Mobile Commerce Trends Report

What the Data Shows

1. Discovery: Where Shoppers First See and Respond to Ads

• TV still leads overall. It remains the most effective channel for motivating purchases, especially among shoppers over 45.
• Digital ads are nearly as strong. Internet ads perform just behind TV, and email + Facebook/Instagram are the only other channels that motivate at least one-third of shoppers.
• Social dominance among younger consumers. Instagram and TikTok ads rise sharply among younger respondents, often matching or surpassing traditional channels for early discovery.

2. Conversion Comfort: Where Shoppers Are Willing to Buy Immediately

• Direct-from-ad purchasing is now mainstream — for some. 

• Nearly 1 in 4 shoppers say they’re most comfortable buying directly from:
  • a social ad (24%)
  • a mobile app (23%)
  • an email (22%)
  • ~20–25% → none of the above (varies by cohort)

• But trust gaps remain. Only 5% feel comfortable purchasing through influencer content or SMS.
• And skepticism is rising. A similar share of respondents say they wouldn’t buy directly from any channel — a major trust signal that affects ad-to-checkout strategy.

3. Generational Segmentation: The Sharpest Divide in the Data

• Gen Z treats social like TV. Among Gen Z, Instagram and TikTok are as effective as TV at motivating purchases — a pattern not seen in any older cohort.
• Effectiveness declines with age. Social ad impact drops steadily after age 30, while TV and email rise significantly for Gen X and Boomers.
• Older shoppers require clarity and trust. Boomers convert best on channels that feel linear, informative, and familiar — TV and email remain their gold standard.

4. Luxury Buyers: The Most Distinctive Audience Segment

• Luxury and jewelry shoppers skew social, hard.
  They are far more likely than average shoppers to be influenced by:
  1. Instagram
  2. TikTok
  3. Facebook

• Visual-first environments matter. These shoppers respond strongly to premium imagery, vertical video, and aspirational creative.
• High-touch personalization influences conversion. Luxury buyers reward brands that match tone, exclusivity, and emotional resonance across every touchpoint.

Taken together, the data shows shoppers no longer respond to static personalization rules. Shoppers expect experiences that adapt in real time to who they are, what they’re doing, and why they’re there.

What It Means for Brands

Why Fragmented Attention Is Quietly Blowing Up Your CAC

The customer journey is fragmenting faster than most brands can keep up with. Shoppers now discover, evaluate, and convert across entirely different emotional environments, and the gap between those environments is widening.

• Rising CAC means misallocated spend is more expensive than ever.
• Creative mismatch (wrong tone, wrong channel, wrong moment) erodes trust instantly.
• Emotional resonance varies by audience, and the gap is widening: Gen Z needs social-first storytelling; Boomers want email and TV clarity.
• Luxury and premium shoppers demand high-touch personalization, and tolerate fewer friction points.

This shift is already playing out in how leading teams are applying AI across creative production and conversion paths.

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Case File → Alo: Narrative Continuity Across Every Channel

Alo reduces funnel drop-off by tailoring creative to the channel and audience (TikTok-native UGC for Gen Z; longer-form tutorials/BTS for older shoppers) while keeping the story consistent from ad → PDP so every click feels like a seamless continuation.

Steal this play:

• Pick one “hero SKU” per campaign and let it carry the story across social, landing, PDP, and retargeting.
• On PDP, lead with proof—not poetry: feature callouts, use-case images, and “this solves X” clarity above the fold.
• Keep the promise consistent: if the ad sells “the perfect travel bag,” the PDP should immediately show travel-specific proof (compartments, organization, carry-on fit).

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Case File → Calpak: Validates TikTok Hype with Proof-Led PDP Design

CALPAK turns TikTok discovery into conversion by rallying campaigns around a single hero product (the Luka Duffel) and using the PDP to instantly validate the hype with scannable utility proof (features, use cases, “why it’s worth it”) that makes checkout feel obvious.

