In this episode, Adam and Alex discuss the critical mistake most Shopify brands make during BFCM: using a one-size-fits-all offer. They advocate for personalized, differentiated offers based on traffic source and likelihood to buy, and leveraging AI for dynamic upsells and product recommendations to maximize profitability.
A short editorial from the VEONIB team on why this content matters.
The video rightly argues that BFCM success in 2025 hinges on personalized, profit-focused offers rather than blanket discounts. It's a timely shift from volume to value.
Unlike generic BFCM advice, this episode dives into AI-driven propensity models and dynamic upsells, offering actionable tactics that most brands overlook. SEONIB echoes this data-first approach to maximize ROI.
Shopify brand owners and marketers should watch this to rethink their BFCM strategy; start by testing one personalized offer segment using a propensity model.
Black Friday Cyber Monday, the peak sales period for ecommerce.
Tailoring offers and experiences to individual customer segments.
A predictive model scoring how likely a customer is to make a purchase.
AI-driven product recommendations that adapt to user behavior and data.
The plan for what discounts or incentives to present to different customer groups.
Ecommerce platform where brands can implement these strategies.
Maximizing revenue while maintaining healthy margins, a key BFCM goal.
Using artificial intelligence to automate and optimize marketing and sales tactics.
Why is a one-size-fits-all BFCM offer strategy ineffective?
It ignores differences in customer intent and traffic source, leading to wasted discounts and missed revenue opportunities.
How can I personalize offers for BFCM?
Segment by traffic source (paid, email, organic) and use propensity models to adjust discount depth based on likelihood to buy.
What is a propensity model and how does it help?
It predicts how likely a customer is to purchase, allowing you to offer deeper discounts to those less likely to buy and lighter offers to those more likely.
What are some dynamic upsell strategies for BFCM?
Use AI to recommend products based on user behavior, cart contents, and past purchases, and test offers like buy-one-get-one.
How can I avoid conflicting offers across channels?
Use rules-based personalization and identity resolution to ensure consistent but differentiated experiences without overlap.
What tools can help with BFCM personalization?
Platforms like Intelligems, Klaviyo, Postscript, and identity resolution services (e.g., Ty, Outer Signal) integrate to drive personalized offers.
How important is experimentation before BFCM?
Very important; testing different offers and upsells in the 80 days prior helps identify what resonates and maximizes profit.
What are the key surface areas to optimize on-site?
Landing pages, product pages, cart, checkout, and post-purchase upsells are all revenue opportunities.
How can small improvements impact BFCM revenue?
A 1-2% increase across multiple surfaces compounds into significant additional revenue, especially during peak season.
Should I offer deep discounts to everyone during BFCM?
No, deep discounts should be reserved for less likely buyers; over-discounting to loyal customers erodes profit.