Personalization & Customer Experience AI
Personalization earns the sale, and service decides whether the customer comes back. This course shows e-commerce, CRM, and customer-experience teams how recommenders, personalized campaigns, AI search, and service assistants actually work, and how to measure them honestly against a holdout. You'll learn to design handoffs, review AI-drafted replies, and apply consent, transparency, and fairness so personalization never turns creepy. You'll finish with a one-page customer experience playbook and a six-question checklist.
Course details
Duration
1h 22m 40s
Lectures
15
Video
1.5 hours
Quizzes
1
Level
Beginner
Course Description
Every personalization signal is a proxy for intent, and every AI-drafted reply goes out under the brand's name. This course covers recommendation engines, personalized campaigns, AI search, and AI-assisted service, and the privacy and fairness principles that keep all of it trustworthy.
What you'll learn
- How recommenders use signals, where they're blind, and what they amplify
- Personalized campaigns, search curation, and measuring lift against a holdout
- The creepy, wrong, and unfair failure modes, and the guardrails that catch them
- Service assistants, handoffs, escalation, and a review standard for every reply
- Multilingual and accessible service, and a scorecard built on resolution and error rate
- Customer data rules, opt-outs that propagate, and fairness in offers and pricing
Who it's for
E-commerce, CRM, marketing, and customer-experience professionals who run or oversee personalization and AI-assisted service.
15 short video lessons in three modules: Personalization, Service, and Privacy and Trust.
This course is educational and is not legal, privacy, or consumer-protection advice. Company policy and applicable law govern.
Module 1: Personalization
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1.1 How Recommendations Work at a Working LevelSignals, models, and their blind spots — what a recommendation engine actually does with what it has.
In this lesson:
- Name the signals a recommender uses and what each one can't tell you
- Distinguish collaborative, content, and generative recommendation models
- Read any recommendation like an operator: driver, blind spot, amplification, override
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1.2 Personalized Campaigns
Segments, content, timing — and how the brand voice survives when a model writes the variants.
In this lesson:
- Build a personalized campaign in four steps with a holdout
- Separate segments that help from segments that harm
- Keep the brand voice and the customer's comfort in every variant
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1.3 Search and Discovery
AI search, merchandising rules, and the human curation that remains.
In this lesson:
- Describe what AI search changes and where its failures move
- Combine merchandising rules with model ranking
- Run the weekly curation and correction routine
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1.4 Measuring Personalization Honestly
Lift, holdouts, and the metrics that mislead.
In this lesson:
- Measure lift against a randomized holdout
- Recognize the metrics that mislead and the ones that hold
- Report personalization results with segments, costs, and losers named
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1.5 When Personalization Goes Wrong
Creepy, wrong, or unfair — the three failure modes and the guardrails that catch them.
In this lesson:
- Recognize the three failure modes: creepy, wrong, unfair
- Install the guardrails that catch them before launch
- Apply the plain-words test to any personalization
Module 2: Service
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2.1 AI-Assisted Customer Service
Chat, suggested replies, knowledge retrieval — and the handoff to a person.
In this lesson:
- Map where AI sits in a service operation
- Run the handoff to a person without losing the customer
- Tell good assistant behavior from bad
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2.2 Returns, Complaints, and Escalations
Where judgment and empathy stay human — the split, the ladder, and the goodwill decision.
In this lesson:
- Split returns and complaints between AI and people
- Run the escalation ladder with named owners and clocks
- Make goodwill decisions as a person, with AI doing the assembly
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2.3 Tone and Brand in Every Reply
The review standard — accurate, on brand, human, honest, actionable, owned.
In this lesson:
- Apply the six-point review standard to any AI-drafted reply
- Turn a boilerplate draft into a reply that reads like a person who listened
- Keep the voice consistent through the guide, the prompt, the review, and the feedback
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2.4 Multilingual and Accessible Service
The gains, and the verification that keeps meaning intact across languages and formats.
