 50-200 employees.' This gives your reps immediate context for their outreach. This is powered by a library called SHAP that explains model predictions.
- What kind of maintenance is required after handoff?
- The system requires very little manual maintenance. The model automatically retrains on new data if accuracy drifts. The main task is a quarterly check-in to see if your sales process has changed in a way that would require adding new features to the model. The provided runbook documents how to perform this check and trigger a full manual retrain if necessary.
- Does this work with data enrichment tools like Clearbit?
- Yes, enriched data is a powerful input. If you already use a tool like Clearbit or ZoomInfo, we can add that data as features for the model. This typically increases predictive accuracy significantly. For example, 'company employee count' and 'technologies used' are often strong signals of a good fit, but they only exist in enriched data.
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