Q4 is here – and for ecommerce brands, that means the biggest sales opportunities of the year are just ahead.

Black Friday, Cyber Monday, Christmas – the biggest sales events are just around the corner. To hit your targets, preparation is key. It’s not too late to act, and the opportunities ahead are huge.

Use this checklist to get up to speed quickly and set your account up for success.

Website and UX

Review site speed 

Start with a website audit to identify any red flags. Tools like PageSpeed Insights can help diagnose technical issues. 

Encourage clients to review key pages and the checkout process on multiple devices to ensure there are no bottlenecks. 

If resources allow, use heatmap or session analysis tools such as Microsoft Clarity or Hotjar to better understand user behavior and improve the on-site experience.

Confirm tracking setup

Double-check that all tracking is configured correctly across platforms. 

Don’t just verify that tags are firing – make sure all events are set up to their fullest potential. 

For example, confirm high match rates in Meta and ensure Enhanced Conversions is fully configured.

Add VIP sign-ups/pop-ups

Before the sales period begins, encourage users to join a VIP list for Black Friday or holiday promotions. 

This can give them early access or exclusive deals. Set up a separate automated email flow to follow up with these subscribers.

Launch sale page early

Publish your sale page as soon as possible so Google can crawl and index it for SEO. 

The page doesn’t need to be accessible from your site navigation or populated with products right away – the key is to get it live early. 

If possible, reuse the same URL from previous years to build on existing SEO equity. 

You can also add a data capture form to collect VIP sign-ups until the page goes live with products.

Display cutoffs clearly

If shipping cutoff dates aren’t clear, many users won’t risk placing an order close to the deadline. 

Clearly display both standard and express delivery cutoff dates on your website.

Highlight sales sitewide with banners

Don’t rely solely on a homepage carousel to promote your sale. 

Add a banner or header across all pages so users know a sale is happening, no matter where they land.

Dig deeper: Holiday ecommerce to hit record $253 billion – here’s what’s driving it

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Creative and messaging

Run pre-sale lead gen ads

As mentioned with pop-ups, supplementing that strategy with lead generation ads can help grow your email list and build early buzz around your upcoming sale.

Launch simple, clear primary sale ads

These will be your Black Friday or holiday sale ads running for most of the campaign. 

Keep the messaging and promotion straightforward. Any confusion in a crowded feed will make users scroll past. 

Use strong branding, put the offer front and center, and include a clear CTA. On Meta, this often works best as a simple image ad.

Create Cyber Monday-specific ads

Many brands simply extend their Black Friday sale rather than creating Cyber Monday-specific ads and web banners. 

Take advantage of the opportunity to give your campaign a fresh angle – both in messaging and offer. 

Since it’s often the final day of your sale, you can go bigger on discounts for one day or add a free gift with purchases over a certain amount. 

It’s also a great way to move slower-selling inventory left over from Black Friday.

Refresh primary ads with ‘last days’ urgency

Add urgency to your messaging as the sale nears its end by including countdowns or end dates. 

This tactic works especially well for longer campaigns where ad fatigue can set in.

Finalize all creative assets early

November and December are busy months for ad builds and platform reviews. 

Make sure all sale assets are ready several weeks before launch to avoid rushed builds and delays from longer approval times.

Advertising and data

Audit product feeds

Make sure item disapprovals and limited products are kept to a minimum. Double-check that your setup is current. 

For example, if your return window has changed, update that information in Google Merchant Center.

Refresh first-party data and remarketing lists

Update any lists you plan to use this season. 

If you don’t have direct integrations, upload new or revised lists manually. 

Review your integrations and confirm that data is flowing correctly.

Build lookalike and custom audiences early

Start building audiences as soon as your first-party and remarketing lists are refreshed. 

Create Meta Lookalike Audiences, Performance Max audience signals, and Custom Audiences. 

If you run into volume issues, you’ll have time to adjust or explore alternatives.

Finalize budget by week, not just month

Agree on budgets early so you know your spending limits. Don’t plan just by month. Map out weekly spend, too. 

You’ll likely want to invest more heavily in the final week of November than in the first.

Use title and description extensions or ad customizers

Updating search ad copy can be tedious and time-consuming. 

These tools let you control and update copy dynamically without editing every RSA manually – saving hours in campaign builds.

Use ad assets, promo sitelinks, and GMC promotions

Enable sale-related sitelinks, callouts, and promotion extensions across search campaigns so your offers appear everywhere. 

