The five levers of AI-powered markdown optimization

Each lever solves a specific problem. Together, they move a retailer from reactive firefighting to proactive margin management.

1. Timing: Act sooner, not deeper

Old reality: Retailers wait too long to mark products down, then are forced to go deeper than needed to shift inventory.

With AI: AI models continuously monitor sell-through velocity, inventory cover, forecast variance, and external factors (e.g. weather, price movements, demand signals). When a product shows early signs of underperformance, the system triggers a recommendation before it becomes a problem.

Result: Smaller, earlier markdowns that protect margin and reduce the need for end-of-season clearance. Think of it as breaking the emergency markdown cycle before it starts.

2. Depth: Use data to set the right discount

Old reality: Blanket 30%, 40%, or 50% markdowns are chosen based on precedent or guesswork.

With AI: Using historical data and real-time demand signals, AI estimates the price elasticity for each product. It then simulates different discount levels and their likely impact on units sold, margin recovered, and stock remaining.

Instead of guessing, you get options like:

• 10% markdown → clears 40%, retains 38% margin
• 20% markdown → clears 75%, retains 32% margin
  
• 30% markdown → clears 90%, but drops to 20% margin

Result: You choose the trade-off that fits your objective, with clarity, not uncertainty. No more “gut-feel” discounting.

3. Segmentation: Stop treating all products the same

Old reality: Blanket markdowns are applied across categories or entire seasons.

With AI: Smart pricing tools segment products based on their role, seasonality, elasticity, and performance. Each segment gets its own guardrails and strategy:

• Protect margin on bestsellers
  
• Push volume on low-elasticity stock
• Clear through long-tail SKUs or obsolete sizes

Result: More personalized pricing decisions that reflect product role, not product location on a planner’s spreadsheet.

4. Simulation: Model before you move

Old reality: Retailers launch promotions or markdowns with limited visibility of what impact they’ll have.

With AI: Before any markdown goes live, AI engines simulate potential outcomes based on live data:

• What will sell-through look like?
• What’s the profit recovered at each discount tier?
• What are the risks of going too early, or too late?

Result: This new approach means that trading and pricing teams don’t just make faster decisions, but confident ones. Fewer surprises,  better post-campaign reviews, and stronger pricing governance. Now, every markdown becomes an intentional move — not a reactive one.

5. Learning: Get smarter over time

Old reality: Each season starts with a blank slate. No structured way to learn from past markdowns.

With AI: Every pricing decision becomes data for the model to learn from. Over time, the system improves:

• Better elasticity estimates
• Tighter forecasting
• Improved store/channel segmentation
• Faster flagging of slow-moving stock

Result: Your markdown strategy becomes self-improving, and even your misses become assets. Your team gets smarter every week, because the system does, too.

Building the playbook into your business

This isn’t a theory, it’s a toolkit. And it’s one that lean teams can embed without hiring armies of data scientists or replacing every legacy system. Here’s a lightweight rollout roadmap for integrating this playbook in the next 90–180 days:

0-30 days: Diagnose

Review markdown decisions from the last two seasons. Where did you go too deep? Where did you wait too long?

30-60 days: Pilot and segment

Choose one or two high-volume categories. Segment SKUs and assign markdown strategies (clear, protect, optimize). Begin testing AI-led simulations.

60-120 days: Operationalize

Embed markdown dashboards and scenario tools into trading meetings. Replace static calendars with guardrail-driven triggers.

120-180 days: Expand and automate

Extend AI recommendations across more categories and integrate into broader promo planning. Build learning loops to inform next season’s strategy.

The key is to start with the decision, not the dashboard. Focus firstly on how your team trades. From here, you can build the system that empowers faster, smarter choices that are going to be of value.

How much value are we talking about?

Based on the AI-powered pricing projects we’ve delivered at Peak, anything is possible. Here’s a quick look at some of the standout results that we’ve achieved for some of our retail customers:

On top of these kind of commercial statistics, let’s not forget other benefits — such as tighter price perception, better customer trust and generally improved full-price sell-through.

For a $100m category, even a 1% margin gain is $1m in recovered profit. Multiply that across multiple categories, and you’re looking at a fundamentally stronger, leaner business.

Final thought: profit lives in the detail

The next five years in retail won’t be about who has the loudest promotions or the flashiest campaign calendar.

• They’ll be won by the retailers who:
• Understand their inventory better
• Know how to price for behavior, not just clearance
• Use AI not to replace decision makers, but to superpower them

Markdowns will always be part of the game. But with the right tools, they no longer have to feel like defeat.

They can be a source of precision, aglity, and — most importantly — profit. Markdowns will always be part of the game. But with the right tools, they no longer have to feel like defeat.
They can be a source of precision. Agility. And—most importantly—profit.

The static markdown calendar: familiar, but failing

The traditional markdown calendar served a purpose in its time. It allowed teams to plan inventory flows, schedule campaigns, coordinate marketing, and align on key trading dates. But in practice, it now does more harm than good.

It’s inflexible. If a product line tanks in week three, but markdown isn’t “scheduled” until week six, you’ve just lost three weeks of potential trading margin — and made the eventual discount deeper than it needed to be.

It’s blind to performance. The calendar doesn’t care if a line is flying off shelves or gathering dust. Everything moves through the same schedule — regardless of demand, inventory cover, or price sensitivity.

It treats all products the same. Not every product deserves the same treatment. A fast-moving essential with high elasticity needs a different markdown rhythm than a premium seasonal item or long-tail replen.

The result of all this? Markdowns become reactive, rather than proactive. Teams scramble to course-correct. Clearance piles up. And promotional performance becomes a guessing game: “that one worked, this one didn’t, let’s try again next season.”

