AI for Analytics
Learn to read the data your website and apps already generate — and turn it into decisions that grow your business.
A hands-on course that teaches non-technical professionals how web and mobile data is collected, which metrics actually matter, and how to model, segment, and visualize user behavior to make better business decisions. You work with a real dataset (the Google Merchandise Store) across all three classes and finish by auditing a real company and recommending how to improve conversion.
Who it's for — Marketing, product, operations, and business professionals who work with websites or mobile apps and want to make data-backed decisions — no coding background required.
- Identify the metrics that matter for your business using the Reach–Engage–Convert framework
- Read Google Analytics to diagnose traffic patterns, bounce rate, and conversion drop-off
- Calculate Customer Lifetime Value (CLV), Net Promoter Score (NPS), and RFM segments from real data
- Build a regression model to predict which inputs (session length, page views) drive your chosen business output
- Analyze social networks to find influencers, measure virality (K-factor), and track sentiment
- Create funnel, cohort-retention, and dashboard visualizations that communicate findings clearly
- Apply mobile-specific metrics — Cost Per Install, app-store optimization — and understand how mobile analytics differs from web
You leave with An analytics workflow that turns clickstream data into conversion decisions (AI assists the coding, not an agent build).
Every Enorasi student leaves with their own AI agent — built to their own custom spec — and uses it in their daily work. Whatever your role, you design it to your own custom spec and put it to work.
How you build them, class by class
Analytics Foundations
- How web and mobile data is collected — JS tags, tracking pixels, cookies, HTTP events
- The Reach–Engage–Convert (REC) framework and the metrics that map to each stage
- Customer acquisition loops: organic, paid, and viral — and how to measure each
- Linear and logistic regression: build a model that predicts engagement or revenue from clickstream data
Google Analytics & Customer Understanding
- Bounce rate, segmentation, frequency and recency analysis in the GA4 demo account
- Campaign URL tracking with UTM parameters — trace every visit to its source
- Customer Lifetime Value (CLV) and why CLV > CAC is the non-negotiable rule
- Net Promoter Score (NPS) and RFM segmentation to identify your best — and most at-risk — customers
Advanced Processing, Social & Mobile
- AI-assisted data analysis: re-create the CLV model in Google Colab using plain-English prompts
- Social network analysis — K-factor, centrality, sentiment analysis — to find influencers and measure virality
- Funnel and cohort-retention charts in Tableau: the definitive retention diagnostic
- Mobile analytics: Firebase SDK events, Cost Per Install (CPI), app-store growth tactics
Your hands-on capstone: pick a real company with accessible data, diagnose what it currently does across reach, engagement, and conversion, and deliver a clear, visual set of recommendations for improving conversion. You apply every framework and tool from all three classes. This is the work your certificate is based on — and a portfolio piece that demonstrates applied analytics competency to a potential employer.
Your certificate is based on what you build — not attendance.
Ready to build your AI agent?
A small, high-quality cohort for leaders — fully online, hands-on from minute one.