Lesson 1Monetary variables and value-based signals: order value, average order value, lifetime value, margin bucketsThis section explains monetary variables and value-based signals. You will learn how to compute order value, average order value, margin buckets, and revenue-based segments to guide bidding and customer strategies.
Gross order value and net revenueAverage order value and basket metricsCustomer lifetime value model choicesMargin buckets and profitability tiersValue-based bidding and ROAS targetsLesson 2First- and third-party behavioural signals: product views, category visits, cart events, search queries, time on pageThis section details first- and third-party behavioural signals. You will learn how to track product views, cart events, searches, and time on page, and how to enrich them with third-party intent and contextual data.
Product and category view event designCart additions, removals and checkout stepsOn-site search queries and filters usedTime on page, scroll depth and engagementThird-party intent and contextual signalsLesson 3Temporal and recency variables: last visit, last purchase, days since last open/click, session recencyThis section explains temporal and recency variables that capture how recently users interacted. You will learn to compute last visit, last purchase, and days since actions, and how to use recency for segmentation and predictive models.
Timestamps and event time normalizationLast visit and last session calculationsLast purchase and order recency metricsDays since last open, click or loginRecency-based segmentation and RFM useLesson 4Technical and contextual variables: device type, OS, browser, screen size, connection type, geo (city/region), local timeThis section details technical and contextual variables that describe the user’s environment. You will learn how device, OS, browser, connection, location, and local time affect tracking quality, attribution, and campaign optimisation.
Device type and form factor taxonomiesOperating system and version detectionBrowser, user agent and feature supportScreen size, resolution and viewport groupsConnection type, IP-based geo and local timeLesson 5Email and marketing automation data: open rate, click-through, send history, engagement segments, unsubscribe eventsThis section examines email and marketing automation data. You will learn how opens, clicks, bounces, and unsubscribes are stored, how journeys are modelled, and how to use engagement segments for targeting and testing.
Send, open, click and bounce eventsUnsubscribe, spam and preference dataJourney and workflow state trackingEngagement segments and lead stagesDeliverability and reputation indicatorsLesson 6Ad platform data: available fields in Google Ads and Meta Ads (keywords, creative, placements, bid, impressions, clicks, conversions)This section explores ad platform data from Google Ads, Meta Ads and others. You will learn about fields for campaigns, creatives, bids, audiences, and conversions, and how to export and join them with first-party data.
Campaign, ad set and ad level fieldsKeywords, audiences and placements dataCreative variants, formats and metadataBids, budgets and pacing indicatorsImpressions, clicks and conversion logsLesson 7Audience and interest data: inferred interests, affinity categories, custom audiences, lookalikes and API-derived signalsThis section explores audience and interest data from analytics, ad platforms, and APIs. You will learn how inferred interests, affinity groups, custom audiences, and lookalikes are built and how to activate them in campaigns.
Inferred interests from on-site behaviorAffinity and in-market category taxonomiesBuilding and refreshing custom audiencesLookalike modeling inputs and controlsAPI-derived intent and contextual signalsLesson 8Privacy and identifier variables: user IDs, cookie IDs, hashed emails, mobile ad IDs, consent flagsThis section covers privacy and identifier variables that link events to people. You will learn about user IDs, cookies, hashed emails, mobile ad IDs, and consent flags, and how to design compliant identity strategies.
First-party user IDs and login identifiersCookie IDs and browser storage limitsHashed emails and identity resolutionMobile ad IDs and app tracking signalsConsent flags, TCF strings and policiesLesson 9Engagement scoring and cohort indicators: email engagement score, site engagement score, churn risk, frequency bucketsThis section covers engagement scores and cohort indicators that summarise user behaviour. You will learn how to design scoring models, define frequency buckets, track churn risk, and build cohorts for lifecycle and retention analysis.
Designing email engagement scoring modelsSite engagement scores from web behaviorFrequency buckets and intensity tiersChurn risk flags and propensity scoresCohort definitions and tracking windowsLesson 10CRM and transaction systems: user profiles, purchase history, lifetime value, order frequency, returnsThis section details CRM and transaction system data. You will learn how profiles, orders, returns, and lifetime value are structured, and how to connect these records to marketing platforms for targeting and measurement.
Customer master records and keysOrder, line item and invoice structuresReturns, cancellations and refunds dataLifetime value and tenure calculationsSyncing CRM data to marketing toolsLesson 11Web analytics data: Google Analytics/GTM variables (pageviews, events, sessions, traffic source, device, behaviour flows)This section focuses on web analytics data, especially Google Analytics and GTM. You will learn key metrics, event structures, traffic source fields, and how to design tracking plans that support marketing and attribution.
Core pageview, session and user metricsEvent design, parameters and namingTraffic source and campaign UTM fieldsBehavior flows, funnels and pathing dataCustom dimensions and enhanced ecommerce