Thankfully, there’s a better way.

Enter the era of self-learning markdown systems

Modern retail demands agility, not rigidity. And that’s where AI-powered markdown planning fundamentally changes the game.

At its heart, this shift is about moving from pre-defined calendars to data-driven, dynamic decisioning. From guessing to modeling, and from rules to learning.

The five rules of the new markdown planning model

1. Plan with probabilities, not predictions

Rather than saying “we’ll markdown these lines in week six”, retailers should think differently and more strategically. For example; “If sell-through is below X%, and demand is trailing forecast, initiate markdown simulation.”

AI models can continuously ingest new information (sales velocity, stock levels, demand curves, competitor pricing) and adjust the plan in real time — offering the most profitable set of options for action at the moment of decision.

2. Segment by strategy, not just category

Different products, regions, and channels have different roles in your commercial model:

• Hero SKUs: Maintain price, protect brand
• Volume drivers: Optimize for revenue
• Seasonal stock: Clear quickly
• Premium lines: Trade carefully, protect perception
  

AI tools allow you to assign different strategies and guardrails to each group — and apply pricing actions accordingly. This protects margin and ensures promotions are aligned with commercial intent.

3. Move from cyclical to continuous

Markdown planning used to happen in “rounds” — each season had three or four phases, and that was it. Now, planning is continuous.

Self-learning systems are constantly evaluating:

• Which products are underperforming?
• Where is demand softening?
• Which stores are accumulating slow-moving stock?
• Where could a shallow markdown unlock demand?

This turns markdowns from a quarterly exercise into a daily optimization loop.

4. Simulate before you act

AI models don’t just suggest markdowns; they simulate outcomes across multiple objectives. For example:

• “If we apply a 20% markdown to Line A, we expect to sell through 65% of remaining inventory in 10 days, recover £42,000 in gross profit, and reduce clearance risk by 40%.”

• “If we wait another week, we’ll likely need to discount at 30% to hit the same result.”
  

This allows commercial teams to choose the best option with full context—balancing risk, revenue, and margin intelligently.

It’s the difference between firefighting and forecasting.

5. Measure learning, not just performance

The most valuable part of self-learning markdown systems isn’t just the uplift they generate — it’s the feedback loop they create.

With every promotion or markdown:

• Elasticity curves are updated
• Seasonality profiles are refined
• Store or channel anomalies are flagged
• Discount thresholds are recalibrated
  

Over time, the system doesn’t just execute better markdowns, but gets smarter at planning them. It learns what works for your unique business, brand, customer, and trading rhythm.

And crucially, it frees your people to focus on why things work, not just what needs doing.

What this looks like in practice

Let’s bring this to life with an example from a project I led with a mid-market fashion retailer.

Old approach

• Seasonal calendar dictated markdown dates
  
• 30% markdown applied across all mid-season lines
  
• Performance reviewed two weeks later
  
• Significant over-discounting in some SKUs; sell-through still weak in others

New approach with AI-powered markdown engine

• Markdown triggers linked to real-time sell-through thresholds
• AI surfaced five options for each SKU, ordered by profit impact
• The team chose optimized markdowns (some as low as 10%, some deferred entirely)
• Gross margin improved by 410bps across the campaign
• Inventory cleared 12 days faster than previous season

The key? They didn’t “plan” the markdown months ahead. They let performance, elasticity, and stock position guide the plan, as it unfolded.

How to build a self-learning markdown capability

This kind of transformation isn’t just about tech. It’s about mindset and capability. Here are some simple steps to follow:

1. Audit your current calendar

Where are markdowns pre-set and rigid? Where do they ignore performance?

2. Define markdown strategies by product role

Assign commercial roles (protect, push, clear, test) to each line or category

3. Centralize data feeds

Ensure sell-through, pricing, inventory, and external signals are accessible to your pricing tools or teams in near real-time

4. Run controlled simulations

Use AI or analytics tools to test multiple markdown scenarios in key categories

5. Shift to weekly or rolling reviews

Ditch static planning meetings in favour of fast-paced, cross-functional trading huddles with live data and AI insight

Final thought: the calendar doesn’t know your customer

Static markdown calendars are comforting, but they’re blind to nuance, change, and reality. And, in a world where every percentage point of margin matters, comfort isn’t good enough.

Markdown planning should be a living process, not a fixed calendar. And the retailers who embrace adaptive, self-learning systems will find they no longer have to choose between profitability and agility. The best plan isn’t one you wrote three months ago, but the one that writes itself every day.

What is price elasticity in retail?

At its core, price elasticity is a measure of how sensitive customer demand is to changes in price. In plain English:

If I lower the price, how much more will I sell — and is that enough to make up for the lower margin?

That’s the big question. If the answer is yes, great. You’ve run a profitable promotion or markdown. If the answer is no, then you’ve just given away profit for no meaningful gain.

However, there’s one thing that you need to keep in mind, and that’s that not all products behave the same.

Some are highly elastic, meaning a 10% drop in price might drive a 40% increase in volume. Others are inelastic; you could slash the price by 50% and see barely any movement. That difference is where money is made or lost.

Real-world examples: what elasticity actually looks like

Let’s move past the theory and look at three examples that highlight how elasticity plays out in a typical retail setting:

Example 1: fashion t-shirts vs. outerwear jackets

Fashion t-shirt: elastic product. At full price ($20), it’s selling modestly. But at 30% off, demand surges — perhaps because shoppers view it as non-essential or easy to impulse-buy.

