Generative AI: Marketing, Sales, CX & Revenue Growth

A dashboard is useful only if it helps you make a better decision. This lecture maps the main categories of AI-assisted market analysis tools and explains the different jobs they perform. You’ll learn how to select and integrate tools around your research needs instead of collecting platforms you never use.

- Analytics and visualization platforms

- Competitive intelligence tools

- Social listening and language analysis

- Tool selection and workflow integration

What if you could spot a change in demand before it appeared in your quarterly report? This lecture shows how predictive analytics uses historical and current data to estimate market trends and customer behavior. You’ll learn where forecasts can improve planning—and why predictions still require context and judgment.

- How predictive models find patterns

- Forecasting demand and consumer trends

- Using predictions in market decisions

- Data quality and forecast limitations

How does a global restaurant group make its marketing more relevant across multiple brands? This case study examines how Yum! Brands applied AI to customer engagement and marketing decisions. You’ll follow the business challenge, the approach, and the lessons that matter when moving from a technology pilot to everyday use.

- The marketing challenge across Yum! Brands

- AI-assisted personalization and offers

- Connecting customer insight to execution

- Results, rollout, and transferable lessons

Your competitors can reshape your market long before you notice a drop in sales. This lecture introduces competitive analysis as a structured way to understand alternatives, pressures, and opportunities. You’ll learn the foundations before using ChatGPT to speed up research in the lectures that follow.

- What competitive analysis is for

- Competitors, substitutes, and market context

- SWOT and Porter’s Five Forces

- Research challenges and ethical boundaries

Who are you really competing against—and where would you find the evidence? This lecture shows how ChatGPT can help organize public research and build a first-pass competitor map. You’ll also learn to check its findings against reliable sources before using them in strategy.

- Identifying direct and indirect competitors

- Working with websites and public information

- Organizing market signals and trends

- Verifying AI-generated research

The most useful competitive insight is often what a rival has overlooked. This lecture uses ChatGPT to examine competitors’ products, pricing, positioning, and strengths and weaknesses. You’ll learn how to turn those observations into testable opportunities rather than accept an AI-generated conclusion at face value.

- Competitor product and pricing analysis

- Messaging and positioning comparisons

- Strengths, weaknesses, and unmet needs

- Validating possible market gaps

AI can produce lots of observations; frameworks help you decide what they mean. This lecture applies SWOT and Porter’s Five Forces to competitor research with ChatGPT as a drafting and organizing assistant. You’ll learn how to challenge the output and use structured analysis to support clearer strategic choices.

- Building an evidence-based SWOT

- Assessing industry forces and competition

- Prompting ChatGPT for structured analysis

- Checking assumptions and drawing implications

What can a competitor’s ads and customer reviews reveal about your own opportunities? This lecture shows how ChatGPT can organize marketing signals and customer feedback into useful themes. You’ll learn to connect those themes to decisions about messaging, channels, and unmet customer needs.

- Competitor campaigns and messaging

- SEO and social media signals

- Reviews, complaints, and customer sentiment

- Turning insights into marketing actions

“How do we stack up?” deserves more than a guess in a meeting. This lecture shows how to use ChatGPT to build side-by-side competitor comparisons around meaningful criteria. You’ll learn to spot performance gaps, check the evidence, and turn a benchmark into an action plan.

- Choosing useful comparison criteria

- Side-by-side competitor benchmarking

- Identifying strengths and shortfalls

- Translating comparisons into priorities

Why does one ChatGPT research prompt produce a generic answer while another surfaces a useful insight? This lecture goes beyond simple questions to show how roles, context, structure, and follow-up prompts improve competitive analysis. You’ll learn to request deeper reasoning while still checking the model’s evidence and assumptions.

- Role and context in analytical prompts

- Multi-turn questioning and refinement

- Structured comparisons and output formats

- Managing complex requests and checking results

If a campaign has no agreed goal, how will you know whether it worked? This lecture shows how to turn broad intentions into measurable advertising objectives that support the business. You’ll learn to use established goal-setting frameworks while balancing immediate sales with long-term brand building.

- Awareness, leads, sales, and perception goals

- SMART advertising objectives

- DAGMAR and communication milestones

- The five M’s and strategic alignment

- Brand building versus short-term activation

The same ad will not mean the same thing to every customer. This lecture shows how audience research, segmentation, and buyer personas sharpen advertising decisions. You’ll learn to look beyond demographics and identify the behaviors and motivations that make a message relevant.

- Segmentation, targeting, and positioning

- Evidence-based buyer personas

- Customer behavior and data signals

- Deeper motivations and actionable insights

Why does one brand come to mind the moment a customer is ready to buy? This lecture explains how clear positioning and a credible value proposition give advertising its focus. You’ll learn to express what your brand offers, why it matters to your audience, and how to carry that promise across campaigns.

