Ecommerce teams can now see almost everything their competitors are putting into the market.
They can browse active ads, study product pages, monitor discounts, watch new product launches, compare landing pages, track creative formats, and observe which channels competitors appear to be using.
Access to information is no longer the main problem.
The problem is knowing what the information means.
A folder containing hundreds of competitor ads may look useful, but it does not automatically tell a team which ideas are relevant, why a competitor made a particular creative decision, or whether the same approach would work for a business with different margins, brand awareness, distribution, and growth objectives.
Ecommerce ad intelligence is the process of analyzing competitor distribution, advertising, products, creative, offers, landing pages, channels, and performance signals to improve ecommerce marketing decisions.
The goal is not to collect more screenshots.
The goal is to make better decisions about:
- Which competitors are worth studying
- Which creative patterns deserve attention
- Which offers are shaping customer expectations
- Which products competitors are prioritizing
- Which landing-page ideas may be worth testing
- Which creative changes should be tied to specific metrics
- Which observations are relevant to your own economics
The most useful ecommerce advertising intelligence does not begin with the question:
What ad should we copy?
It begins with a more disciplined set of questions: Which competitor is comparable to us? Which distribution system appears to be working for them? Why might this creative decision exist? Which metric would prove whether the underlying idea is relevant to our business?
What Is Ecommerce Ad Intelligence?
Ecommerce ad intelligence is the structured practice of turning visible market activity into better advertising and growth decisions.
It includes the analysis of:
- Competitor ads
- Distribution channels
- Product positioning
- Creative formats
- Hooks and messages
- Offers and promotions
- Product and landing pages
- Merchandising activity
- Seasonal campaigns
- Internal advertising performance
- Customer acquisition economics
General ad intelligence can apply to many industries. Ecommerce ad intelligence views advertising through the specific realities of product-based businesses.
Those realities include:
- Product margins
- Average order value
- Returns
- Inventory
- Shipping costs
- Repeat purchase
- Discounting
- Product variants
- Merchandising
- Customer acquisition cost
- Contribution margin
- Product-page conversion
A competitor's ad may look effective while being commercially unsuitable for your business.
A heavily funded company may accept an unprofitable first purchase because it expects a long customer lifetime value. A bootstrapped brand may need contribution margin on the first order. A subscription business may tolerate a longer payback period than a company selling a one-time-purchase product. A legacy brand may convert with simple creative because buyers already know its name.
The creative cannot be separated from the business behind it.
For a broader explanation of the discipline, read the complete guide to ads intelligence.
Why Ecommerce Brands Need Ad Intelligence
Ecommerce advertising has become easier to produce and harder to evaluate.
Creative teams can generate more videos, images, hooks, and variations than before. Advertising platforms automate more targeting and delivery. Competitors can launch products, offers, and campaigns quickly. Customers see more advertising across more channels.
Producing more creative does not solve the problem if the underlying inputs are weak.
Ecommerce ad intelligence helps improve those inputs.
Creative volume is increasing
Many DTC teams need a continuous supply of new creative.
The challenge is not simply to create more ads. It is to understand which customer problems, product benefits, proof types, formats, and offers deserve further testing.
Competitor research can contribute useful hypotheses, but only when it is filtered through your own positioning and performance data.
Offers shape customer expectations
Customers do not evaluate an offer in isolation.
They may already be seeing:
- Percentage discounts
- Bundles
- Free shipping
- Free gifts
- Guarantees
- Subscription savings
- Limited editions
- Buy-more-save-more promotions
- Seasonal urgency
A brand needs to understand whether it should compete directly with these patterns, avoid them, or create a more differentiated form of value.
The product page is part of the ad
For ecommerce brands, the advertisement is only the first part of the experience.
An ad may attract attention, but the product page must continue the promise, explain the value, address objections, provide proof, and make the purchase feel safe.
A strong ecommerce ad intelligence process studies the complete journey from impression to product page and checkout.
Competitor activity changes quickly
Ecommerce competitors can rapidly change:
- Hero products
- Pricing
- Bundles
- Landing pages
- Seasonal messages
- Creative formats
- Channel investment
- Product positioning
- Inventory-driven promotions
A recurring intelligence process helps a team notice meaningful changes without reacting to every isolated ad.
Start With Business Context Before Studying Ads
The first step is not opening Meta Ad Library.