In Shopping, set up Google Merchant Center promotions to highlight deals and incentives in your Shopping ad annotations.

Apply countdown features

Add a dynamic countdown timer to search ads to show exactly when your sale ends. 

This feature helps your ads stand out and adds urgency as the sale nears its close.

Launch search remarketing activity

Bid on generic keywords you wouldn’t normally target, but limit them to remarketing or first-party data audiences. 

For example, people searching for “Black Friday deals” who have purchased from your site in the past 30 days already know your brand and are primed to buy again.

Apply seasonality adjustments

If you use Google Ads or Microsoft Ads with a target ROAS strategy, apply seasonality adjustments to prepare the algorithm for higher conversion rates during the sale period. 

Remember to apply a negative adjustment once the sale ends to prevent unnecessary spend spikes.

Dig deeper: Seasonal PPC: Your guide to boosting holiday ad performance

Focus on what matters most for Q4 success

Not every tactic will fit your business or resources – and that’s OK. 

The key is to focus on what will have the biggest impact on your store. 

By addressing most of the points in this checklist, you’ll build a solid foundation for a strong Q4 and set yourself up to capture more sales during the busiest shopping season of the year.

Preparation is everything. The earlier you audit, test, and launch, the smoother your campaigns will run when traffic – and competition – start to surge.

A new research paper quietly published last week outlines a breakthrough method that allows large language models (LLMs) to simulate human consumer behavior with startling accuracy, a development that could reshape the multi-billion-dollar market research industry. The technique promises to create armies of synthetic consumers who can provide not just realistic product ratings, but also the qualitative reasoning behind them, at a scale and speed currently unattainable.

For years, companies have sought to use AI for market research, but have been stymied by a fundamental flaw: when asked to provide a numerical rating on a scale of 1 to 5, LLMs produce unrealistic and poorly distributed responses. A new paper, "LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings," submitted to the pre-print server arXiv on October 9th proposes an elegant solution that sidesteps this problem entirely.

The international team of researchers, led by Benjamin F. Maier, developed a method they call semantic similarity rating (SSR). Instead of asking an LLM for a number, SSR prompts the model for a rich, textual opinion on a product. This text is then converted into a numerical vector — an "embedding" — and its similarity is measured against a set of pre-defined reference statements. For example, a response of "I would absolutely buy this, it's exactly what I'm looking for" would be semantically closer to the reference statement for a "5" rating than to the statement for a "1."

The results are striking. Tested against a massive real-world dataset from a leading personal care corporation — comprising 57 product surveys and 9,300 human responses — the SSR method achieved 90% of human test-retest reliability. Crucially, the distribution of AI-generated ratings was statistically almost indistinguishable from the human panel. The authors state, "This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability."

A timely solution as AI threatens survey integrity

This development arrives at a critical time, as the integrity of traditional online survey panels is increasingly under threat from AI. A 2024 analysis from the Stanford Graduate School of Business highlighted a growing problem of human survey-takers using chatbots to generate their answers. These AI-generated responses were found to be "suspiciously nice," overly verbose, and lacking the "snark" and authenticity of genuine human feedback, leading to what researchers called a "homogenization" of data that could mask serious issues like discrimination or product flaws.

Maier's research offers a starkly different approach: instead of fighting to purge contaminated data, it creates a controlled environment for generating high-fidelity synthetic data from the ground up.

"What we're seeing is a pivot from defense to offense," said one analyst not affiliated with the study. "The Stanford paper showed the chaos of uncontrolled AI polluting human datasets. This new paper shows the order and utility of controlled AI creating its own datasets. For a Chief Data Officer, this is the difference between cleaning a contaminated well and tapping into a fresh spring."

From text to intent: The technical leap behind the synthetic consumer

The technical validity of the new method hinges on the quality of the text embeddings, a concept explored in a 2022 paper in EPJ Data Science. That research argued for a rigorous "construct validity" framework to ensure that text embeddings — the numerical representations of text — truly "measure what they are supposed to." 

The success of the SSR method suggests its embeddings effectively capture the nuances of purchase intent. For this new technique to be widely adopted, enterprises will need to be confident that the underlying models are not just generating plausible text, but are mapping that text to scores in a way that is robust and meaningful.

The approach also represents a significant leap from prior research, which has largely focused on using text embeddings to analyze and predict ratings from existing online reviews. A 2022 study, for example, evaluated the performance of models like BERT and word2vec in predicting review scores on retail sites, finding that newer models like BERT performed better for general use. The new research moves beyond analyzing existing data to generating novel, predictive insights before a product even hits the market.