- Defining a distinct brand position

- Building a customer-centered value proposition

- Differentiating from alternatives

- Keeping advertising consistent with the promise

A memorable ad still needs to communicate the right idea. This lecture connects brand strategy to the creative brief, message structure, and campaign execution. You’ll learn how to make creative work engaging without losing sight of the audience or the business objective.

- Writing an effective creative brief

- Structuring messages with AIDA

- Translating insight into creative ideas

- Aligning execution with brand positioning

Even a strong message can fail if it reaches people in the wrong place or at the wrong time. This lecture examines how to choose advertising channels, build a media mix, and distribute spending around campaign goals. You’ll learn to use timing, reach, and performance data to make budget decisions more deliberate.

- Channel strengths and trade-offs

- Building a balanced media mix

- Allocating budget to campaign priorities

- Timing, frequency, and performance checks

Which part of your advertising budget is creating results? This lecture introduces the metrics and attribution choices that help answer that question. You’ll learn to distinguish revenue from profit and communicate campaign performance in terms stakeholders can use.

- ROAS, acquisition cost, and other key metrics

- Revenue, margin, and lifetime-value considerations

- Attribution approaches and their limits

- Reporting results and recommended actions

Why wait until a campaign ends to improve it? This lecture shows how testing, budget shifts, automation, and creative changes can improve ads while they are live. You’ll also learn how post-campaign reviews turn what worked—and what did not—into a better next launch.

- A/B and multivariate testing

- In-flight budget reallocation

- AI bidding and creative optimization

- Ad fatigue and creative rotation

- Post-mortems and reusable learnings

Can ChatGPT help you get past the blank page without producing generic content? This lecture shows how a stronger brief improves outlines, first drafts, headlines, social copy, and scripts. You’ll learn why fact-checking, brand voice, and human editing remain part of the job.

- Briefing ChatGPT with audience and context

- Using examples to guide style

- Drafts, headlines, social posts, and scripts

- Editorial review for quality and accuracy

What if one strong article could support a whole month of content? This lecture shows how ChatGPT helps adapt existing material for social channels, email, and other formats without losing the original message. You’ll also see where it can assist with SEO tasks and how to build a repeatable repurposing workflow.

- Platform-specific tone, length, and format

- Turning long-form content into multiple assets

- Titles, meta descriptions, and other SEO support

- Templates, editing, and avoiding repetition

Stuck staring at a blank content calendar? This lecture uses ChatGPT as a sounding board for topics, content themes, briefs, and early campaign ideas. You’ll learn how to use its speed and structure while leaving strategic priorities and final decisions with your team.

- Brainstorming topics and content pillars

- Expanding ideas for different audiences

- Structuring notes into outlines and briefs

- Sketching distribution and campaign messaging

Why does ChatGPT sometimes deliver exactly the wrong kind of copy? This lecture breaks effective marketing prompts into practical techniques you can use immediately. You’ll learn to supply context, work in steps, and guide tone and format without relying on technical-sounding instructions.

- Specific prompts and useful context

- Breaking complex tasks into steps

- Resetting or restating task context

- Examples for tone and structure

- Clear, consistent, natural language

Where should ChatGPT fit between a content brief and publication? This lecture maps an AI-assisted team workflow with clear roles, review points, and usage rules. You’ll learn how to move faster without weakening quality standards, privacy, or accountability.

- Draft-to-publication workflows

- Team policies and AI usage boundaries

- Sensitive-data and security safeguards

- Editorial QA and human approval

- Collaboration between writers and AI

Before publishing AI-assisted content, what should you check besides spelling? This lecture examines accuracy, originality, ownership, privacy, and disclosure risks. You’ll learn why a responsible content process needs source verification and a human who stands behind the final work.

- Hallucinations, bias, and fact-checking

- Originality, overlap, and attribution

- Copyright and human creative contribution

- Data privacy and legal compliance

- Transparency and editorial accountability

How can AI help a dating app refresh content at scale without losing its voice? This case study follows OkCupid’s use of ChatGPT to suggest new matching questions and the human editing that shaped the final experience. You’ll learn what the team measured, how it handled transparency, and what other content teams can take from the experiment.

- Spotting an audience and content opportunity

- Generating and editing matching questions

- Measuring in-app question engagement

- Human judgment and disclosure decisions

Today’s shopper might discover a product on social media and buy it in a store days later. This lecture maps the journey from first awareness through post-purchase loyalty across online and offline touchpoints. You’ll learn to use journey mapping to find friction and understand what customers need at each stage.