It is understanding the business behind the ads.
Understand the competitor's distribution
Before analyzing thumbnails, hooks, or video structures, determine how the competitor appears to create and capture demand.
Ask:
- Are they acquiring customers primarily through Meta?
- Are they capturing existing demand through Google Search or Shopping?
- Are they using TikTok or YouTube to create awareness?
- Are they dependent on Amazon or another marketplace?
- Are creators, influencers, or affiliates important?
- Do they have retail or wholesale distribution?
- Do they benefit from a large email database?
- Are branded searches doing much of the conversion work?
- Is paid advertising the main growth engine or only one part of a wider system?
This distinction matters because the visible ad may not be the primary reason the business is growing.
A brand may run relatively simple creative while benefiting from retail distribution, customer loyalty, word of mouth, branded search, marketplace visibility, or years of accumulated awareness.
Choose comparable competitors
The biggest company in the category is not always the most useful company to study.
Legacy brands with high brand equity often operate under different conditions from younger ecommerce businesses.
They may benefit from:
- Decades of brand awareness
- Retail visibility
- Large customer databases
- Strong branded search demand
- Celebrity associations
- Public relations
- Existing trust
- High product familiarity
- Customer loyalty
- Wider distribution
A simple image featuring a famous product may perform because customers already recognize the brand.
The same image could fail for an unknown DTC company because it does not explain the product, establish trust, or address a buying objection.
The goal is not to study the biggest competitor.
The goal is to study the most relevant competitor.
Prioritize businesses that are reasonably comparable in:
- Brand maturity
- Product category
- Price point
- Customer awareness
- Geographic market
- Acquisition model
- Funding position
- Distribution
- Growth stage
- Customer economics
Legacy brands can still be useful for understanding long-term category positioning, merchandising, and brand-building. They are simply less reliable as direct creative benchmarks.
Match their goals and economics to yours
A bootstrapped ecommerce brand should not blindly extract advertising intelligence from a company deliberately burning cash for growth.
A heavily funded competitor may tolerate:
- High customer acquisition costs
- Long payback periods
- Unprofitable first orders
- Aggressive discounting
- Free products
- Large creator budgets
- Expensive production
- Losses in exchange for market share
A bootstrapped business may require:
- First-order contribution margin
- Short payback periods
- Controlled discounting
- Sustainable acquisition
- Efficient inventory turns
- Lower production costs
- Strong cash flow
The visible ad may look attractive while being completely unsuitable for your financial model.
Before using a competitor as a learning source, ask:
- Are they optimizing for profit, revenue, market share, or fundraising?
- Can they accept losses that we cannot?
- Do they have higher margins?
- Do they benefit from repeat purchase?
- Are they monetizing customers through subscriptions or additional products?
- Are they advertising a low-margin hero product and earning profit elsewhere?
- Can they afford a much longer payback period?
A competitor's execution becomes useful only after you understand the objective and economics that may support it.
The Seven Signals Ecommerce Brands Should Track
A strong ecommerce competitor-ad tracking system should examine more than creative.
Distribution and channel signals
Start by mapping where the competitor appears to create and capture demand.
- Meta
- TikTok
- Google Search
- Google Shopping
- YouTube
- Amazon
- Other marketplaces
- Influencers
- Affiliates
- Retail
- Wholesale
- Branded search
- Email and retention channels
Different channels perform different jobs. Meta and TikTok may introduce a product to new customers. Google Search and Shopping may capture buyers who already have intent. YouTube may educate customers about a more considered purchase. Marketplaces may generate trust and comparison activity. Email may produce a large share of repeat revenue.
Do not assume the most visible channel is the most commercially important one.
Competitor creative
Once the distribution context is clear, examine the creative itself.
- Static images
- Video ads
- UGC
- Founder-led ads
- Product demonstrations
- Testimonials
- Comparison ads
- Lifestyle imagery
- Unboxing
- Review-led creative
- Before-and-after formats
- Educational ads
The objective is not to save every execution. It is to identify how competitors are trying to earn attention, explain the product, prove value, and move customers toward purchase.
Hooks and messaging
Track the underlying idea that introduces the ad.