The dawn of the digital focus group

For technical decision-makers, the implications are profound. The ability to spin up a "digital twin" of a target consumer segment and test product concepts, ad copy, or packaging variations in a matter of hours could drastically accelerate innovation cycles. 

As the paper notes, these synthetic respondents also provide "rich qualitative feedback explaining their ratings," offering a treasure trove of data for product development that is both scalable and interpretable. While the era of human-only focus groups is far from over, this research provides the most compelling evidence yet that their synthetic counterparts are ready for business.

But the business case extends beyond speed and scale. Consider the economics: a traditional survey panel for a national product launch might cost tens of thousands of dollars and take weeks to field. An SSR-based simulation could deliver comparable insights in a fraction of the time, at a fraction of the cost, and with the ability to iterate instantly based on findings. For companies in fast-moving consumer goods categories — where the window between concept and shelf can determine market leadership — this velocity advantage could be decisive.

There are, of course, caveats. The method was validated on personal care products; its performance on complex B2B purchasing decisions, luxury goods, or culturally specific products remains unproven. And while the paper demonstrates that SSR can replicate aggregate human behavior, it does not claim to predict individual consumer choices. The technique works at the population level, not the person level — a distinction that matters greatly for applications like personalized marketing.

Yet even with these limitations, the research is a watershed. While the era of human-only focus groups is far from over, this paper provides the most compelling evidence yet that their synthetic counterparts are ready for business. The question is no longer whether AI can simulate consumer sentiment, but whether enterprises can move fast enough to capitalize on it before their competitors do.

ChatGPT referral traffic converts worse than Google search, email and affiliate links, trailing on both conversion rate and revenue per session, according to a new analysis of 973 ecommerce sites.

Why we care. AI search platforms are starting to refer meaningful traffic to retailers – but not yet sales. For now, Google (paid organic) search still wins on conversion and revenue per session.

By the numbers. The dataset consisted of 12 months (Augusut 2024 to July 2025), 973 ecommerce sites, and $20 billion combined revenue.

• ChatGPT referral traffic was ~0.2% of total sessions – ~200× smaller than Google organic.
• >90% of LLM-originating ecommerce traffic came from ChatGPT (Perplexity, Gemini, Copilot, etc., are were negligible).
• Affiliate (+86%) and organic search (+13%) conversion rates were higher than ChatGPT; only paid social converted worse than ChatGPT.
• ChatGPT trailed paid and organic search on revenue per session, but beat paid social.
• ChatGPT referrals had lower bounce rates than most channels, but organic/paid search was still best on bounce rate. Session depth was generally lower than most channels.

Trendline. Conversion rate and revenue per session from ChatGPT improved, while average order value declined.

• Model projections suggested continued gains but no parity with organic search within the next year.

Between the lines. Authors suggested early-stage friction – trust and verification behavior – may push shoppers to confirm elsewhere before buying, shifting last-click credit to traditional channels.

Yes, but. Findings reflect last-click attribution and an emerging channel. If ChatGPT (and other LLMs) reshape customer journeys or make it easier to buy directly, its impact on sales could become more visible in the data.

Bottom line. Despite the hype, the data suggests AI assistants haven’t disrupted Google Search – and won’t at least in the next year. However, the trajectory for AI assistants is up and to the right. Now is the time to test, learn, and iterate to be ready when LLM shopping matures.

About the research. The study analyzed 12 months of first-party Google Analytics data from 973 ecommerce websites generating $20 billion in combined revenue. Researchers compared more than 50,000 ChatGPT-driven transactions with 164 million from traditional digital channels, using regression models that accounted for data sparsity, site effects, and device differences to evaluate conversion, order value, and engagement metrics.

Recent studies echo the same pattern. LLM traffic may be rising, but it’s weaker on engagement and conversion.

• A SALT.agency analysis found organic search visitors were more likely to engage or complete key actions across most sectors, with LLMs performing better only in a few niches like health and careers.
• Another study by Amsive showed no significant conversion difference between LLM and organic traffic, and that LLM referrals made up less than 1% of total sessions.
• All of this research points to a consistent takeaway: AI-driven referrals are growing, but still lag traditional search in both scale and purchase intent.

The working paper. ChatGPT Referrals to E-Commerce Websites: Do LLMs Outperform Traditional Channels?

Dig deeper:

• ChatGPT Shopping is here – and it’s changing ecommerce SEO rules
• Optimizing for ChatGPT Shopping: How product feeds power GEO