Outerwear jacket: inelastic product. High value, high consideration. Even a 20% discount doesn’t drive much additional volume. Shoppers are comparing features, brand value and timing (e.g., winter seasonality) more than price alone.

Takeaway: One-size-fits-all markdowns across both categories will over-discount one and under-discount the other. Margin is lost either way.

Example 2: same product, different channels

Online: A home appliance priced at £199 sees a strong uptick in conversions when dropped to £179. Price comparison is immediate, and digital customers are sensitive to psychological price breaks.

In store: The same product, with the same markdown, barely moves. Footfall is low, and the visual merchandising doesn’t draw attention to the discount.

Takeaway:  Channel elasticity matters. Pricing strategies should vary by channel, not just product.

Example 3: markdown timing on a seasonal line

Swimwear in early June: Elastic. A small markdown (say, 15%) triggers a demand spike because customers know they’ll use it imminently.

Swimwear in mid-August: Inelastic. Even at 50% off, sell-through is low. The season’s psychologically over.

Takeaway: Elasticity isn’t static — it shifts based on season, stock cover and shopper mindset.

Why most retailers get elasticity wrong

Despite these clear patterns, most retail pricing strategies remain sub-optimal:

• Flat 30% markdowns across an entire category
• Discounting based on weeks-on-sale, not sales velocity
• Promotions planned three months in advance, not based on real-time data

This is because calculating true elasticity across thousands of SKUs, channels and time periods is hard. It requires:

• Clean data (sales, pricing, inventory)
• Strong attribution logic (e.g., isolating price impact vs. marketing impact)
• Statistical modeling that can scale

Most teams don’t have the tools, or the time, to do this manually. So they fall back on rules of thumb, not rules of science.

The AI advantage: surfacing elasticity at scale

Here’s where a modern approach, driven by artificial intelligence (AI) can help. AI pricing solutions don’t just automate pricing. They illuminate elasticity in ways that teams can immediately act on. 

1. Elasticity curves by product

Advanced tools can model how each product behaves at different price points — visually surfacing where demand surges, plateaus, or drops off.

For example, say a handbag shows strong demand between £59–£69, but no uplift at £49. That lower price might signal lower quality to the customer — hurting, not helping, conversions.

2. Scenario planning

Elasticity data powers simulations:

“What happens to profit if we drop this line by 10%?”

“What’s the revenue gain at 30% off, and what margin are we leaving on the table?“

This lets teams make deliberate trade-offs, rather than discounting blindly.

3. AI-driven recommendations

Instead of asking teams to digest complex elasticity curves, smart platforms simply surface ranked markdown options. These options can be ordered by your most important objectives, whether it’s to maximize profit, clear stock quickly or find the right balance between the two.

Each option comes with clear projections — expected revenue, margin, units sold — so you can choose based on real context, not guesswork.

Why elasticity should be the foundation of every markdown decision

Because it’s the only way to answer the three questions every pricing team should be asking:

1. Should we reduce the price?
2. If so, by how much?
3. What outcome will that deliver — and is it better than doing nothing?

Without elasticity insight, these answers are speculative at best. With it, they become strategic.

The commercial impact: real numbers, not theory

From AI pricing projects I’ve led at Peak across fashion, home and beauty categories, here’s what using elasticity modeling unlocks:

• 30–50% fewer deep markdowns (many products clear with a shallower cut)
• 200–500 bps improvement in margin on promoted lines
• Faster sell-through with more predictable demand curves
• Fewer clearance “fire sales” at end of season

In one apparel business, AI recommendations showed that 20% of markdowns were completely unnecessary — sales would have cleared without any discount at all. That’s a huge margin recovery opportunity hiding in plain sight.

Making elasticity actionable for your team

You don’t need to invest in new tools or software immediately. Here’s how to embed elasticity thinking into your pricing process, even before rolling out AI:

Segment your SKUs: Group products by type, seasonality, price band and channel

Analyze past performance: Look at how demand shifted with different discounts last season

Run markdown experiments: A/B test different discount levels in small cohorts to estimate demand response

Build guardrails: Set rules: e.g., no markdown below 30% margin unless elasticity justifies it

Educate your teams: Make elasticity a commercial skill, not a data science secret

The future lies in tools that automate all of this — but the mindset starts now.

Final thought: elasticity is a lens, not a lever

Price elasticity isn’t just a number, more a way of seeing and interpreting the market. It helps you understand your products, your customers and your commercial model more deeply.

In a world of rising input costs and tighter margins, understanding elasticity is no longer a “nice to have,” but a competitive imperative.

Because if you don’t know how your prices shape demand, you’re not really in control of either.

What is data latency?

Data latency isn’t just a tech problem, but also a commercial one. It refers to the delay between when something happens — e.g., a product stops selling, a competitor undercuts or a promotion starts to underperform — and when your systems, people and processes can detect and react to it.

This delay can be costly. We’re talking missed revenue, eroded margin and depleted customer trust.

In my time advising retailers and delivering AI-powered pricing systems, I’ve seen this issue repeatedly:

• Merchandisers still working from spreadsheets that are a week out of date
• Pricing meetings based on static reports generated every Monday for decisions made on Wednesday
• Campaign performance only reviewed after the promotion ends

By then, it’s too late: the damage is already done.

Where data latency shows up (and what it costs you)

Markdown timing

Let’s say a high-volume SKU starts to slow down in week four of its life cycle. You spot the decline in your weekly trading report; generated on Monday, reviewed on Wednesday.

A decision is made to apply a 20% markdown, effective the following Monday.

That’s a two-week lag from the point of slowdown to the price change hitting the shelf.

In fast-moving categories, that window can mean the difference between a 20% markdown clearing stock profitably and a 40% markdown needed to shift it later.