In this lesson:
- Describe the gains from AI translation and accessibility tools
- Verify translated and accessible content, with a gate for high-stakes text
- Recognize where translation and accessibility tools fail and how to catch it
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2.5 Measuring Service Quality
Resolution, satisfaction, and error rates — the scorecard that tells the truth.
In this lesson:
- Build a service scorecard that separates resolved from contained
- Recognize the metrics that flatter an assistant and the ones that tell the truth
- Run a weekly error-rate review and act on it
Module 3: Privacy and Trust
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3.1 Customer Data Rules
Consent, purpose, minimization, retention — and what may enter which tool.
In this lesson:
- Apply the four data principles to personalization and service
- Decide what customer data may enter which tool
- Run the four questions before any new AI use of customer data
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3.2 Transparency and Choice
Telling customers what's personalized, and letting them opt out in a way that actually works.
In this lesson:
- Apply transparency in practice: label, explain, disclose, no dark patterns
- Design an opt-out that is findable, granular, honored everywhere, and verified
- Distinguish transparent personalization from manipulative personalization
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3.3 Fairness in Offers and Pricing
Principle-level consumer-protection concerns as of September 2026 — and the review before an offer ships.
In this lesson:
- Recognize where fairness concerns arise in personalized offers and pricing
- Separate acceptable differentiation from unfair treatment
- Run the fairness review before an offer or price variation ships
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3.4 Composite Cases
Three anonymized programs — the lift, the backlash, the fix.
In this lesson:
- Trace how a recommender, a campaign, and an assistant each went wrong after a strong start
- Identify the fix that let each program survive
- Name the pattern all three shared at the moment they failed
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3.5 Your Customer Experience Playbook
The one-page playbook, the checklist, and your next campaign; the finale.
In this lesson:
- Assemble the one-page playbook: personalize, serve, measure, protect
- Run the six-question checklist on any personalization or service change
- Complete Personalization & Customer Experience AI
Personalization & Customer Experience AI — Final Exam
Learn at your own pace
Study the materials at your convenience, you can always return to them to repeat
Certificate of completion
Complete every lesson and pass the final exam to earn a RiseCert certificate of completion you can download and share.
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Personalization & Customer Experience AI
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Got Questions? We've Got Answers!
Answers to the most common questions about this course.
What is the Personalization & Customer Experience AI course?
Personalization & Customer Experience AI is a RiseCert online course on recommendation engines, personalized campaigns, AI search, AI-assisted customer service, and the privacy and fairness principles that keep them trustworthy, built for e-commerce, CRM, and customer-experience teams. It includes 15 video lessons (about 1 hour 25 minutes) and a final exam.
Who should take a course on AI personalization and customer experience?
E-commerce, CRM, marketing, merchandising, and customer-experience professionals who run or oversee recommenders, personalized campaigns, site search, or service assistants, plus customer service leaders and quality reviewers. It also suits anyone who reports personalization or service results to leadership and wants those numbers to hold up.
How do recommendation engines work?
In this course's terms, a recommender combines five signals: behavior, context, catalog, declared preferences, and similarity. Collaborative models learn from what many customers did together and amplify what's popular; content and generative models handle new items better but can invent. Every signal is a proxy for intent, and none of them is intent.
How do you measure whether personalization actually works?
The course teaches measuring lift against a randomized holdout, with a metric chosen before launch, a duration that covers the returns window, and costs counted. Click-through, attributed revenue without a holdout, and open rates mislead. Incremental revenue, return-adjusted margin, repeat purchase, and unsubscribes reported beside every lift hold up.
What makes personalization creepy, and how do you prevent it?
The course names three failure modes: creepy, wrong, and unfair. Guardrails include no inferred sensitive-trait segments, signal decay and profile reset, suppression lists, fairness checks, and human escalation. One test catches most problems: could we explain this targeting to the customer on a screen, in plain words, and would they be glad we knew?
Does the course cover AI chatbots and customer service?