Steal this play:

• Pick one “hero SKU” per campaign and let it carry the story across social, landing, PDP, and retargeting.
• On PDP, lead with proof—not poetry: feature callouts, use-case images, and “this solves X” clarity above the fold.
• Keep the promise consistent: if the ad sells “the perfect travel bag,” the PDP should immediately show travel-specific proof (compartments, organization, carry-on fit).

AI Actions to Try Now

Before we dive in, here's a simple way to think about how modern teams are applying AI across growth.

Most AI tactics fall into three practical layers, including how teams decide, how experiences feel, and how journeys stay connected across channels.

This structure is guided by Huang & Rust’s AI framework, but what matters here is execution. The actions below show how leading brands are using each layer to reduce waste, increase relevance, and convert intent faster.

How AI Cuts CAC and Lifts ROAS Across Channels

1. Predictive Channel Allocation: Stop Funding the Wrong Screens (Thinking AI)

Your audiences aren’t just scattered across different channels, they emotionally prefer different ad environments.

• Older shoppers respond strongest to TV and email, where messaging feels linear, informative, and familiar.
• Gen Z and luxury buyers convert on TikTok, Instagram, and vertical video, where storytelling feels fast, aspirational, and visual-first.

The challenge with this? Human teams are too slow to manually redistribute budget across fragmented channels, especially when performance shifts hourly during peak season.

Try This AI Tactic: 

Use predictive models to automatically identify the highest-converting channel by:

• Age (Gen Z vs Boomers)
• Category (luxury vs essentials)
• Intent level (browsers vs cart abandoners)
• Past engagement (click depth, session quality, add-to-carts)

This is why channel strategy alone isn’t enough. Emotional alignment now determines whether creative actually converts.

2.  Emotionally Aligned Creative: Matching the Mood, Not Just the Demographic (Feeling AI)

Different audiences don’t just prefer different channels, they prefer different emotional languages. 

AI can autonomously tailor tone, structure, and visual identity to match:

• Vertical, high-energy video for younger shoppers
• High-trust, clarity-first messaging for older audiences
• Aspirational, premium visuals for luxury buyers
• Problem/solution frameworks for essentials shoppers

This matters because the real conversion killer isn’t bad creative, it’s emotionally mismatched creative. What AI now does automatically is

• Adjusts ad tone to match demographic sentiment
• Autogenerates dozens of creative variants for testing
• Predicts which visuals will outperform before launch
• Localizes the “energy” of each ad to the culture of the platform (TikTok ≠ Reels ≠ TV ≠ email)

AI is making ads faster and emotionally accurate.

3.  Making Every Click Feel Like the Same Story (Narrative AI)

Today’s shopper doesn’t move through a linear funnel. They’re bouncing all over the place, across multiple channels and touchpoints.

A TikTok ad → an IG Story → your mobile app → an email → the checkout.

This is where most brands lose conversions: the tone, visuals, and messaging don’t match.

If the first touchpoint feels fun and modern, but the PDP feels generic or outdated, the buyer feels a story break and drops.

Since ~1 in 4 consumers is now comfortable buying directly from an ad, AI must ensure the entire journey feels like one continuous experience:

The ad →The landing page → The PDP → The app → The email → The push…

…all share the same emotional arc.

What AI does here:

• Carries over messaging from ad → PDP automatically
• Surfaces ad-referenced attributes in the product page modules
• Syncs visuals across paid + owned channels
• Adjusts tone dynamically based on the ad that brought the user in
• Creates persistent narrative “threads” across touchpoints

This unlocks a connected, story-driven buying experience that feels intentional, not stitched together.

What the Data Shows

1. Push Notification Behavior: High-Intent, High-Value, and Unlocked by AI Precision

• Only 25% of shoppers look at push notifications “all or most of the time.”
• 40% check them occasionally.
• 35% rarely or never look at push.
• Luxury shoppers are the strongest push-engagers across the entire dataset. They’re significantly more likely to open and act on push notifications compared to other segments.
• The most valuable push notifications are time-sensitive:
  
  • Shipping updates
  • Flash sales
  • Low-inventory alerts
• The least useful push messages are:
  
  • General brand updates (only 4%)
  • New arrivals
  • Generic product recommendations
• Push beats SMS for urgent moments. When urgency is high (flash sale, restock, countdown), shoppers slightly prefer push (41%) over SMS (31%).