If this is happening across hundreds of products, that’s a disaster for your margin.

Promotion performance management

Most retailers run promotions blindly for at least 5–7 days. They launch on a Thursday, wait for the weekend, and don’t review results until Monday morning. Underperformance creates panic, while overperformance leads to stockouts.

But with modern tooling, you can get visibility in near real-time:

• Is the promo driving expected uplift?

• Is it cannibalizing full-price lines?

• Are certain stores or regions struggling to execute?

When that information lands fast, intervention becomes possible. When it doesn’t, you’re left explaining why a discounting campaign actually hurt you.

Store execution and channel consistency

Data latency isn’t just digital — it’s physical. In multi-channel environments, inconsistencies between online, store and third-party pricing can erode margin and damage trust.

One retailer I worked with found that due to reporting lag, stores were operating under two versions behind the current promotional file. Customers were being offered incorrect discounts, managers were manually overriding prices, and trust was eroding from the shop floor up.

In today’s omnichannel world, these lags are both unacceptable and entirely preventable.

Why the problem persists

Most retailers will openly admit that they’re operating in the rear-view mirror. So why does data latency remain unsolved in so many businesses?

Siloed systems

Promotions, pricing, stock, traffic and web analytics often sit in separate systems with no shared schema or unified view. Stitching insights together becomes a manual task for teams — it’s slow, error-prone and not scalable.

Reliance on manual intervention

Even with strong BI tools in place, most pricing and trading teams are still exporting data into spreadsheets, manipulating it by hand and emailing decisions around for approval. This is no longer fit-for-purpose in modern retail businesses.

Legacy operating rhythms

Weekly trade meetings, monthly performance packs, quarterly reviews — they’re deeply embedded within organizations. Changing the cadence of commercial decision making feels hard, even when teams know it’s hurting them.

The good news? Data latency is a fixable problem

Fixing data latency doesn’t require ripping out all your systems or hiring an army of expensive data scientists. It all starts with rethinking your processes and tooling with one goal in mind: to enable the business to see, decide and act faster.

Here’s what that looks like in practice:

1. Live promotion and pricing dashboards

Real-time or near-real-time dashboards give commercial teams live visibility into:

• Sell-through by product and channel
• Promo effectiveness by region or store
• Competitor pricing changes
• Inventory cover and ageing

These aren’t just visual aids; they’re command centers for action. One customer I worked with used a real-time dashboard to pause an underperforming promotion mid-flight, redirect inventory, and rerun a more targeted campaign. This saved £350k in potential margin loss.

2. Trigger-based alerts

Rather than relying on human memory or a weekly report, AI solutions can be configured to trigger alerts when:

• A product’s sell-through falls below plan
• Demand forecast deviates sharply from reality
• A competitor price undercuts by >10%
• A promotion is driving margin-negative sales
  

These alerts land in Slack, Teams or even on mobile — putting the insight where decisions are actually made.

3. Intelligent markdown engines

This is where AI comes into its own. Intelligent markdown engines continuously learn from live data and recommend optimal pricing actions, with speed and precision, while taking into account a business’ specific guardrails.

They don’t just suggest markdowns; they simulate different outcomes:

1. What happens to revenue at 10% off vs. 30% off?
   
2. What’s the impact if we wait one more week?
   
3. What’s the effect on adjacent products or categories?

This moves markdowns from guesswork to governed science, while also speeding up your entire commercial rhythm.

4. Embed data into daily workflow

Dashboards are only valuable if they’re actually used. Embedding data into the systems traders already work in (merchandising tools, buying platforms, ERP systems) ensures that latency isn’t just reduced, but eliminated at the source.

The goal is to make the right decision the easiest decision to make.

So, what happens when you get it right?

The shift from lag to live unlocks powerful benefits:

Before

• Markdown decisions made weekly
• Performance reviewed post-campaign
• Teams acting on partial, outdated data
• Missed signals in long-tail SKUs

After

• Markdown simulations updated daily
• Promo results visible mid-flight
• Teams aligned on a single, live source of truth
• Automated alerts surface at-risk stock instantly

Results

Across multiple AI deployments over my time at Peak so far, I’ve seen this translate to:

• Gross margin uplift of 3–5% on discounted ranges
• 20–30% faster decision cycles across pricing teams
• Increased sell-through without deeper discounts
• Fewer emergency clearance events and markdown “fire drills”

This is not just a data problem — it’s a leadership problem

Solving latency requires more than dashboards and integrations; it takes a cultural shift. Leaders need to ask:

• Are we empowering our teams to act fast?
• Are we measuring lag time in our decision processes?
• Do we expect performance reviews to be retrospective or responsive?

Those who build their organizations around speed to signal will outperform those stuck in end-of-week reports and spreadsheet bottlenecks.

Final thought: margin hides in the minutes

Retail is won or lost in moments. These moments could be when a buyer spots a trend early, when a trader intervenes mid-promo, or when a markdown happens just before the tipping point.

In those moments, data latency is the enemy. But it’s also the opportunity.

If you reduce the lag — between signal and decision, decision and action — you don’t just protect margin, you unlock it.

The psychology of the discount reflex

Let’s start with the human side.

Promotions can be addictive. They offer immediate feedback: sales spike, sell-through improves, and reports look greener. That hit of “commercial dopamine” feels good, especially when in-store footfall is down or KPIs are under pressure.

But, over time, it rewires behavior across the business:

• Buyers overcommit on volume, assuming price cuts will bail them out

• Merchandisers focus on short-term clearance, not long-term productivity

• Marketers lean on “30% off” messaging because it performs reliably in email and paid media

• Customers learn to wait for discounts, and hold back on full-price purchases
  

It creates a dangerous loop: we sell more, but make less. And worse, we train our customers to devalue our product and brand.

