AI for Analytics
Over half your web traffic is no longer human. Learn to measure all three storefronts — the website, the chatbot, and the AI agents — and turn the data into revenue you can defend.
A hands-on course in analytics for the two-visitor web, where your visitors are humans and machines. You run the analytics of LaceYard — a fictional online shoe retailer with a live simulated store, a 13-file data corpus, a chatbot, and an agent-commerce endpoint — using your AI assistant as the analyst's workhorse while you own every number. You learn to read websites (funnels, customer value, honest regression), chatbots (containment, chat-assisted revenue), and AI agents (tasks, mandates, protocol orders), and you finish by writing the strategy memo that finds +30% revenue, every number defended from the files.
Who it's for — Marketing, product, operations, and business professionals who work with websites, apps, chatbots, or AI-facing storefronts and want to make data-backed decisions — no coding background required.
In class you work on a synthetic company corpus — a fictional business modeled on real-world challenges, so you practice on realistic, messy data, not toy examples.
- Measure the two-visitor web: know who's really at your door — humans, chatbot users, verified bots, declared agents, and stealth scrapers
- Apply the Reach–Engage–Convert framework as controllable inputs — and never present a rate without its base
- Direct an AI assistant to sessionize raw clickstream data and fit an honest conversion model — catching the leakage, confounders, and self-selection traps a flattering model hides
- Read GA4 like an analyst — and know exactly which questions (margin, identity, machines) need the warehouse instead
- Compute RFM tiers, CLV, LTV:CAC, and cohort retention — and join behavioral tribes to wallets to find the highest-ROI fix on the site
- Grade a chatbot by its job: containment with a satisfaction signal next to it, false-containment-adjusted rates, and chat-assisted revenue without the self-selection lie
- Review AI agents like employees — golden sets, cost per task, red-teaming — and audit agent commerce by reconciling ledgers, not funnels
- Measure your brand's Share of Answer across AI assistants and design a tracker with an owner and an action trigger
You leave with Your AI assistant as the analytics workhorse — loading the corpus, modeling, segmenting, and generating verified reports — plus a live buyer-agent vs seller-agent negotiation lab..
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 decide what it knows, what it does, and how it works — then put it to work.
How you build them, class by class
Foundations, Data & Honest Analysis
- The course thesis: majority-machine traffic — and the three analytics (website, chatbot, agent) that share one discipline
- Where the data comes from: tags, cookies, pixels — plus the server logs where bots and agents are honest
- Reach–Engage–Convert as controllable inputs, computed on LaceYard's real numbers
- From 61,812 raw events to an honest conversion model: leakage, confounders, A/B tests, and the duration paradox
GA4, Your Best Customers & the Chat Storefront
- GA4 by example — reports, key events, funnels, UTMs — and what it quietly hides
- People, not sessions: identity stitching, RFM tiers, CLV vs CAC, cohorts, and the replenishment heartbeat
- Find the tribes with clustering, join them to wallets, and design a segment A/B with a guardrail
- Chatbot analytics: every web metric's chat twin, containment traps, false containment, and when NOT to build a bot
When Customers and Employees Are Agents
- Hire agents like employees: golden sets, cost per task, red-teaming, and drift watching
- The machine customer audit: verified, declared, and stealth visitors — and the serve / price / block decision
- A purchase with zero pageviews: mandates, protocol funnels, and ledger match rate as the new conversion rate
- GEO and Share of Answer: earning the citation inside AI answers — plus the unified scorecard and 7-step framework
The names the whole industry runs on.
Your AI assistant
Claude, ChatGPT, or Gemini as the analyst's workhorse — it loads the files, computes, and drafts the reports; you verify and own every number.
Google Analytics 4
The industry-standard web analytics cockpit — you learn what it answers well and exactly where the data warehouse must take over.
MCP & agent protocols
The fast-emerging rails of agent commerce — mandates, protocol ledgers, llms.txt, and registries — instrumented before they're big numbers.
Plus the rest of the working stack — Google Analytics 4 (GA4) · Warehouse-style event exports · Claude / ChatGPT / Gemini as the analysis workhorse · AI-generated, human-verified reports · Web-server logs · Agent protocol ledgers · MCP · llms.txt & product feeds · Share of Answer panels.
Your hands-on capstone: the +30% memo. As LaceYard's head of analytics you find the revenue levers hiding in the corpus — across the website, the chatbot, and the agent surfaces — size each one in dollars with a method a classmate could re-run, and attach a validation plan to every recommendation. Graded on arithmetic, method, and governance, not prose. This is the work your certificate is based on — and a portfolio piece that proves you can measure a business whose visitors are no longer only human.
Your certificate is based on what you build — not attendance.
Ready to build your AI agent?
A small, high-quality cohort for professionals — fully online, hands-on from minute one.