2. Urgency Is Generational: Push for Gen Z, SMS for Gen X, Email for Boomers

• SMS preference grows with age. Gen X prefers SMS to push. Boomers prefer email → SMS → push in that order. 
  
  • SMS wins for medium urgency (restock, cart reminder, back-in-stock)

• Email is low-engagement, except for automated flows. Only 15% prefer email for time-sensitive updates, BUT abandoned-cart automations outperform campaigns by 30× RPR, proving “slow” channels still convert when automated + personalized, according to Klaviyo’s 2025 email marketing benchmarks report.
  
  • Email wins for low urgency (narrative, loyalty, education)

• Many shoppers avoid real-time messages altogether. Roughly a quarter prefer “none of the above” for buy-now prompts, signaling rising skepticism and a demand for trust, not just urgency.

These patterns make one thing clear: the challenge isn’t sending more messages, but it’s deciding which message belongs where and when. That orchestration problem is exactly where AI begins to change how modern teams operate.

What It Means for Brands

Why Sending More Messages Isn’t the Problem —Misrouting Them Is

The modern consumer is overwhelmed by notifications. They don’t simply ignore what feels irrelevant; they actively opt out.

This means a poorly routed message doesn’t just miss revenue; it erodes trust and future deliverability. But the real challenge is deeper than channel overload:

• Consumers don’t behave uniformly anymore. A Gen Z shopper can check their phone 100+ times a day; a Boomer checks email twice and ignores push entirely.
• Brands often assume their best messages should go to every channel. They shouldn’t.
• The emotional context of each channel matters. SMS feels personal. Push feels instant. Email feels thoughtful.
• The cost of a mismatch is rising. CAC is up, inbox fatigue is real, and the tolerance for “batch-and-blast” messaging is gone.
  

In an era where shoppers jump from TikTok ad → SMS → email → brand app → checkout, the brands that win aren’t the ones who message the most because they’re the ones who message correctly.

That’s why AI must take over the orchestration layer.

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Case File → Princess Polly: The Blueprint for Converting Social Demand into Owned Revenue

Princess Polly turns social discovery into an owned, high-intent customer loop. They use app-first incentives (early access + app-exclusive discounts) to convert hype quickly, then rely on push to keep customers engaged and buying. 

Why this works: Princess Polly captures demand at peak hype with app-first access, then turns that moment into an owned retention loop—using push to re-engage for free and positioning the app as a “VIP lane” customers actually want to enter.

They were able to send thousands of push notifications without paying for every touchpoint. And the best part? 28% of total revenue comes from app and they see a 27% higher CVR on app vs. mobile website

Steal this play:

• Tie drops and promotions to app-first access (early access + app-only perks).
• Use push to extend the story after social discovery (launch reminders, back-in-stock, VIP access).
• Build the app as the “VIP lane” so customers choose it as their fastest path to checkout.

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Case File → Sephora: Orchestrated Messaging That Doesn’t Rely on One Channel

Sephora SEA proves what enterprise teams already know: the best messaging doesn’t “pick a channel,” it stacks channels by purpose. To drive adoption of an AR feature, they used a coordinated sequence of push notifications + in-app messaging + Content Cards (persistent in-app content), paired with a short step-by-step video that reduced friction and made the action obvious.

Why this works: Push notifications create the moment. In-app messaging delivers the guidance. Content Cards catch everyone who didn’t click, without spamming or relying on a second opt-in.

The result: Sephora SEA drove a +28% increase in user adoption and a +48% lift in traffic to the AR experience.