Yes. The Service module covers chat assistants, suggested replies, knowledge retrieval, and the handoff to a person; returns, complaints, and an escalation ladder; a six-point review standard for every reply; multilingual and accessible service with verification; and a service scorecard built around resolution and a weekly error-rate review.
What is the difference between containment and resolution in AI customer service?
Containment counts contacts an assistant handled without a person, which includes customers who simply gave up. Resolution counts problems actually solved and not reopened. The course teaches that containment rising alongside repeat contacts is deflection, not success, and pairs it with resolved-and-not-reopened and a sampled error rate.
Do I need technical or data science skills to take this course?
No. The course explains recommenders, AI search, and service assistants at a working level for operators, not engineers, and there is no coding. It focuses on what you can read, question, configure, review, and measure, such as tracing a recommendation to its signal or grading a sample of assistant replies.
What will I be able to do after completing the course?
You'll be able to read a recommendation like an operator, build campaigns with a holdout, run weekly search curation, measure lift honestly, install personalization guardrails, design service handoffs and escalations, review AI-drafted replies, verify translations, track error rates, apply customer data rules, design opt-outs that propagate, and run a fairness review before an offer ships.
Is the course content up to date?
Yes. The course describes recommender models, privacy expectations, and fairness scrutiny as of September 2026. Because tools and rules keep moving, it anchors on durable habits summed up in five words: consent, explain, verify, measure honestly, and treat everyone fairly. Your company's policies and counsel hold the current specifics.
How long does Personalization & Customer Experience AI take?
The course has about 1 hour 25 minutes of video across 15 lessons, plus a final exam. Lessons run about five to six minutes each and are organized into three modules: Personalization, Service, and Privacy and Trust. Most learners finish in two or three sittings.
Is the course online and self-paced?
Yes. The course is 100% online and self-paced, and your access does not expire. You can start, pause, and resume at any time on a computer, tablet, or phone with a modern web browser. There's nothing to install.
Is there a final exam?
Yes. The course ends with a 30-question multiple-choice final exam covering all three modules. You need a score of 80% or higher (24 of 30) to pass, and you can retake the exam as many times as you need. Finish all 15 lessons and pass the exam to complete the course and earn your certificate.
Do I get a certificate of completion?
Yes. After you complete all 15 lessons and pass the final exam, you can download a RiseCert certificate of completion from your account. It is a course completion certificate, not a license, professional certification, or continuing education (CE) certificate.
Does the course count for continuing education credit?
No. Personalization & Customer Experience AI is not currently approved for continuing education (CE, CEU, or CPE) credit by any state, licensing body, or accrediting organization. It is a practical training course for customer-experience professionals, and the certificate records course completion only.
Does the course recommend specific personalization or chatbot vendors?
No. The course is vendor-neutral. It covers the categories of tools CX teams use, including recommenders, campaign tools, AI search, and service assistants, and teaches the questions to ask of any of them, such as which signal drove a result, what the tool can't see, and whether a deletion request can reach it.
Is this course legal or privacy advice?
No. The course is educational and is not legal, privacy, or consumer-protection advice. It teaches principles such as consent, purpose, minimization, retention, transparency, and fairness at a working level, without naming specific laws. Your company's policies, counsel, and applicable law govern specific decisions.
Is personalized pricing legal or fair?
The course doesn't give legal answers, but it teaches a working test: acceptable differentiation is a published rule anyone could meet, such as loyalty rewards or volume discounts, while unfair treatment is an inference about who the customer is. As of September 2026, personalized pricing draws rising scrutiny, and proxy-based pricing goes to counsel.
How much does the course cost, and can I enroll my team?
The course costs $99 per learner as a one-time purchase, with no subscription or recurring fees. To enroll an e-commerce, CRM, or customer service team, contact RiseCert through the Contact Us page at risecert.org.
What is the refund policy?
All sales are final and no refunds are issued, as stated in the RiseCert Terms of Use. Please review the course description, lesson list, and these FAQs before purchasing, and contact RiseCert with any questions first.