Reframing Black Friday around the customer journey

Black Friday was one of the biggest trading periods of the year, but there was a risk of treating it primarily as a promotional calendar: increase send volume, expose more customers to the sale and maximise revenue over a few days.

I wanted CRM to play a broader role.

Rather than asking "How many Black Friday campaigns can we send?", I approached Peak as one connected customer journey.

Capture demand before the event, convert that demand intelligently during it, and then turn Black Friday buyers into repeat customers afterwards.

Most Peak CRM problems weren't caused by a lack of activity. They came from high activity without enough coordination or relevance.

Broadcast campaigns and automated journeys could compete for the same customer. Heavy send volumes often treated highly engaged customers, dormant customers and high-intent shoppers in the same way. Creative quickly became repetitive, while discount depth often became the main form of personalisation.

The biggest missed opportunity was customer intent.

Customers were already browsing products, creating wishlists, searching categories and abandoning baskets before Black Friday started. Those behaviours told us far more about what someone wanted than a generic engagement segment.

There was also a retention problem. Customers acquired during Black Friday could generate a strong first order but then disappear once promotional activity ended.

I therefore structured the strategy around three phases.

The objective before Black Friday was to increase the amount of identifiable customer intent available when the promotion went live.

Instead of revealing the strongest offer immediately, CRM focused on encouraging behaviours such as wishlist creation, product discovery, app downloads and sign-up for early access or stock notifications.

That changed what we were able to say once the sale began.

Using AI-driven segmentation

I moved beyond traditional RFM segmentation by combining three dimensions.

The signals feeding those dimensions came from across the customer's behaviour.

That allowed different customers to receive different treatment. A high-value customer repeatedly viewing a product might need early access or a stock reminder rather than another incentive. A promotion-led customer could receive a spend threshold. A customer who historically returned around promotional periods could be prioritised for reactivation.

The role of AI wasn't to create hundreds of micro-segments. It was to help answer a commercially important question.

Who actually needs an extra incentive to convert?

Pre-Peak content also moved beyond promotion. Product edits, gifting inspiration, new collections and wishlist-building kept customers engaged without training them to ignore everything until the discount arrived.

Building intent before the sale

Anonymous demand

Real demand — browsing, searching, saving — with no customer record attached to it.

Captured before the discount

• Wishlist creation
• Product discovery
• App installs
• Early-access sign-ups

Identifiable intent

The same demand, now named and grouped by value, intent and discount sensitivity.

Once Black Friday went live, CRM volume increased — but not equally across the database.

Highly engaged and high-intent customers could receive more communication, while weaker audiences remained under tighter pressure rules.

Email remained the main channel for product discovery and campaign storytelling. SMS and push were used more selectively for early access, urgency, stock availability and deadlines.

Broadcast creates discovery. Automation responds to intent.

This became one of the most important rules in the strategy. A broadcast email might introduce the sale, but if a customer then viewed the same product repeatedly, created a wishlist or abandoned checkout, that behavioural signal should take priority over the next generic campaign.

Automated journeys therefore worked alongside the broadcast calendar rather than independently from it.

Once someone purchased, they were suppressed from unnecessary acquisition and Black Friday reminder activity.

Broadcast and automation working together

Broadcast creates discovery

The calendar introduces the sale across the base.

Automation responds to intent

A browse, wishlist or checkout signal outranks the next generic send.

Purchase ends acquisition

Once someone buys, the remaining Black Friday reminders are suppressed.

Product personalisation

Black Friday personalisation also went further than changing a first name. Dynamic content could reflect recently viewed products, wishlist items, category affinity and previous purchases.

This was particularly important as Peak progressed. Instead of repeatedly showing customers the same generic sale grid, CRM could increasingly focus on the products and categories they had already demonstrated interest in.

Keeping content fresh

I planned Black Friday creative as a sequence rather than repeating the same sale message throughout the weekend.

Different audiences could see different products and messages based on their behaviour. This allowed campaign frequency to increase without every communication feeling like another version of "Black Friday is still live."

Loyalty added another layer through member access, selected points multipliers and member benefits rather than simply providing a larger discount.

The moment a customer purchased, the objective changed from conversion to retention.

The first step was suppression. Customers who had already bought no longer needed repeated Black Friday acquisition messages.

From there, CRM moved through service, product ownership and the next relevant opportunity. New customers entered loyalty and onboarding communications, while product-level purchase data could power personalised "Complete Your Look" recommendations around one to two weeks later.

Peak intent also remained useful for non-buyers. Wishlist activity, repeated browsing and abandoned baskets could feed more targeted post-Peak retargeting rather than immediately returning everyone to the normal campaign calendar.

Building a reason to return in January

One of the most important retention mechanics was designed before Black Friday started. Customers purchasing during Peak could qualify for an automated January Spend & Save journey, with the reward determined by their Black Friday spend.

Instead of adding another discount during an already heavily promotional period, customers effectively earned future value.

Spend more during Black Friday → unlock a stronger January reward.

The journey held qualifying customers until January before triggering personalised reward, reminder and expiry communications, subject to a minimum spend.

This helped move promotional investment into a quieter trading period while creating a deliberate reason for customers to make their second purchase.

The January Spend & Save journey

What you spend sets what you unlock

Black Friday spend January reward

Lower →

Mid →

Higher →

Band widths are illustrative. Qualification required a minimum spend, so the reward was earned rather than given.

When it happens

Black Friday Held until January Reward Reminder Expiry

The biggest change was that Black Friday stopped being managed as a standalone campaign event.

CRM became responsible for the customer journey around Peak.

Pre-Peak activity captured intent. Broadcast and automation worked together rather than competing. AI-driven segmentation helped determine where incentives were actually required. Product personalisation made high-frequency communications more relevant, while post-Peak journeys created a structured route towards repeat purchase.

Success could therefore be measured well beyond Black Friday revenue.

Measuring Peak beyond revenue

Trading performance

↑20%

CRM revenue

Total revenue driven by CRM

↑15%

CRM revenue share

CRM's share of Peak trading

↑9%

CRM conversion rate

Sessions from CRM converting

Quality of the revenue

↑25%

Automated journey revenue

Behavioural triggers, not broadcast

↑17%

Second purchase, new Peak customers

Versus the average new customer

↑13%

Repeat-customer revenue share

Revenue from returning customers