Steal this play:

• Use push for urgency, then backstop with a persistent in-app module so the message is still there when the shopper returns.
• Pair the message with “do-this-next” creative (short video, 3 steps, or a single CTA) to reduce cognitive load.

Segment by behavior, not just demographics (recent browsers, VIPs, category affinity), so every send feels like service, and not noise.

AI Actions to Try Now

How AI Orchestrates Messaging So You Don’t Burn Out Your List

1. Predictive Routing: Put Each Message in the Channel That Can Actually Win (Thinking AI)

AI can now evaluate which channel a shopper is most likely to respond to based on:

• Age and generational behavior
• Category affinity (luxury vs essentials)
• Session quality
• Past engagement with push/SMS/email
• Device usage
• Urgency level
• Purchase intent signals
  

Instead of sending everything everywhere, AI reroutes each message to the channel that has the highest conversion probability for that individual shopper.

As channels fragment and expectations rise, the brands pulling ahead aren’t layering AI on top of broken processes, they’re weaving it directly into how work gets done. From content and localization to merchandising and internal operations, AI is becoming infrastructure. Simon Hamblin, Co-Founder & CEO of Fusefabric, captures where this shift is heading:

2. Emotional Native: Writing Push, SMS, and Email in Their Native Languages (Feeling AI)

Every channel has its own emotional code, and AI is now able to write natively for each environment:

• Push: short, urgent, energetic
• SMS: clear, concise value
• Email: narrative, trust-focused
• Luxury segments: premium, restrained tone
• Younger segments: fast, casual, emoji-friendly
  

AI models can even adjust tone by age, urgency, time of day, and previous interactions. This ensures that the message feels like it was written for the shopper, and not blasted to everyone.

3. Threaded Journeys: One Story, Many Channels (Narrative AI)

Consumers rarely convert from the first message. What converts is a cohesive story across channels. 

Most brands break the narrative without realizing it:

• TikTok ad: playful
• Push: generic
• Email: corporate
• PDP: factual
• SMS: urgent
  

AI fixes this by ensuring the tone, visuals, and messaging carry from the ad → the push → the email → the checkout page. It becomes one cohesive journey instead of a series of disconnected nudges.

This alone materially lifts conversion because it reduces cognitive dissonance, which is the silent killer of mobile commerce.

What this data makes clear is that messaging performance isn’t about volume, but it’s about timing, routing, and relevance. As Nicole Cuillo, Senior Product Marketing Manager at Attentive, puts it, the teams pulling ahead are redesigning how messaging decisions get made in the first place.

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Messaging Framework To Try (With AI Prompts)

To make this actionable, the data rolls up into a simple routing framework. Different generations don’t just prefer different channels — they respond to different signals, levels of urgency, and value exchanges. This table summarizes how leading teams align message type, channel, and timing by audience.

Once channel routing and urgency are clear, AI’s real value shows up in execution. The prompts below are designed to help teams generate on-brand, context-aware messages for different urgency levels, without defaulting to batch-and-blast or one-size-fits-all copy.

Each prompt can be adapted for your brand voice, product category, and channel mix, and is intentionally structured so AI handles speed and variation while humans retain creative control.

High Urgency

Best for: Flash sales, limited-time offers, low-inventory alerts, drops
Message strategy: Short, time-sensitive, clarity first

AI Prompt:

You are a brand copywriter for an ecommerce company selling [product type].

Write 3 short-form message options for a limited-time sale that ends tonight.

Requirements:

• Use urgency-driven language (e.g., “Only hours left,” “Last chance,” “Almost gone”)
• Include one clear CTA to buy
• Keep each message under 25 words
• Maintain a tone that is energetic and high-clarity, but not spammy or pushy

Generate:

• 1 version for a push notification
• 1 version for SMS
• 1 version for a social caption

Medium Urgency

Best for: Restock alerts, personalized promotions, cart reminders
Message strategy: Contextual, helpful, light FOMO

AI Prompt

You are writing for an ecommerce brand that sells [product category].

Create 2 message variations for a cart abandonment or restock alert.

