Lead-Scoring Model and AI Roadmap for an Enterprise-Bound SaaS Firm
Overview
What this challenge is about.
Build a logistic regression model predicting trial conversions and outline an AI roadmap for a B2B SaaS firm. Earn a verifiable certificate.
The scenario
Cadence Labs is a 40-person business-to-business software company selling Cadence, a project-management tool that until now spread bottom-up as individual teams adopted it; the company is shifting to top-down enterprise selling worked by account executives. It runs roughly 200 active enterprise trials at any time and has accumulated about 600 historical enterprise trial records over the past 18 months, but it has no data scientists and no machine-learning tooling.
The Brief
What you'll do, and what you'll demonstrate.
Determine which enterprise trials are worth a salesperson's effort by building an explainable lead-scoring model, then decide how to operationalize that score and sequence Cadence's first six months of AI investment.
Earning criteria — what you'll demonstrate
- Frame a fuzzy commercial question as a supervised binary classification problem with a clear label and evaluation metric
- Train and validate an explainable model and communicate its drivers without statistical jargon
- Translate a model output into concrete actions inside a sales system rather than leaving it as an abstract score
- Sequence AI investments under real organizational constraints (no infrastructure, small team, new sales motion)
- Tailor technical findings into recommendations a non-technical executive can act on
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Lead Scoring
Apply lead scoring to solve real industry problems and demonstrate production-level capability.
- Logistic Regression
Apply logistic regression to solve real industry problems and demonstrate production-level capability.
- Ai Strategy
Apply ai strategy to solve real industry problems and demonstrate production-level capability.
- Stakeholder Communication
Apply stakeholder communication to solve real industry problems and demonstrate production-level capability.
- Roadmapping
Apply roadmapping to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Revenue Operations Analyst
This challenge mirrors the core revenue-operations task of turning trial and usage data into a scoring rule that routes sales effort, then wiring that rule into the CRM so account executives act on it daily.
This challenge sharpens
- lead-scoring
- logistic-regression
- stakeholder-communication
AI Product Strategy Consultant
You practice the consultant's loop of scoping a fuzzy business problem, proving value with an explainable model, and sequencing a realistic AI roadmap that a small company without infrastructure can actually execute.
This challenge sharpens
- ai-strategy
- roadmapping
- stakeholder-communication
Go-To-Market Data Analyst
The work bridges to go-to-market analytics roles by combining model building with clear driver explanations and concrete operational recommendations that leaders can adopt without a data-science team.
This challenge sharpens
- lead-scoring
- logistic-regression
- ai-strategy