- Problem-led
- Benefit-led
- Price-led
- Quality-led
- Comparison-led
- Objection-led
- Identity-led
- Proof-led
- Founder-story-led
- Urgency-led
Do not stop at labeling the hook. Ask why that hook may have been selected. A competitor may lead with price because the category is highly comparison-driven. It may lead with quality because buyers distrust cheaper alternatives. It may lead with fit because sizing uncertainty is blocking purchase.
The useful lesson is the customer issue behind the message, not the sentence itself.
Offers and promotions
Track how competitors frame value.
- Percentage discounts
- Bundles
- Free shipping
- Free gifts
- Guarantees
- Trials
- Subscription savings
- Seasonal promotions
- Limited editions
- Buy-more-save-more incentives
Offers can reveal what a category believes it needs to do to convert customers. A market full of discounts may indicate price sensitivity. It may also indicate that discounting is saturated and a bundle, guarantee, or value-led offer could create more differentiation.
Product and merchandising signals
Ecommerce ad intelligence should include which products competitors are choosing to support with advertising.
- Hero products
- Bestsellers
- New launches
- Bundles
- Variants
- Colors
- Styles
- Collections
- Inventory-driven campaigns
- Upsells
- Cross-sells
- Products appearing repeatedly in paid media
Advertising activity can reveal which products a company considers strategically important. A product receiving continuous support may be a strong acquisition product, a high-margin item, an inventory priority, or an entry point into a wider customer journey.
The visible activity does not prove which explanation is correct, but it provides a useful hypothesis.
Landing pages and product pages
Study what happens after the click.
- Headline
- Hero image
- Benefit hierarchy
- Social proof
- Reviews
- Guarantees
- Comparison tables
- Product details
- Materials or ingredients
- Sizing and fit
- Delivery
- Returns
- Bundle presentation
- Checkout path
Ask whether the page continues the promise made in the ad. If the creative focuses on craftsmanship, does the product page show construction details? If the ad addresses fit, does the page provide sizing support? If the offer is a bundle, is the value clear after the click?
A disconnect between the ad and the page can weaken conversion even when the creative earns attention.
Timing and seasonality
Track when competitors change their advertising.
- Holidays
- Product launches
- Sale periods
- Gifting occasions
- Weather changes
- Back-to-school
- Black Friday
- Inventory clearance
- Regional differences
- Category-specific seasons
Timing changes the meaning of creative and offers. A discount during an inventory-clearance period does not necessarily represent the normal acquisition strategy. A gifting campaign may require different messages, bundles, and landing pages than an everyday product campaign.
The Ecommerce Ad Intelligence Decision Framework
The following process turns observations into disciplined tests.
Step 1: Define your objective and economics
Begin with the decision you need to make.
- Which creative angle should we test?
- Which product should receive more advertising support?
- Should we test a bundle or discount?
- Why are viewers dropping during the opening seconds?
- Which objection should the product page address?
- Which channel deserves deeper investigation?
Then clarify your constraints:
- Target customer acquisition cost
- Contribution margin
- Payback period
- Average order value
- Return rate
- Repeat-purchase potential
- Available production budget
Without this context, competitor activity can push the team toward tactics that do not fit the business.
Step 2: Select comparable competitors
Choose five to ten useful learning sources.
Include a mix of:
- Direct competitors
- Similar-stage brands
- Premium alternatives
- Lower-priced alternatives
- Fast-growing challengers
- Brands targeting the same customer differently
Avoid building a list dominated by legacy brands or businesses with completely different economics.
Step 3: Map their distribution channels
Create a simple distribution map for each competitor. Record where they appear to be active and what role each channel may perform.
| Channel | Possible role |
|---|---|
| Meta | Demand creation and retargeting |
| Google Search | Capturing existing intent |
| Google Shopping | Product and price comparison |
| TikTok | Discovery and creator-led education |
| YouTube | Product explanation and awareness |
| Amazon | Marketplace demand and trust |
| Retail | Physical discovery and brand credibility |
| Retention, offers, and repeat purchase |
The purpose is not to estimate exact revenue by channel. It is to understand the environment around the creative.
Step 4: Understand the "why" behind the creative
Do not record only what the ad contains.
Ask why each choice may exist:
- Why was this thumbnail selected?
- Why is the product shown immediately?
- Why does the ad begin with this problem?
- Why is the founder speaking?
- Why does the proof appear at this moment?
- Why is the price shown early or late?
- Why does the CTA use urgency?
- Why does the ad lead to this specific page?