Audience:

• Returning customer
• Added items to cart but did not check out

Requirements:

• Tone: friendly, helpful, low-pressure, with subtle urgency
• Emphasize relevance and usefulness over pressure
• Include personalization cues (e.g., product viewed, size, category)

Deliver:

• Email version with:
  • Personalized subject line
  • 50–60 word body
  • One clear CTA button
• SMS version under 25 words using first-name personalization

Low Urgency

Best for: Loyalty rewards, education, community updates, brand storytelling
Message strategy: Relationship-building, trust-first, long-term value

AI Prompt

Act as a retention marketer for a DTC brand focused on loyalty and brand affinity.

Write 3 message options for a loyalty email celebrating a customer’s 6-month anniversary.

Requirements:

• Include a short milestone moment (e.g., “You’ve been with us for 6 months”)
• Tone: warm, genuine, community-oriented
• Avoid hard-selling language
• Focus on appreciation, trust, and long-term value

Format each option with:

• Subject line
• Preview text
• ~80-word email body
• CTA that invites engagement (like “Explore your reward,” “See what’s new for you”)

How to Use These Prompts

These prompts work best when paired with:

• Behavioral segmentation (recent activity, intent signals, lifecycle stage)
• Channel-specific delivery rules
• Clear guardrails around brand voice and frequency

AI handles speed and variation. Humans set strategy, tone, and trust.

What the Data Shows

1. BOGO Wins and Discount Dependency Is Shrinking

• 60% shoppers place BOGO in their top three; one-third rank it first. It also earns the strongest average rank (3.12) of all offers.
• Percentage-based discounts still convert, but the minimum effective discount is falling:
  • 70% convert at 30% off or less
  • 50% convert at 25% or less (up from 40% last year)
  • Only 10% require 40%+
• What this signals: Shoppers are becoming less discount-dependent. Value framing now matters more than depth. Luxury shoppers reinforce this shift. They convert with smaller incentives, particularly in high-margin categories — making them one of the most margin-efficient segments when promotions are personalized.

2. Value-Based Offers Beat Savings Alone

• Not all incentives are created equal. Promotions that feel like value outperform those that simply reduce price.
• Underperforming offers:
  • Dollar-off discounts (worst-performing across all age groups)
  • Gift with purchase (lowest-ranked overall)
  • Free shipping (matters primarily to Gen X and Boomers)

• Community features and brand events, while valuable for brand-building, rank low as purchase motivators — less than 10% of shoppers list them as important during decision-making moments.‍
• What this signals: Shoppers don’t want more promotions. They want better value signals — access, recognition, and perceived advantage.

• Top-performing value signals:
  • Exclusive perks and rewards, selected by 67% of shoppers as most desirable
  • Early access and access-based incentives among loyalty-driven buyers

3. Generational + Gender Splits Define Promotion Strategy

• Gen Z and Millennials over-index on exclusivity, early access, and limited drops.
• Gen X and Boomers prefer traditional value signals like free shipping and predictable percentage discounts.
• Early access ranks especially high among Millennials, Gen Z, and luxury buyers
• Across all demographics:
  • 90% of shoppers say loyalty programs are at least “somewhat important.
  • ~50% say “extremely important.”
  • Only 6% say loyalty isn’t important. Loyalty expectations are becoming universal.
• Gender differences add nuance:
  • Female shoppers prefer exclusive perks and customer reviews/photos more than men.
  • Male shoppers show more interest than women in less common loyalty features (limited editions, experimental perks).
• What this signals: Loyalty expectations are no longer optional — they’re universal. The winning strategy isn’t one-size-fits-all discounts, but personalized incentives aligned to sensitivity, segment, and intent.

As discounts lose their power, trust is stepping in to do the heavy lifting.

Shoppers aren’t just asking “Is this on sale?”, they’re asking “Is this worth it?” And increasingly, they’re looking for that answer in reviews, photos, and real customer language, not brand claims.