- Awareness, consideration, purchase, and post-purchase

- Digital and in-store customer touchpoints

- Customer journey maps and pain points

- Convenience, consistency, and personalization

Is being present on several channels the same as connecting them? This lecture distinguishes multichannel activity from a truly integrated shopping journey. You’ll learn what shared customer data, synchronized inventory, and consistent messaging make possible across app, website, and store.

- Multichannel versus omnichannel

- A unified customer view

- Inventory and order-system integration

- Consistent offers and communications

- Starbucks’ connected loyalty experience

What keeps customers coming back after their first purchase? This lecture looks at the engagement choices that build a lasting retail relationship, from relevant offers to community and service. You’ll learn how loyalty programs and win-back tactics fit alongside everyday interactions that make customers feel valued.

- Personalized customer engagement

- Points, tiers, and paid loyalty programs

- Email, SMS, and social interaction

- Community and belonging

- Cart recovery and customer win-back

What is your retail data telling you that your instincts might miss? This lecture connects core e-commerce metrics to funnel analysis, experimentation, and customer data integration. You’ll learn how to improve decisions while respecting the privacy of the people behind the numbers.

- Conversion rate, order value, and lifetime value

- Acquisition cost and cart abandonment

- Web analytics and A/B testing

- Connecting data across systems

- First-party data and privacy practices

How many sales are lost because a store is frustrating to use? This lecture examines the shopping experience from product discovery to the final checkout step. You’ll learn how mobile design, site speed, simpler forms, and trust cues can reduce friction and help visitors buy with confidence.

- Navigation, search, and product pages

- Mobile-first shopping and payments

- Page speed and site performance

- Guest checkout and clear total costs

- Reviews, return policies, and trust signals

What makes a digital store feel relevant to each shopper? This lecture explores how generative AI can adapt site content, marketing messages, and offers using customer context. You’ll learn where personalization helps—and why stale data or overly intrusive targeting can damage trust.

- Dynamic website and product content

- Personalized email and campaign messages

- Tailored promotions and timing

- Data quality, brand rules, and customer trust

Why does a great recommendation feel like help rather than an upsell? This lecture compares traditional recommendation methods with more contextual, conversational suggestions enabled by generative AI. You’ll learn how data, placement, and customer control shape a recommendation experience worth using.

- Collaborative and content-based filtering

- Hybrid recommendation systems

- Contextual and conversational discovery

- Product data and placement choices

- Stock, relevance, and trust guardrails

How do you write useful product copy for thousands of items without losing your brand voice? This lecture explores AI-generated descriptions, marketing variations, and SEO-supporting copy as first drafts. You’ll learn to give the model accurate product inputs and review every claim before it reaches shoppers.

- Product descriptions at catalog scale

- Headlines, emails, and other copy variants

- SEO-friendly structure and wording

- Prompts and brand-tone examples

- Human review for features and accuracy

What if a simple product photo could become several campaign-ready concepts? This lecture looks at AI-generated product settings, visual variations, video assets, and fashion imagery. You’ll learn to protect brand consistency and product truth while experimenting with faster creative production.

- AI-generated product backgrounds and variants

- Video and multimedia asset creation

- AI-generated models and virtual imagery

- Creative testing and visual brand rules

- Transparency and avoiding misrepresentation

Can a shopping assistant answer questions and guide a purchase in the same conversation? This lecture examines generative AI chatbots for customer support, product discovery, and sales assistance. You’ll learn why reliable product data, firm boundaries, and an easy handoff to a person matter as much as natural language.

- Conversational commerce and generative bots

- Product discovery and shopping guidance

- Support questions and conversion assistance

- Grounded answers and operational guardrails

- Transparency and human escalation

What is the difference between AI that drafts a sales email and AI that analyzes a sales call? This lecture introduces generative AI and conversation intelligence through everyday selling tasks. You’ll learn the key terms and see how call transcripts and CRM context can support faster, more informed follow-up.

- Generative AI and large language models

- Conversation intelligence versus customer-facing bots

- NLP, transcription, and call insights

- Prompts and sales content generation

- CRM integration and context

How much selling time disappears into preparing for the next call? This lecture shows how AI can assemble account plans, pre-call briefs, lead priorities, and suggested next actions. You’ll learn to treat those recommendations as decision support while checking sources and applying your own relationship judgment.

- AI-assisted account research and planning

- Prospect briefings and pre-call preparation

- Predictive lead scoring

- Next-best actions and deal risks

- Source checks and rep judgment

What did your last sales call reveal that your notes missed? This lecture explores how conversation intelligence transcribes meetings, drafts summaries, and flags moments that matter. You’ll learn to use individual and team-level call insights for follow-ups, coaching, and better sales decisions.