The objective is to identify the advertiser's possible hypothesis.
Step 5: Identify repeated patterns
One ad is an example. Repeated behavior across comparable competitors is a signal.
Look for repetition in:
- Thumbnails
- Opening frames
- Hooks
- Product demonstrations
- Offers
- Proof
- Objection handling
- Creative length
- Editing pace
- Landing-page headlines
- Calls to action
- Pricing presentation
- Product positioning
Do not copy the pattern. Understand why it may exist, then test the underlying idea in an original way.
Step 6: Convert patterns into hypotheses
Turn each useful observation into a possible explanation.
Observation: Several comparable competitors use close-up product imagery in the first frame.
Possible reason: The close-up may communicate quality, improve product recognition, or create curiosity.
Hypothesis: Product-detail thumbnails may stop more quality-focused customers than broad lifestyle imagery.
Step 7: Link every test to a metric
A creative learning is incomplete until it is connected to a measurable outcome.
Determine which metric should respond first and which downstream metrics must also be monitored. A thumbnail change should primarily affect the thumbstop rate. A revised offer should primarily affect conversion and order value. A stronger landing-page promise should primarily affect conversion.
Step 8: Evaluate attention and business outcomes separately
Do not confuse a better platform metric with a better business result.
An opening may improve three-second views while attracting less qualified traffic. A stronger call to action may improve clicks while reducing conversion quality. A discount may increase conversion while damaging contribution margin.
Measure the complete path from attention to economics.
A Practical Research Database
For every useful competitor ad, record:
- Competitor
- Date observed
- Platform
- Distribution role
- Product
- Hook
- Thumbnail or first frame
- Format
- Offer
- CTA
- Landing-page URL
- Proof type
- Customer objection
- Possible reason
- Test idea
- Primary metric
- Secondary metric
A useful entry should contain analysis, not only description.
Instead of:
Close-up thumbnail.
Record:
Close-up product thumbnail may communicate material quality earlier. Test against lifestyle imagery and compare thumbstop rate, three-second views, click-through rate, and conversion.
Ecommerce Ad Intelligence Example
Consider an anonymized premium fashion or leather-goods brand.
The team reviews a group of comparable competitors across Meta, TikTok, Google, and YouTube.
It notices:
- Heavy use of discounts
- Repeated claims about quality
- Similar lifestyle photography
- Limited evidence of construction quality
- Few founder-led explanations
- Weak fit and sizing education
- Product pages focused heavily on style rather than durability
The weak approach would be to copy the most common lifestyle images or launch another discount.
A stronger analysis would ask what the category may be overlooking.
Potential hypotheses include:
- Customers may want proof behind quality claims.
- Buyers may be uncertain about fit.
- Competitors may be describing craftsmanship without showing it.
- Heavy discounting may have made value-led positioning less distinctive.
- Founder-led education may build trust in a considered-purchase category.
Potential tests could include:
- Founder-led craftsmanship creative
- Material comparison ads
- Durability demonstrations
- Fit and sizing objection ads
- Customer-review creative
- Value-over-time positioning
- A product-page section explaining construction
The goal is not to reproduce a competitor's ad.
It is to understand what the market emphasizes, identify what it may be overlooking, and create an original test around that gap.
How to Connect Creative Learnings to Metrics
Creative intelligence becomes valuable when every observation leads to a measurable hypothesis.
| Creative element | Primary metric |
|---|---|
| Thumbnail or first frame | Thumbstop rate / initial view rate |
| First few seconds | Hook rate / three-second view rate |
| Middle of video | Retention / average watch time |
| Message or CTA | Click-through rate |
| Offer | Conversion rate / average order value |
| Landing-page promise | Conversion rate |
| Overall creative | Customer acquisition cost / contribution margin |
Thumbnail test
Observation: Comparable competitors repeatedly use close-up product thumbnails.
Change: Replace a lifestyle thumbnail with a close-up product image.
Primary metric: Thumbstop rate or initial view rate.
Secondary metrics:
- Three-second view rate
- Hook rate
- Click-through rate
- Conversion rate
A better thumbstop rate does not prove that the new thumbnail is commercially stronger. It may attract curiosity without attracting qualified customers.
Opening-seconds test
Observation: Competitors show the product in the first one or two seconds.
Change: Move the product demonstration earlier.
Primary metric: Three-second view rate or hook retention.
