Last year’s BFCM playbook is dead

Last year, you probably checked every box on the big BFCM prep list. Your site was lightning-fast, your discounts grabbed attention, and still, you could’ve lost out on millions in revenue. Surprising, right?

Here’s why: those “old-school” checklists help you optimize everything except the one thing that really matters, which is turning conversations into revenue.

Looking back at last year’s prep 

Are these the areas last year’s BFCM checklist told you to focus on? 

• Traffic prep: load tests, CDNs, speed audits, caching, uptime.
  
• Promos & pricing: discount ladders, doorbusters, bundles, urgency timers.
  
• Campaign orchestration: email/SMS calendars, ads, landing pages, pop-ups.
  
• Checkout tweaks: BNPL, accelerated wallets, and address validation.
  
• Ops & logistics: inventory buffers, 3PL SLAs, returns pages, cutoff dates.
  
• Support basics: macros, FAQs, order tracking, “we’ll be right with you.”
  

Is it useful? Of course. Enough to win? Sadly, no.  None of this prepares you for the moment a shopper raises their hand and says, “I’m ready, help me decide.” And…

The old BFCM checklist actually destroys your revenue

Because the checklist is built on the wrong assumption! It treats conversations as a problem to resolve quickly, not as an opportunity to convert profitably. So while your store runs smoothly under pressure, the very tool meant to “support” customers – chatbots – often works against you.

Instead of guiding buyers toward the checkout, the old checklist trains bots to:

• Measure efficiency, not outcomes: tracking AHT and deflection instead of AOV and revenue per chat.
  
• End conversations too soon: answering a question, then closing the chat, leaving high-intent shoppers unconvinced.
  
• Miss buying windows: routing anything complex to a human after the moment of decision has already passed.
  
• Play defense, not offense: focusing on “being available” instead of actively persuading and upselling.
  

Even official playbooks reinforce this mindset. Shopify’s checklist, for example, praises chatbots for their ability to quickly answer questions. The checklist frames them as faster FAQs, not revenue drivers. The result? You optimize your chat for efficiency, but in practice, you end up shutting down revenue conversations instead of closing them.

That’s how a BFCM checklist built to “help” ends up quietly eroding your bottom line.

Here is the real BFCM conversation that should be

If last year’s playbook was about handling traffic, this year’s playbook has to be about converting conversations. 

To see the contrast, picture this: It’s 2:07 a.m. on Black Friday. A shopper asks:
“Is this jacket waterproof for skiing?”

Standard playbook response (what most stores do):

• Bot: “Yes, this jacket is waterproof.”
  
• Customer: “Cool, thanks.”
  
• Result: Question answered. Customer leaves. No sale.

What a revenue-focused conversation (with AI support) looks like instead:

• AI bot: “Yes, it’s rated 20k/20k waterproof and breathable, fully seam-sealed, and built to stay dry in heavy snow. What size are you considering?”
  
• Shopper: “Not sure, usually M.”
  
• AI bot: “For skiing, most customers your height prefer M if they’re layering a midweight fleece. Want me to check if M in Storm Gray ships by Friday? It’s our warmest colorway and pairs with insulated bibs that are 20% off today.”
  
• Shopper: “Yes.”
  
• AI bot:  “Got it. M fits your chest and sleeve length. Storm Gray is in stock and ships free by Thursday. I’ve added the bibs to your cart with the bundle discount. Want to check out now or compare M vs L in 30 seconds?”

And the result? The AI bot keeps encouraging a move toward checkout!

It’s time to follow the new BFCM checklist because…

Customer expectations have leapt forward: 

• 43% of shoppers now demand real product expertise before they buy (Attentive, 2024). If your answers aren’t specific and trustworthy, they’ll go to the competitor who gives them confidence.
• 48% of shoppers abandon their shopping carts when delivery details are unclear (SellersCommerce, 2025). During BFCM, that translates into thousands of orders lost in just a few hours.
• Personalized suggestions lift revenue by 10-15% (McKinsey, 2024). Miss that, and you’re leaving revenue on the table while competitors scale their upsells with precision.

These expectations hit all at once, at a massive scale, during the busiest shopping days of the year. No human team can keep up. But AI can, and that’s precisely why you need to follow the new BFCM checklist, which puts AI-first conversations at the center.

