Get a Custom Lead Scoring Algorithm Built for Your Team
A custom lead scoring algorithm is a fixed-price build, not a recurring SaaS fee. The cost depends on data sources and CRM complexity, typically a 2-4 week engagement.
Syntora built the product matching algorithm for Open Decision, an AI-powered software selection platform. This demonstrates Syntora's experience in developing custom, AI-driven algorithms. For B2B sales teams, Syntora applies this expertise to design and implement tailored lead scoring systems based on specific business requirements.
The project scope is defined by the number of data sources that would need to be connected and the cleanliness of your historical sales data. A team with a standard HubSpot setup and 18 months of consistent deal data would typically represent a more straightforward project. Connecting to Salesforce, Intercom, and a proprietary database would require more data mapping and cleanup.
What Problem Does This Solve?
Most sales teams start with their CRM's built-in scoring, like HubSpot's. It allows you to assign points for actions like email opens or form fills. But it's a blunt instrument. It cannot learn from past deals, so a lead from a high-value industry gets the same score as an intern if they both download a whitepaper.
A common next step is a workflow tool. A sales team might try to build logic in Zapier: when a HubSpot form is filled, enrich with Clearbit, check a Google Sheet for suppression, then route to a Slack channel. This burns through 4 tasks per lead. At 100 leads a day, that's 400 tasks and a $120 monthly bill for a slow, brittle workflow with a 5-minute lag time.
These tools fail because they are designed for simple, linear automation, not probabilistic scoring. They cannot weigh 15 different weak signals together to produce one strong prediction. They treat every signal as independent, which fundamentally misunderstands how buyers behave. This approach cannot tell you that a lead who visited your pricing page three times is 8x more likely to close than one who downloaded three e-books.
How Would Syntora Approach This?
Syntora would begin by connecting to your CRM's REST API using Python's httpx library for asynchronous data extraction. The approach would involve pulling historical contact, company, and deal data. This raw data would then be loaded into a Supabase Postgres instance, where it could be joined with web analytics and email engagement records to create a unified timeline for each lead.
From this raw data, Syntora's team would engineer predictive features, which might include metrics like 'time since last website visit' or 'job title seniority'. The engagement would involve testing various models, with gradient boosted trees, often implemented with the LightGBM library, being a common choice due to their ability to find non-linear patterns in sales data.
The final model would be wrapped in a FastAPI application and deployed as a serverless function on AWS Lambda. When a new lead is created in your CRM, a webhook would trigger the Lambda function. This system would be designed to return a 0-100 score that is written directly to a custom field on the lead record, visible to your team within their existing CRM. Syntora would optimize the architecture for efficient execution and cost-effectiveness, aiming for minimal hosting expenses.
For ongoing monitoring, structlog could be used to generate JSON logs, which would be sent to AWS CloudWatch. Alerts would be configured to notify stakeholders via Slack for any significant deviations in API error rates or response times. The model would also be scheduled for periodic automatic retraining to adapt to changes in lead flow dynamics.
What Are the Key Benefits?
A Score in Milliseconds, Not Minutes
Our AWS Lambda-based API responds in under 300ms. No more waiting for a 5-minute Zapier poll to see if a new lead is hot.
Pay Once, Own It Forever
A single fixed-price build, not a monthly per-seat SaaS fee that punishes you for growing your sales team.
Your Code, Your GitHub Repo
We deliver the complete Python source code, FastAPI application, and deployment scripts to your private GitHub repository. No vendor lock-in.
Know Why a Lead Scored High
The model provides feature importance scores. See that 'job title' and 'case study views' are your top two predictors, right in your CRM.
Connects Directly to Your CRM
We use native HubSpot or Salesforce webhooks and APIs. Your sales team sees the score in the tool they already use every day.
What Does the Process Look Like?
Week 1: Scoped Build Plan
You grant read-only access to your CRM. We analyze your data structure and sales process, then deliver a detailed technical plan outlining the model features and integration points.
Weeks 2-3: System Build
We build the data pipeline, train the model, and deploy the scoring API. You receive a staging URL to test the API directly.
Week 4: Integration and Go-Live
We connect the API to your live CRM, verify data flow, and your sales team starts using real-time scores. You receive the full source code in your GitHub repo.
Post-Launch: Monitoring and Handoff
We monitor model performance for 30 days. You receive a runbook with instructions for monitoring and manual retraining, plus an optional monthly maintenance plan.
Frequently Asked Questions
- What factors most influence the project cost?
- The two biggest factors are data sources and CRM customization. A standard HubSpot setup with clean data is straightforward. Integrating a custom-built ERP or multiple marketing platforms requires more discovery and data mapping. A typical 2-4 week project has a fixed price, which we provide after our initial data audit.
- What happens if the scoring API goes down?
- The API is deployed on AWS Lambda, which is highly available. If it were to fail, the CRM webhook would not get a response, and the score field would remain blank. No data is lost. We use CloudWatch alarms to get notified instantly. Our maintenance plan includes a 2-hour response SLA for production outages.
- How is this different from buying a tool like MadKudu?
- MadKudu is a recurring SaaS subscription with per-contact pricing that can become very expensive. You are also limited to their platform's features. A custom-build is a one-time capital expense. You own the code and the model, allowing you to modify it for unique business logic, like scoring based on industry-specific signals.
- What is the minimum data we need to get started?
- We need at least 12 months of CRM history with a minimum of 500 closed deals, both won and lost. This provides enough data for the model to learn meaningful patterns. If you have less than this, the model's predictions will not be reliable. We verify this during the free data audit before any work begins.
- Can my sales reps see why a lead got a certain score?
- Yes. For each lead, we can provide the top 3-5 features that contributed to its score. For example: 'High score due to: visited pricing page 2x, job title contains Director, company size 50-200'. This explanation can be written to a note or custom text field in your CRM, giving your reps immediate context.
- What technologies do you use and do we need to manage them?
- The system is built with Python, FastAPI, and Supabase, and deployed on AWS Lambda. You do not need to manage anything. The infrastructure is serverless and the hosting costs are minimal. We handle the initial setup and provide a runbook. For ongoing management, we offer an optional flat-rate monthly maintenance plan.
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