Secondary metrics:
- Average watch time
- Click-through rate
- Conversion rate
The team should examine whether the improvement continues beyond the first few seconds.
Offer test
Observation: Comparable brands use bundles rather than percentage discounts.
Change: Test a bundle against a standard discount.
Primary metric: Conversion rate.
Secondary metrics:
- Average order value
- Contribution margin
- Customer acquisition cost
A bundle may produce fewer purchases but better order economics. The business decision should not be based on conversion rate alone.
Proof test
Observation: Competitors introduce reviews or product proof immediately after the hook.
Change: Move social proof earlier in the ad.
Primary metric: Retention after the hook.
Secondary metrics:
- Click-through rate
- Landing-page conversion
- Customer acquisition cost
The goal is to understand whether earlier proof helps the customer continue through the ad and purchase journey.
Separate Attention Metrics From Business Metrics
Attention metrics help explain where creative is succeeding or failing.
Business metrics determine whether the advertising is commercially useful.
- A thumbnail may improve thumbstop rate.
- A hook may improve three-second views.
- A product demonstration may improve watch time.
- A CTA may improve click-through rate.
- An offer may improve conversion.
- A bundle may improve average order value.
But the final decision must consider:
- Customer acquisition cost
- Contribution margin
- Return rate
- Average order value
- Repeat purchase
- Payback period
- Incremental revenue
A stronger opening may attract more attention without attracting the right customer.
For example, a curiosity-led thumbnail could improve the thumbstop rate but reduce conversion because viewers misunderstand the product. A deep discount could increase conversion but attract customers with a higher return rate or lower repeat-purchase value.
The best creative is not necessarily the creative with the strongest first metric.
It is the creative that produces the strongest overall business outcome.
For teams looking to apply the same rigor to landing pages and conversion decisions, the principles behind value-driven conversion rate optimization extend this framework beyond creative into customer intelligence, offer design, and operational improvement.
The Observation → Reason → Test → Metric Framework
Every useful competitor insight should be documented in four parts.
Observation
What did you notice?
Possible reason
Why may the competitor be doing it?
Test
How can you test the underlying idea without copying the execution?
Metric
Which metric will determine whether the hypothesis was correct?
Example
Observation: Three comparable competitors use product-detail thumbnails rather than lifestyle images.
Possible reason: The close-up may communicate craftsmanship, improve product recognition, or attract customers concerned about quality.
Test: Create two versions of the same ad: one with a lifestyle thumbnail and one with a product-detail thumbnail.
Metric: Compare thumbstop rate first, followed by three-second views, click-through rate, conversion rate, customer acquisition cost, and contribution margin.
This framework prevents the swipe file from becoming a collection of disconnected inspiration.
A 30-Minute Weekly Ecommerce Ad Intelligence Routine
A useful process does not need to consume the entire week.
Review active ads and distribution activity for the most relevant competitors. Focus on meaningful changes rather than saving every variation.
Record:
- New creative
- New hooks
- New offers
- Products receiving support
- Landing-page changes
- Channel changes
Compare the observations with internal metrics.
Ask:
- Are we seeing similar customer objections?
- Are related creative elements already performing well or poorly?
- Does the idea fit our economics?
- Is the pattern relevant to our brand maturity?
Create three Observation → Reason → Test → Metric hypotheses. Avoid vague recommendations such as "make more UGC." Specify what should change and which metric should respond.
Choose one or two ideas for deeper investigation or production. The goal is not to collect more ads. The goal is to improve what the team tests next.
How AI Can Support Ecommerce Ad Intelligence
AI can reduce the manual work involved in organizing research.
It can help teams:
- Summarize ads
- Classify hooks
- Group offers
- Compare messaging
- Extract landing-page claims
- Analyze customer reviews
- Identify recurring objections
- Build first-draft creative briefs
- Maintain a structured research database
- Generate initial hypotheses
AI is especially useful when a team is processing large numbers of ads, reviews, product pages, and notes.
But AI does not automatically know:
- Your margins
- Your return rates
- Your customer economics
- Your product truth
- Your operational limitations
- Your brand position
- Your inventory constraints
- Your cash-flow requirements
AI can identify a pattern.
It cannot decide whether that pattern is appropriate for a bootstrapped business, a premium brand, a low-margin product, or a company with a high return rate. Operator judgment remains necessary.