For leading brands, reviews are no longer just conversion assets. They’re becoming one of the highest-signal inputs for AI-powered discovery, search, and recommendation engines both on-site and off.

Here’s how forward-thinking teams are turning customer feedback into a competitive advantage.

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Steal this play: Treat your reviews as a product: prompt for depth, summarize with AI, sort intelligently, then syndicate the best answers into your offsite content strategy.

What leading brands are doing right now (in the weeds):
Leading teams use tools like Yotpo Smart Prompts to collect longer, image-rich, topic-dense reviews, then apply AI Review Summaries and Smart Sorting so the most useful content surfaces instantly on PDPs. They also activate loyal customers and communities to generate additional, high-signal content built for offsite discovery (like Reddit-style threads, Q&A posts, blog FAQs), often incentivized with rewards that compound retention and CLTV.

Why this matters now:
Answer engines are becoming a real shopping surface. Some estimates suggest ChatGPT already accounts for ~8% of US search (up ~3× since the start of the year) and shopping is a top use case, reinforced by OpenAI’s recent shopping-related launches and Shopify tie-ins.

4. Luxury Shoppers Need Smaller Incentives

• High-end categories convert with less discounting, which is ideal for enterprise brands with margin constraints.
• Luxury shoppers convert with: smaller discounts, higher responsiveness to exclusivity, and stronger loyalty program engagement
• They are also more influenced by early access than other segments.
  → This segment is highly margin-efficient if promotions are personalized.

5. Reviews & Transparency Are Critical Trust Builders

• Customer reviews/photos rank among the top requested features shoppers want from their favorite brands.

• This is especially true for female shoppers, who rate UGC as highly influential.

→ Shoppers want proof and transparency over deeper discounts.

Value doesn’t end when the order is placed.

As loyalty expectations rise, post-purchase has quietly become one of the most important (and overlooked) opportunities to reinforce trust, reduce friction, and drive repeat revenue. The brands pulling ahead aren’t reacting faster when things go wrong; they’re anticipating needs before customers ever ask.

Here’s what that evolution looks like in practice.

What It Means for Brands

Why Blanket Discounts Are Quietly Taxing Your Margins

Shoppers aren’t just deal-driven anymore, they’re value-driven, margin-aware, and increasingly strategic about where and when they spend. And that shift has massive implications for brands.

The old promo playbook “go louder, discount deeper, hope for volume” no longer works in a world where:

• CAC is rising and margin pressure is at an all-time high
• Consumers compare prices instantly across apps, marketplaces, and social
• Emotional trust determines where shoppers convert, not the size of the markdown
• Loyalty expectations have gone mainstream (90% say rewards matter)

Discounting used to be a lever you could pull freely. Now it’s a liability if misused.

The deeper issue is that not all customers need or respond to the same incentives, and blanket promotions create more problems than they solve:

• Luxury and high-LTV cohorts convert with less discounting
• Younger shoppers chase exclusivity, not raw savings
• Older shoppers are more deal-sensitive but less impulse-driven
• Enterprise brands stand to lose margin and brand equity if they over-incentivize

The promotional landscape has fragmented just like the media landscape, and the cost of misalignment is rising. A poorly timed discount doesn’t just erode margin; it trains customers to wait for deeper cuts, sabotages LTV, and compresses contribution margin for quarters to come.

And in an economy where shoppers happily toggle between Amazon, TikTok, email, and your mobile app before purchasing, the brands that win won’t be the ones offering the biggest discounts.

They’ll be the ones offering the right value, to the right customer, at the exact moment it matters, without destroying profitability.

That’s why AI has to run point on promotions.

A​​s discount efficiency declines, bundling is emerging as one of the most reliable ways to increase order value without sacrificing margin.

The challenge isn’t whether bundles work — it’s knowing which combinations actually resonate with shoppers. For years, brands have relied on instinct or imitation. Now, data and AI are changing how bundles are built, priced, and deployed.