The real cost of the discount addiction

There’s a long-standing myth in retail that if margins are declining, it’s just the cost of staying competitive. But here’s what’s really happening when discounting becomes the default:

1. Margin drain

Every percentage point of discount given away unnecessarily is money that could’ve been reinvested in product, people, or innovation. For mid-sized retailers, blanket markdowns can wipe out millions in potential profit annually.

2. Brand erosion

Over-discounting doesn’t just hurt the bottom line, but changes perception. If your “new in” drops to 40% off two weeks later, customers start to mistrust your pricing. That undermines brand value, particularly for lifestyle or aspirational brands.

3. Inventory inefficiency

Markdowns applied late in the season, or inconsistently across channels, trap working capital. Clearance decisions become reactive, often made under pressure, with no visibility into which items are truly overstocked or which could still sell at higher price points.

A better question than margin or market share

Framing the problem as margin vs. market share creates a false binary.

The real question retailers should be asking is:
“How do we maximize profitable sell-through, while protecting brand and customer trust?”

That’s where smarter, AI-assisted decision making changes the game.

Because the truth is: you can grow sales and improve margin. You just need to make sure your discount decisions are strategic, not sweeping.

Enter AI: How to trade smarter, not just harder

AI has the potential to help retailers escape the discount trap. It doesn’t do this by removing promotions altogether, but by making them more precise, better timed, and more profitable. Here’s how:

Product-specific elasticity insights

AI models can estimate price elasticity at the SKU level. This means you don’t just know what’s not selling, but how much of a discount is actually needed to change demand. You’d be surprised how often that answer is: “less than you think.”

Simulating trade-offs in advance

Rather than committing to 30% off across an entire category, AI can show the projected impact of 5%, 10%, or 20% markdowns on both sell-through and gross margin. This lets you choose the right balance, rather than guessing.

Segmented and localized tactics

Not every channel or store behaves the same, meaning that one-size-fits-all promotions waste opportunity. AI enables dynamic strategies — markdowns that vary by geography, store type, or digital behavior — optimizing sell-through without over-discounting where you don’t need to.

Campaign evaluation and learning loops

With AI-led systems, every promotion becomes a learning opportunity. Models ingest post-campaign performance data, improving their recommendations over time and preventing repeated mistakes.

Real world impact: what we’ve seen at Peak

We’ve been working with forward-thinking retail brands for over ten years now, helping them to refine their approach to pricing with data, AI and machine learning. In the margin optimization projects we’ve delivered, the most successful retailers tend to share two traits:

They start by understanding their true discount dependence

• Where is margin most eroded?
• Which categories or teams lean too heavily on price cuts?
• What’s the real cost of your markdown reflex?

They adopt AI incrementally, but decisively

• Start with high-volume or problem categories
• Test multiple discount strategies in parallel
• Measure outcomes in both revenue and retained margin

The results are compelling…

• 200–500 bps of margin uplift on promoted product lines 
• Higher full-price sell-through in targeted categories 
• Increased trust from commercial teams who now make decisions based on data, not gut feel

From default to deliberate: building a promotions strategy that works

There are some clear differences between a traditional approach to discounting and a future-fit promotional strategy:

Traditional approach

• Timing: Fixed to the calendar
• Depth: Flat across all SKUs
• Scope: Blanket campaigns
• Measurement: Based on volume uplift
• Process: Manual and reactive

Modern approach

• Timing: Triggered by live data
• Depth: Varies by price elasticity
• Scope: Segmented by channel and demand
• Measurement: Based on profit and velocity
• Process: AI-supported and strategic

This isn’t about replacing people with machines. It’s about equipping traders, merchandisers, and planners with tools that make them faster, smarter, and more effective.

Conclusion: the discount game has changed

We’re entering a new age of retail, one where margin resilience is a competitive advantage, not just a finance KPI. The retailers who continue to operate in the same way, defaulting to discounting, will struggle to defend their economics in a world of rising costs and increasingly-savvy consumers.

But those who rethink their markdown and promotion strategy — those who use AI to power precision and profit at scale — will find they don’t have to choose between market share and margin, but achieve both.

The traditional markdown playbook is outdated and overexposed

Retailers have long relied on a calendar-based markdown structure:

• XX% off at six weeks
• A further 20% off if the stock hasn’t moved by week eight
• Final clearance before the new range lands

Sound familiar? These static triggers are comforting, yet predictable and wildly inefficient.

This traditional markdown method assumes all products perform the same, all channels behave identically and demand patterns are linear. The reality? They aren’t, they don’t and it’s not. As a result, retailers are often stuck between two dangerous extremes:

• Markdowns applied too early, eroding profit on products that would have sold through at full price with just a little more time or visibility
• Markdowns applied too late, leaving aging stock to gather dust in the warehouse, requiring even deeper discounts to shift, if it shifts at all

Both paths lead to margin erosion, inventory write-offs and poor customer price perception. But worst of all, they create a cycle of overbuying and reactive pricing. And this is a dangerous loop many retailers are struggling to escape from.

Four reasons retail markdown strategy is broken

1. Decisions are based on lagging indicators

Most markdown decisions today are made using reports pulled weekly or even monthly. By the time a trend is spotted — like slow-moving stock, competitor activity or channel underperformance — the window to act profitably has often closed.

Retail needs real-time visibility in order to move from reactive to responsive.