Ecommerce Ad Intelligence Tools
A useful ecommerce ad intelligence system can combine several types of tools:
- Official platform ad libraries
- Creative research platforms
- Competitor tracking tools
- Creative analytics platforms
- Search and shopping intelligence
- Internal analytics
- Product and conversion reporting
The tool should match the decision being made.
A free ad library may be enough for a small competitor review. A creative workflow tool may help a larger team organize research. Internal analytics are necessary to determine whether an external observation produces a useful result.
See the best ad intelligence tools by use case for a broader comparison.
The broader advertising intelligence framework also explains how market, customer, media, and campaign signals fit together.
Common Ecommerce Ad Intelligence Mistakes
Copying competitor ads
Competitor creative should create hypotheses, not imitation. Study the customer problem, proof type, offer structure, or creative principle. Then create an original response.
Studying legacy brands with high equity
A famous brand's simple creative may perform because of awareness and trust. Do not assume the execution will work for a newer brand.
Comparing a bootstrapped business with a company burning cash
A funded company may tolerate acquisition economics that would damage a cash-constrained business. Match the learning source to your own objectives and financial model.
Ignoring distribution
The ad may not be the main reason the competitor is growing. Retail, marketplaces, branded search, email, and organic demand may be doing much of the work.
Assuming visible ads are profitable
An active ad may be a test, a brand campaign, a seasonal push, or an unprofitable execution. Visible activity provides signals, not proof.
Studying creative without understanding why
Descriptions such as "UGC," "close-up thumbnail," or "founder ad" are not sufficient. Identify the possible customer problem or hypothesis behind the execution.
Ignoring product pages
The ad may succeed because the product page provides strong proof, sizing information, delivery clarity, guarantees, or reviews. Study the complete path.
Tracking too many competitors
A large list creates noise. Monitor a focused group of relevant businesses.
Ignoring margins and returns
An offer may improve conversion while weakening contribution margin or attracting customers with a higher return rate.
Improving attention metrics while hurting business outcomes
Do not optimize only for thumbstop rate, video views, or clicks. Evaluate conversion, customer acquisition cost, contribution margin, and customer quality.
Saving ads without test ideas
Every research session should produce a decision, hypothesis, or question.
Reacting to every short-term trend
Not every trending format deserves testing. Evaluate whether the idea fits your audience, product, channel, and economics.
For a wider strategic comparison, read about the difference between ad intelligence and marketing intelligence.
Ecommerce Ad Intelligence Checklist
Before turning competitor observations into campaign decisions, ask:
- Are these competitors comparable to our business?
- Do we understand their likely distribution model?
- Are their goals and economics similar to ours?
- Which products are they supporting with advertising?
- Which hooks appear repeatedly?
- Which offers are common?
- Which creative patterns are becoming crowded?
- Which customer objections are being addressed?
- What happens after the click?
- Does the product page continue the ad promise?
- Which patterns align with our customer research?
- Which patterns align with our internal performance data?
- What appears underused?
- What may create meaningful differentiation?
- Which observation can become a measurable test?
- Which metric should respond first?
- Which downstream business metrics must also be monitored?
Final Takeaway
Ecommerce ad intelligence is not about watching more competitor ads.
It is about choosing relevant competitors, understanding their distribution, identifying the reason behind their choices, finding repeated patterns, building original hypotheses, and connecting creative changes to measurable outcomes.
The quality of the process depends on context. A creative decision that works for a legacy brand may fail for an unknown company. An offer that works for a venture-backed business may be unsustainable for a bootstrapped operation. A thumbnail that improves attention may still produce worse customers.
The objective is not to copy what appears to be working.
It is to understand the underlying idea, test it within your own business model, and evaluate the complete outcome.
The best ecommerce ad intelligence does not begin with "What ad should we copy?" It begins with "Which competitor is comparable to us, what distribution system is working for them, why may this creative decision exist, and which metric would prove whether the underlying idea is relevant to our business?"
Ecommerce Ad Intelligence FAQ
Common questions about ecommerce ad intelligence answered.
Written by
Syed Obaid is a DTC founder and marketer who has scaled a bootstrapped ecommerce brand to multimillion-dollar annual sales. He writes about advertising intelligence, ecommerce growth, competitor ad tracking, creative analytics, AI advertising, and practical paid media strategy.