Here’s how teams are turning bundling into a repeatable profit lever:

Steal this play: Use AI to mine your order data for bundle pairings, then launch 2–3 high-confidence bundles (with a clear value story) instead of guessing.

What to do (AI prompt you can copy/paste):
Prompt: Analyze this anonymized spreadsheet of 50 recent orders.

1. Identify products frequently purchased together in the same order.
2. Identify items commonly bought as a second purchase within 30 days of the first.
3. Flag high-frequency or high-value pairings and any patterns by category/price point.
   Output: Recommend 3 bundle ideas formatted as: Bundle Name → Items → Price → Why It Works (include a short note on where to promote each bundle: PDP, cart, post-purchase, email/SMS/push).

Why this matters:
Order data turns bundling from a creative guess into a repeatable profit lever: helping you decide what to bundle, how to position the offer, and where to deploy it for maximum AOV and margin.

AI is the only way to:

• Prevent overdiscounting
• Personalize incentives by sensitivity, segment, and behavior
• Protect margins while increasing AOV
• Turn promotions into retention engines instead of one-time hits

In 2026, smart promotions aren’t a marketing tactic, they’re a financial strategy. And AI is the operating system that makes them sustainable.

Case File → Westman Atelier: Value-Stacked Promotions Done Right

Westman Atelier makes promos feel premium by mixing bundles, percentage-off offers, dollar-offs, and scarcity cues like “online exclusive” to appeal to both value seekers and premium buyers — without leaning into deep discounting. This way shoppers feel like they’re getting more, and not just paying less. 

Steal this play:

• Lead with “sets,” not sitewide discounts. Build 2–4 curated “starter kits” (like theSignature Skin Duo, Essentials Edit) and show the value math right on the tile: price + “$X value.”
• Use “online exclusive” as the incentive. Make at least one hero set exclusive so the offer is access/status (and doesn’t train customers to wait for markdowns).
• Create a savings ladder. Offer good/better/best sets (“duo” → “trio” → “full line-up”) so customers self-upgrade without needing deeper discounts.‍
• Make gifting a value wrapper. Repackage sets into seasonal edits/gift guides so “promotion” reads as curation (premium) rather than price cutting.

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Case File → Kith: Exclusivity-First Promotions (No Margin Meltdown)

Kith turns “promotions” into repeat-value by using loyalty and app access (early access to drops, exclusive drawings, and member-only experiences) so the incentive is status and scarcity, not deeper discounts.

Steal this play:

• Try offers that build FOMO: early access windows, member-only drops, VIP waitlists.
• Use “lottery” mechanics (drawings) for hype products to drive repeat check-ins without discounting.
• Put the best perks behind an owned surface (app / logged-in loyalty) so the promo compounds LTV, not CAC.

AI Actions to Try Now

How AI Finds the Minimum Effective Discount for Every Customer

1. Predictive Discounting: Hitting the Sweet Spot, Not the Floor (Thinking AI)

AI determines the exact incentive needed to convert each shopper, and suppresses unnecessary discounts.

AI identifies:

• Discount sensitivity
• Past purchase margin
• Price ceilings and thresholds per customer
• Who should receive perks instead of percentage cuts
• When to show no discount at all
  

Outcome: Higher contribution margin + higher AOV + no brand dilution.

2. Dynamic Loyalty: Letting AI Decide When to Surprise and Delight

AI analyzes user behavior to dynamically adjust loyalty levers:

• Double points during churn windows
• Tier upgrades triggered by predicted CLTV
• Personalized milestone rewards
• Automated early-access eligibility
• VIP-specific offers during peak weeks

For enterprise brands: This builds long-term revenue without eroding margins.

3. Offer Suppression: When ‘No Discount’ Is the Smartest Move (Mechanical AI)

Not every shopper needs a deal. AI auto-suppresses offers to users who:

• Already have high purchase intent
• Are loyalty repeaters
• Engage deeply with content
• Convert from non-discount channels (email, social, push)
  

Result:
Revenue → up
Margin → protected
Discount exposure → controlled