Pecan
Lead scoring software
Marketing analytics software
Predictive analytics software
Data science and machine learning platforms
Machine learning software
MLOps platforms
Retail analytics software
Data preparation software
Demand generation software
Lead generation software
Retail software
AI data analysis agents
Low-code machine learning platforms software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
Take the quiz to check if Pecan and its alternatives fit your requirements.
$760 per month
Free Trial unavailableFree version unavailable
Small
Medium
Large
- Retail and wholesale
- Accommodation and food services
- Banking and insurance
What is Pecan
Pecan is a predictive analytics platform that helps business teams build and deploy machine-learning models on company data without requiring a full data science workflow. It is commonly used for propensity and lead scoring, churn prediction, and revenue or demand forecasting across marketing, sales, and customer operations. The product emphasizes guided model creation, automated feature engineering, and production scoring so teams can operationalize predictions in downstream systems. It also provides monitoring and iteration capabilities to support ongoing model performance management.
Business-focused predictive modeling
Pecan is designed around common business prediction use cases such as propensity, churn, and forecasting rather than open-ended experimentation. This makes it easier to align model outputs to operational decisions like prioritizing leads or targeting campaigns. Compared with general-purpose CRM and marketing automation tools, it focuses more directly on building and deploying predictive scores from first-party data. The workflow is oriented toward analysts and revenue/marketing operations teams who need predictions delivered to business systems.
Automated feature engineering workflow
The platform automates parts of data preparation and feature engineering that typically require specialized data science effort. It supports creating training datasets from historical events and outcomes and generating candidate features for modeling. This can reduce time-to-model for teams that already have a data warehouse but limited ML engineering capacity. The approach is suited to structured business data (transactions, product usage, marketing touches, CRM activity).
Operational scoring and monitoring
Pecan supports deploying models to generate scores on new data and making those scores available for activation in business processes. It includes capabilities intended to manage model lifecycle concerns such as performance tracking and refresh cycles. This helps teams move beyond one-off analysis to repeatable scoring in production. The emphasis on deployment and monitoring aligns with MLOps needs for business prediction use cases.
Not a full CRM suite
Pecan does not replace core CRM functions such as pipeline management, activity tracking, quoting, or account management. Organizations typically still need a CRM and/or marketing automation system to execute outreach and manage customer records. As a result, value depends on integrating predictions into existing sales and marketing workflows. Teams expecting an all-in-one lead generation and sales execution platform will need additional systems.
Data readiness requirements
Effective modeling depends on having clean historical data with consistent identifiers and well-defined outcomes (for example, what counts as a qualified lead or churn event). If data is fragmented across tools or lacks sufficient history, model performance and usability can be limited. Implementation often requires data engineering work to connect sources and maintain reliable pipelines. This can be a barrier for smaller teams without a warehouse or strong analytics foundation.
Less flexible than custom ML
A guided, low-code approach can constrain advanced customization such as bespoke feature logic, specialized model architectures, or highly tailored evaluation methods. Organizations with mature data science teams may prefer building and managing models directly in their own stack for maximum control. Some edge cases (complex unstructured data, highly domain-specific signals) may not fit the platform’s standard workflows. Buyers should validate support for their specific modeling and deployment requirements.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Starter | $760 per month (billed annually) | Monthly prediction batches: 2; Storage: 500M rows (total across projects); Support & enablement: In-app; Number of users: 1; Annual pricing ("ANNUAL save 20%") shown on site; Contact sales for signup. |
| Team | $1,400 per month (billed annually) | Monthly prediction batches: 10; Storage: 2Bn rows; Support & enablement: In-app + Essential enablement; Number of users: 3; Contact sales for signup. |
| Business | $2,000 per month (billed annually) | Monthly prediction batches: 60; Storage: 5Bn rows; Support & enablement: In-app + Pro enablement; Number of users: 7; Contact sales for signup. |
Additional/usage add-ons & notes:
- Additional prediction batch: $50 per prediction batch.
- Single sign-on (SSO): Google Workspace & Microsoft (Starter/Team); Any SAML, OIDC & OAuth SSO provider (Business).
- Enterprise / AI at scale: "Contact us" for special deployments, advanced monitoring, custom dashboards, and custom pricing.
Seller details
Pecan AI, Inc.
New York, NY, USA
2018
Private
https://www.pecan.ai/
https://x.com/pecan_ai
https://www.linkedin.com/company/pecan-ai/
