Customer Intelligence and Analytics for Omni-Channel

This module presents data as the foundation of analytics, outlining how to build data stores, warehouses, and databases aligned with business needs, plus data architecture and big data.

Learn how data serves as the foundation for analytics, from legacy data warehouses and 360 customer views to star schemas, ETL processes, and master data governance.

Explore why data strategy matters for decision making, who owns it, and how business framing sets scope across digital transformation, campaigns, and risk; includes star schema and data models.

Frame the business problem before analytics to guide evidence-based decisions in customer and marketing intelligence, using forecasting, segmentation, and analytics to assess decision quality.

Leverage analytics governance to balance centralized, decentralized, and hybrid models, establish centers of excellence, and empower analytical liaisons to translate business problems into actionable pilots and data monetization.

Learn the key analytics functions, from self-service and campaign insights to journey and segment analytics, and how teams align data science, marketing, and IT for faster omni-channel decisions.

Explore marketing measurement options across direct and digital marketing, using control cells, champion challenger tests, and holdback samples to gauge incremental lift, revenue, and accounts.

Set up KPIs and metrics by mapping business questions to defined metrics and calculations, using a grid to track social followership, reach, awareness, and competitive benchmarks.

Examine randomness in sampling and scoring to estimate probability of response. See how the model lifts outcomes by focusing on top deciles with higher response rates.

Explore predictive analytics and data mining basics, including measurement, profiling, segmentation, pattern recognition, and regression, machine learning, and deep learning, to become a strong consumer of analytics for marketing.

Master optimization modeling, a constrained predictive analytics approach using genetic algorithms to target outcomes under multiple constraints, manage risk. Use historical campaigns and portfolio performance as inputs to inform plans.

Ask why a modeling technique was chosen and how the analytics fit the business problem, then assess performance with case statistic, root mean squared error, and top decile lift.

Ask data scientists about correlation versus causation, data sources, recency, and key variables. Review the objective function, dependent and independent variables, exclusions, compliance, scoring, and constrained optimization.

Explore segmentation as a multi-dimensional cube blending market research, behavioral analytics, and qualitative insights. Design 3-5 explainable segments with personas, messages, CRM scores, and predictive models to drive action.

Know your audience and the business problem to design visualizations showing the average sales per day in dollars and units, with clear line, bar, or pie charts.

Apply design principles in data visualization by using similarity and proximity to group related sales data, guiding perception and interpretation of omni-channel analytics.

Explore data monetization as a revenue opportunity by piloting data-driven products with a cross-functional team and strategic partnerships, using agile practices to turn pilots into living programs.

Explore how marketing automation drives omnichannel campaigns by unifying the marketing data model and orchestrating personalized offers across multiple channels. Understand the omnichannel data hub.

Showcase how marketing automation drives a mapped customer journey, delivering offers via online and offline channels, retargeting, push notifications, in-store cues, and privacy and permission compliant social feedback.

Explore how marketing automation uses drag-and-drop workflows and decision boxes with business rules to automate customer journeys, including offers, emails, calls, pausing rules, and data integration with Adobe Campaign.

Explore identity resolution and how offline and online signals connect in an identity graph. See how campaigns link direct mail, cookies, mobile IDs, and display ads.

Explore the three types of digital marketing—paid, owned, and earned—and their roles in campaigns, including search engine marketing, display ads, retargeting, and social earned media, with omnichannel data integration.

Web analytics answers questions about user attraction, page visits, and conversions to optimize the site experience. It stresses goals, KPI ownership, and measuring traffic sources, bounce rates, and returning visitors.

Explore tag management and its role in web analytics, detailing page tags, log files, cookies, and identity resolution to link online and offline customer data.

Explore tracking codes, pixel tags, and encrypted identifiers like destination URLs and customer IDs to link social campaigns, paid keywords, and AdWords data across web analytics and marketing analytics.

Explore how mobile marketing and analytics enable real-time, context-aware experiences through push notifications, geo-fencing, and app interactions, while measuring usage patterns and app functions for omni-channel insights.

Combine social listening with internal data and platforms to generate actionable insights that inform marketing, PR, and corporate strategies across the enterprise.

Explore privacy and preferences in permission marketing, including opt-in and opt-out, data storage and ownership, and the chief marketing officer's accountability for opt-in rates and net customer growth.

Explore the privacy continuum from consumer friendly data collection to more creepy uses, illustrated by the Target case, and how permissions, clickstream data, and location monitoring shape consent and compliance.

Explore how artificial intelligence powers marketing through big data, machine learning, and deep learning, with continuous learning from intelligent agents, chatbots, and virtual assistants.

Explore how big data fuels marketing AI with collaborative filtering and next-best offers across channels, powering dynamic content and automated lead scoring via Adobe Sensei and Salesforce Einstein.

Explore five future trends in customer intelligence and analytics, from big data monetization and cognitive automation to the rise of analytics leadership and internet of things driven cross-device marketing.

Explore designing and interpreting A/B tests in digital marketing, set goals with data science, ensure sufficient sample size for significance, and optimize offers using machine learning and armed bandits.