2. Blanket, one-size-fits-all approaches

Most markdowns are still being applied en masse, ignoring key differentiators such as:

• Channel elasticity (online vs. in-store)
  
• Product attributes (seasonal, replenishable, premium)
  
• Store size and profile
  
• Region- or climate-specific demand
  

Treating all inventory equally is like treating all customers the same; it ignores the nuance that drives value.

3. Organizational silos prevent coordinated action

Buying, merchandising, pricing and supply chain teams often operate in disconnected silos, rarely relaying key information to one another. This means that markdowns become last-minute decisions made in isolation, and aren’t always aligned to broader commercial objectives like customer lifetime value or stock productivity.

4. Markdowns are too manual

Many pricing and merchandising teams still run markdown processes manually. Spreadsheet gymnastics, version control chaos, pricing decks, approval chains. It’s exhausting, error-prone and slow.

More importantly, it eats into strategic thinking time. Teams are caught firefighting, unable to step back and ask: Why did we get into this position in the first place? How can we do better next season?

So, what should a modern markdown strategy look like?

Retailers who are winning today are approaching markdowns differently. They treat them not as a desperate last act but as a strategic profit lever. Here’s what that involves:

Data-rich decision making

Modern markdown strategies leverage real-time data: sales velocity, traffic trends, competitor pricing, inventory age, weather forecasts — even macroeconomic indicators or things like US tariff adjustments.

And this data doesn’t just describe performance, but predicts it. With the right systems, it can prescribe the best actions to take, too.

Product-level elasticity modeling

Rather than assuming an arbitrary 30% or 50% discount will move stock, artificial intelligence (AI) models can estimate how much a specific product needs to be discounted to trigger a meaningful increase in demand — and how that discount will impact gross margin.

This allows retailers to identify the optimal discount that clears stock and protects profitability.

Guardrails and scenarios

Modern systems allow merchants to set business-specific guardrails, like minimum margin thresholds, price floors or even brand guidelines.

From there, you can simulate multiple markdown options, ranked from most profitable to most aggressive, allowing decision makers to choose the best scenario with full visibility of the potential trade-offs.

Optimizing markdown strategy at scale with AI

AI doesn’t just have the potential to automate markdowns, but completely transform them. Here’s how:

Continuous learning

AI algorithms ingest performance data daily (sometimes even hourly), adjusting their understanding of product velocity, seasonal decay curves and demand shifts in real time. This enables dynamic markdowns that are not rigid, but responsive.

Price elasticity curve visualization

AI solutions can now surface elasticity curves for each product, allowing retailers to see how much demand is unlocked at each potential price point. This provides clarity on whether to push for margin or sales volume.

Pattern recognition across SKUs

AI can identify markdown opportunities across thousands of SKUs that no human could spot, whether that’s dead stock hidden in a long tail or a halo effect that suggests keeping a key SKU full price longer.

Strategy embedded into workflow

The best AI pricing tools embed recommendations directly into the workflow of buyers, merchandisers and traders — making it fast and easy to act on insights without switching between systems or hunting through reports.

AI for markdown optimization: the results speak for themselves

From the AI-driven projects I’ve delivered at Peak, we’ve delivered some game-changing results for some of the world’s leading retail brands. Some of the typical stats we usually see include:

• Gross margin uplift of 200–500bps on markdown sales
• Reduction in stock write-offs by 10–20%
• Time savings of 30–40% for pricing and merchandising teams
• Higher sell-through at shallower discounts
• Better price perception by customers

In a world of tight budgets and fragile consumer confidence, this isn’t just nice to have, but a matter of retail survival.

Ready to get going? Start with the problem, not the tech

This isn’t a pitch for selling software. It’s a call for merchandisers to rethink how you approach margin recovery.

The retailers who’ll thrive over the next five years won’t just be the ones who “adopt AI” — they’ll be the ones who challenge legacy processes, rebuild their commercial decision making with data, and give their teams tools to trade smarter, faster and with more confidence.

It starts with a simple question:

Is your markdown strategy really working for you? If the answer’s no, you know what to do.

3. More investment in brick and mortar stores

There’s already been a number of high-profile examples of retailers investing more time and money into transforming their physical presence — Aldi, for example, is planning to invest almost £100m into improving the in-store experience for its shoppers.

Another hot topic when it comes to physical retail in 2025 is digital shelf-edge labels, which could potentially become the new RFID — lots of people are talking about it, but very few will actually bite the bullet and invest in this kind of future-facing technology. 

However, those that do — the more progressive retailers with both efficiency goals and ambitious technology goals — are starting to make moves in these kind of spaces already. These retailers will see the most success in 2025.

4. Addressing the AI misconception

Over the past couple of years there’s been a significant increase in retailers claiming that they’re now “doing some artificial intelligence (AI)” — but they’re not really, it’s something else. The term ‘AI’ has become something of a catch-all in the industry for a wide range of technologies; machine learning, generative, IoT, robotic process automation, chatbots and more. 

According to Gartner, when it comes to AI, many retailers are still in “the trough of disillusionment.” However, in 2025 we’ll see more move up towards “the slope of enlightenment,” educating themselves on what AI actually is and moving forwards.

5. Embracing the marketplace model

Marketplaces are becoming more and more of a cornerstone in an effective retail strategy, and are one of the quickest ways that businesses can increase their return on capital employed (ROCE).

This rise has been on the cards for a while, and a number of big players are in the process of retro-fitting the marketplace operating model into their businesses. This will only continue; brands like it, retailers like it, consumers like it. Everyone wins, at least for now.

Poor allocation is a secret brand killer

When you consider what may negatively impact your business, poor allocation may not be the first thing that comes to mind. Bad customer service, misguided marketing campaigns and failing to nail down your unique selling point may appear more fatal. While they are certainly important factors to consider, it’s often best to pinpoint the shortcomings that are harming your organization covertly, too.