Let’s see what the New BFCM playbook looks like

In the 2025 new BFCM checklist, every box you tick should prepare your AI chatbot to act like a sales associate, not a help desk. Here’s what the new playbook looks like:

1. The foundation with revenue intelligence

Your AI needs the same level of training you’d give to a top salesperson. That means it can: 

• Understanding specs, sizing, compatibility, and product trade-offs.
  
• Explaining choices in ways that build confidence.
  
• Addressing BFCM-sensitive objections like delivery cutoffs, returns, and warranties with clarity.
  
• Driving checkout by applying promotions, coupons, and expedited shipping offers.

2. The new success metrics

Old KPIs, such as “first response time” and “ticket deflection,” tell you nothing about the sales impact. In the new playbook, you track outcomes that prove conversations are profitable:

• Revenue per conversation (RPC): how much each chat is worth.
  
• Conversion rate from chat (CRc): the % of chats that end in a purchase.
  
• Average order value via chat (AOVc): Are carts getting bigger?
  
• Attachment rate: % of chats that add a cross-sell or upsell.
  
• Self-serve checkout rate: chats where AI completes the sale without hand-off.
  
• Speed-to-resolution with sale:  how fast conversations reach a confident buying decision.

3. The team evolves into AI-enhanced sales

With the new metrics in place, your team’s role also evolves. They’re no longer just staffing agents. Instead, their focus shifts to improving the AI and intervening only when necessary. In practice, the pod now looks like this:

• Playbook designers: encode your brand’s sales play into the AI.
  
• Merch & ops: keep the AI synced with live inventory, shipping windows, and promotions.
  
• Analysts: run experiments on prompts, nudges, and offers to lift AOVc.
  
• Closers: step into hot carts or VIP chats that the AI flags.

4. The path follows the right milestones before BFCM

The new playbook builds momentum step by step, so your AI is ready for peak weekend. Key milestones include:

• 6 weeks before: Train AI on catalog, policies, and top 20 buying questions.
  
• 4 weeks before: Enable proactive chat triggers for high-intent shoppers.
  
• 2 weeks before: Launch bundles and upsell flows linked to BFCM deals.
  
• 1 week before: Stress-test objection handling and simulate peak traffic.
  
• BFCM week: Let AI handle the surge while humans focus on high-value conversations.

Stop following dead playbooks. Start building revenue engines. 

You already have the foundation, the scoreboard, the team, and the timeline.  Now it’s time to turn all of that into a scalable revenue engine. Here’s how to operationalize the BFCM playbook in three decisive moves:

Move 1: Build AI that sells (not just answers)

Before you measure or optimize, your AI has to prove it can actually sell like a real associate. You need to: 

✅ Train on your entire product catalog (specs, variants, compatibility), policies, and FAQ history.

✅ Program the sales flow: qualify → recommend → bundle → de-risk → close → escalate.
✅ Add proactive triggers on PDP and cart pages for high-intent signals (scroll depth, dwell time, return visits).
✅ Set guardrails for margins, inventory availability, shipping cutoffs, and promo logic.

Move 2: Connect conversations to a revenue strategy

With selling skills in place, the next step is to link every interaction to measurable outcomes: 

✅ Track performance with smart KPIs: Revenue per Conversation (RPC), Conversion Rate from chat (CRc), Average Order Value via chat (AOVc), attachment rate, self-serve checkout rate, and speed-to-resolution.
✅ Create margin-safe offer kits: bundles, gift-with-purchase, expedited shipping unlocks.
✅ Set VIP escalation rules: flag high-value carts or customers and hand them to human closers with full context (cart details, transcript, last offer).
✅ Localize messaging: allow AI to auto-translate while preserving brand voice.

Move 3: Prove and scale the results

Once the system is live, pressure-test it to find and scale what works:
✅ Run A/B experiments such as:
  • Answer-only vs. guided sale
  • Single-SKU vs. bundle pitch
  • Standard shipping vs. “expedite if add-on”
✅ Publish daily scorecards tracking CRc and RPC.
✅ Automate the winners: roll high-performing prompts and offers into evergreen flows; retire underperformers.

However, what you see here is only the highlights. The comprehensive checklist outlines the precise steps for training data, irresistible offers, robust guardrails, and automation strategies that drive substantial revenue.