- Transcripts and post-call summaries

- Objections, competitor mentions, and buying cues

- Next steps and CRM action items

- Coaching from specific call moments

- Patterns across sales conversations

What if a rep received the right reminder during a difficult sales call, not afterward? This lecture explains how live AI guidance can surface playbook cues, useful resources, and objection-handling prompts. You’ll learn to configure support that helps reps stay present without turning the conversation into a scripted performance.

- Live call triggers and battlecards

- Handling pricing and competitor cues

- Playbook-aligned coaching prompts

- New-rep support and consistency

- Avoiding distraction and overreliance

How do you send tailored sales material quickly without making up details? This lecture examines AI-assisted emails, proposals, and decks grounded in CRM notes and approved product information. You’ll learn where the time savings come from and why a rep still owns accuracy, tone, and the final message.

- Personalized outreach and follow-up emails

- Proposals, one-pagers, and slide decks

- Grounding drafts in trusted sales data

- Accuracy and brand-voice review

- Human insight in the final draft

How can a sales team coach more calls without asking managers to review every minute? This lecture uses AI call analysis to identify skill gaps and shows how adaptive training and role-play simulations support practice. You’ll learn how immediate feedback complements—not replaces—a manager’s judgment.

- Call-based skill-gap analysis

- Learning from strong sales conversations

- Personalized training paths

- AI role-play and practice simulations

- Fast feedback and scalable coaching

What changes when an organization learns from its customer conversations instead of relying on scattered call notes? This case study follows Diligent’s rollout of conversation intelligence for sales coaching and enablement. You’ll see how adoption, shared call insights, and measurable behaviors helped the tool become part of the team’s workflow.

- Diligent’s coaching and onboarding challenge

- Choosing and phasing in Gong

- Using real calls in training and coaching

- Shared insights across business teams

- Tracking behavior and adoption

Can AI help revenue teams make faster choices about prices, plans, and processes? This lecture explores AI-assisted dynamic pricing, scenario planning, and revenue operations workflows. You’ll learn why better forecasts and less administrative friction matter—and where pricing fairness and human oversight must shape the decisions.

- Dynamic pricing and offer optimization

- Forecasting and what-if scenarios

- Contract, quote, rebate, and billing workflows

- Personalized bundles and margin decisions

- Fairness, transparency, and guardrails

What should happen when a customer needs help at midnight—or has a problem a bot cannot solve? This lecture examines generative AI chatbots, proactive service messages, and copilots for support agents. You’ll learn how to pair fast answers with verified information, customer choice, and a smooth human handoff.

- Always-available AI support assistants

- Proactive issue communication

- Agent copilots and suggested responses

- Accuracy, empathy, and escalation

Could AI make a busy drive-thru faster without making it feel less human? This case study examines Wendy’s FreshAI experiment in conversational ordering. You’ll follow the pilot, the real-world language and accuracy challenges, and the role employees played in improving the customer experience.

- The drive-thru ordering challenge

- Wendy’s FreshAI and its pilot rollout

- Handling custom orders and varied phrasing

- Speed, accuracy, and customer experience

- Employee feedback and human takeover

Why do promising AI pilots sometimes fail to produce business value? This lecture shows how to connect an initiative to a strategic goal, prioritize use cases, and plan for rollout. You’ll learn to measure both the AI system and the outcome it was built to improve.

- Aligning AI initiatives with business goals

- Scoping users, data, workflows, and risks

- Readiness and impact-versus-feasibility priorities

- Pilot-to-scale roadmaps

- Business and technical KPIs

What does it take to move an AI idea from whiteboard to working service? This lecture introduces the data, infrastructure, technical approaches, and platform choices behind implementation. You’ll learn how pilots, testing, and governance help teams build something useful and dependable.

- Clean data and scalable infrastructure

- Prompting, fine-tuning, and RAG

- Model, platform, and tool selection

- Pilots, experiments, and A/B tests

- Monitoring, security, and ownership

A working AI model is not enough if employees do not trust or know how to use it. This lecture addresses the people side of adoption alongside governance and ethical safeguards. You’ll learn how role-specific training, human review, clear ownership, and change communication support responsible use.

- Specialist, business, and leadership roles

- AI literacy and role-specific training

- Human-in-the-loop review

- Governance, risk, and accountability

- Communication and change management

What happens when AI moves from answering a customer to helping resolve the whole issue? This final lecture looks ahead to more autonomous, multimodal, and proactive customer experiences. You’ll explore how those possibilities may change marketing roles and customer expectations—and why thoughtful experimentation matters now.

- Agentic AI and coordinated actions

- Evolving marketing roles and automation

- Changing customer expectations

- Multimodal, edge, and immersive technology

- Early CX experiments and learning