Without question, this can be applied to poor allocation. It’s easy to see how it can occur; traditional allocation can often feel like balancing the most delicate of scales, with insufficient stock to meet demand or excess inventory tipping them into disastrous territory and potentially losing customers for good. This is particularly critical as we see the decline of “shop locally,” which boomed post-lockdown and has lost popularity due to the cost-of-living crisis.

Similarly, a retailer must be targeting improved inventory turnover rates to introduce new products, or react rapidly to shifting trends. If stock is not budging and clearing shelf space, it can lead to stale offerings and lost sales. This is compounded by increased costs overall, due to excess inventory and the impact of markdowns to clear it out. In 2022, high profile brands such as Burberry felt the sting of costly markdowns and that continues to this day.

To minimize these risks, retailers must invest in AI to prioritize data-driven allocation. Without it, retailers should be likened to driving a car without any headlights on; you may make it to your destination without crashing, but it’ll be by blind luck rather than knowing what’s on the road in front of you. Full visibility of your stock is essential, as it allows you to plan more efficiently and react quickly to customer needs and market trends. AI delivers these comprehensive insights, allowing you to perfect your inventory management plan throughout the year, not just for the “golden quarter.”

The return of “E” in ESG and how AI can help

After much early attention, the economic downturn has been the perfect excuse for businesses to dismiss anything related to ESG. But the “E” is firmly sneaking back up people’s agendas. With certain pension funds starting to challenge the investments their Private Equity borrowers make and the UK government set to unveil regulation for ESG reporting companies, scrutiny on organizations will grow. Pair that with consumers becoming more and more socially conscious, and the “E” can become a real body-blow if it’s not understood and addressed.

AI’s strength for curbing poor allocation also has a big sustainability win that can be achieved at the same time. By having full visibility of your inventory, less movement across the supply chain is required — this means reduced packaging and shipping of unneeded stock between locations.

Retailers will receive only exactly what they need, when they need it. For too long, retailers have felt stuck between the profit vs. planet conundrum, but AI shows both can work in tandem moving forward. Not only will it save retailers considerable sums of money, but it will also, by extension, reduce their much-scrutinized carbon footprint.

Sustainable inventories all year round

As we edge ever closer to December, many eyes are fixated on the golden quarter. But the truth is that there’s plenty of work to put in before this crucial period, and the “right” to bring in your customers must be earned.

A good starting point is to invest in AI; its capabilities will allow retailers to cater to every customer’s needs and, crucially, keep them coming back with less impact to the planet. By accepting that inventory management isn’t just for Christmas, retailers can build a strategy to outperform competitors consistently, and ensure consumers continue to spend their money with them throughout the year.

This article was originally published as a contribution to Supply Chain Brain.

The rise of price optimization in retail

To really assess why 2024 is seemingly shaping us as the year of price optimization, let’s take a quick step back and look at what happened last year. In spring 2023 I attended a closed-door virtual forum with one of our partners, talking business performance and future strategy with several retail C-suite executives, including the CEO of a UK retailer with around $1 billion in annual turnover.

They claimed that they were starting to see reductions in their raw materials costs, and had seen freight and logistics costs recover to the levels they were at before the disruption caused by the COVID-19 pandemic. As a result, they were looking to pass these savings on to their customers by lowering the prices of their products.

This is huge.

If some businesses are thinking in this way, the retail sector will soon see a divide between those who are able to do it and those who can’t. For the latter — those businesses that don’t have a good grip of their supply chain process or don’t have the right relationships with their suppliers — the only way they’ll be able to compete with the others is through discounting and promotions. And, from our experience talking to a number of retailers over the years, we know that not many of those businesses are discounting in a sophisticated, data-informed way.

This bifurcation of businesses has the potential to radically change the fortunes of retail and consumer brands. It’s impossible to look at the news these days without reading something related to the cost of living crisis, and what have become some really tough trading conditions for businesses to be dealing with as a result. And, throughout the rest of the year, we did start to see more and more businesses freeze or even lower their prices.

Grocers were the ones who led the way in this sense — the likes of Carrefour, Morrisons, Aldi, Tesco, Sainsbury’s and more came out publicly and said they were bringing prices down and they were investing in doing so. Non-food retailers, though, were slower to react — you can count them on one hand.

In the summer, Primark became one of the few non-food retailers that announced price cuts, closely followed by IKEA in November. Primark owns its supply chain, in the sense that they source and make their products themselves, which gives them the agility they need to do so. If you’re reliant on separate third-party suppliers, or middlemen companies, though, this makes things significantly more difficult.

Using a reduction in price to attract consumers

To summarize the story so far, the retail sector has been split in two in terms of who’s capable of reducing prices and who isn’t. The issue here is that those who are capable are transforming the expectations of consumers.

The already-savvy consumer is now getting more and more used to the concept of prices being reduced — it becomes the new norm and the expectation, even if subconsciously. “Finally prices have stopped going up. Some are even starting to come down! What a relief!”

The issue is that not every business is doing it. Consumers are now looking at non-reduced prices and, perhaps subconsciously, feeling like they’re too expensive. This is triggering, to an extent, a macroeconomic bullwhip where the only way some of these retailers are going to be able to compete is by improving their markdown and promotions strategies. But this can be challenging, and hard to pull off successfully.

Last year both Foot Locker and Macy’s declared that increased discounting, markdown and promotional activity had demonstrably negatively impacted their overall financial performance. It’s a trade off that is quite a serious problem in the current climate — your rate of sale on certain things has dropped to a point where you’ve had to get busy with markdowns, but you’re doing it inelegantly and are harming your business performance.

Price optimization in retail: why now?

It’s never a nice thought, but my prediction is that this problem will signal the demise of at least a handful of big name retailers over the course of the rest of the year. This isn’t a desirable outcome, but most businesses simply aren’t moving fast enough on this and looking at the way they approach pricing in a more sophisticated way.

The proof is in the headlines — last year a staggering 18.2% of all publicly-listed companies in the UK issued profit warnings. That exceeds those issued at the height of the 2008 financial crisis. And, when you slice that by the retail sector, the percentage becomes even higher.

Retailers need to act fast and rethink their approach to pricing, avoiding those margin-eroding blanket discounts and acting more strategically. The issue is that this is impossible to do manually — there’s a delicate balancing act between keeping stock moving, but doing so profitability. You could increase your rate of sale tomorrow, but that would mean hurting your margin. You could increase your margin tomorrow, but that would harm your rate of sale. It’s hard for businesses to find that sweet spot and achieve that necessary balance — but technology is here to help with that.

Peak’s pricing AI solutions are enabling businesses to optimize their pricing, with product-level price recommendations that find the perfect balance between customer demand and business goals, preserving margin and driving profit — all without spending weeks in spreadsheets.

To learn more, join our next live product demo and see our game-changing AI in action.

Since the pandemic first reared its head and we were plunged into Lockdown One, it was always going to be a difficult, difficult year for many retail businesses. For the majority, it was all about survival, by hook or by crook.

It’s not all doom and gloom

It wasn’t all bad, though. There are lessons to be learned from those who were successful during this period, and I think there are still plenty of reasons to be positive about the shape of retail in 2021. For example, last year’s Black Friday period can be considered, across the board, as a success, with overall retail sales up month-on-month by 3.3% in November 2020. Considering physical stores across the country were shut for the majority of this month, and with the Black Friday weekend itself taking place in lockdown, this is a better result than many could’ve hoped for.

Online, of course, played a key role in this, with 2020 turning out to be a huge year for e-commerce. UK online sales growth came in at +37% year on year for December 2020, driving the annual figure to a 13 year high.

With consumer behavior and shopping habits shifting throughout the year, the market matured greatly, and many brands will have found themselves being dragged – perhaps kicking and screaming! – into the 21st century. Digital transformation plans have accelerated off the back of this, and at Peak we’ve seen first hand that more and more retailers are becoming aware of the importance of data and harnessing technology like Decision Intelligence and AI when it comes to making those crucial business decisions and delivering convenience for consumers. These new attitudes and priorities will stand many businesses in good stead for the months ahead.

Getting back on track in 2021

Looking forward, I strongly believe that there’s lots to be positive about in retail in 2021. For me, this year will be the ‘Year of Demand’ for retailers. The focus for many will be on getting back on track and ensuring a bit more stability in their day-to-day operations, particularly as brands look to balance the eventual return of physical footfall with a maturing e-commerce offering.

A key component of this will be the ability to forecast and map demand with greater accuracy, joining up and utilizing as many data points as possible across channels to make smarter predictions and better decisions around areas like stock, allocation and replenishment. This may well be the year that flexibility and agility become more than just buzz words, but mission-critical objectives that retailers need to be succeeding in. 

The difference maker will be a retailer’s ability to connect these demand levers with other parts of the business – such as marketing and supply chain, for example. This connected commerce vision, where each function of a retail business talks to each other, will help smooth out any disconnect between the sales channels and the supply chain, powering a slick operation with accurate demand forecasting at its core. Plus, don’t underestimate the importance of any cross-team cultural silos and barriers being broken down as a result of these data points being shared.

Customer convenience will be key

The result? Well, imagine if your recommendation engine on your website automatically knew if you had a challenge in the supply chain and reacted accordingly. Or if you could ensure that the right stock is available at the right time for the right customer, supported by intelligent, personalized marketing – aimed at guiding consumers to the products that need shifting in the supply chain.

This is all about ensuring that customers are provided with the convenience they expect; the 2020 e-commerce boom means that consumer convenience should now be the number one priority for all retailers. The highly competitive nature of the industry now means that brands can’t afford to have stockouts or miss sales opportunities; it’s imperative that retailers are optimizing stock levels across channels and locations to deliver the convenience the modern day shopper now demands, both in store and online. 

There are big opportunities there for the taking for retailers in 2021, despite increased competition and a challenging market. Being able to predict demand accurately and linking this to other parts of the business – connecting the dots across the entire retail operation – will help in achieving the endgame of delivering ultimate convenience to customers.

To do this successfully, building upon the digital transformation work of the previous year should be high up on the priority list for any retailer, utilizing all available data points and Decision Intelligence to work smarter, not harder. Those who don’t do this will simply be left trailing behind. In the early months of 2021, I expect we’ll see a substantial gap created as the acceleration of businesses who were already able to deploy elements of their digital transformation roadmap last year start to not just survive, but thrive.

You’re no doubt back at your desk (sorry – your kitchen table!) after the Christmas period, and your attention will now be on preparing for what’s to come over the next twelve months, from hitting the ground running to planning those all-important Black Friday campaigns (yes, it’s time to start thinking about those already!)

Everyone needs a day or two to get back into the swing of things after the festive holidays, so as a starter for ten, we’ve outlined the ultimate list for those working in the retail sector: the metrics that matter for your 2021 retail strategy. So, whether you work in sales and marketing, merchandising and buying, or supply chain and fulfillment, take a look at the below – if you can win in these areas in 2021, you’ll have the edge on your competitors and be in the best place possible to navigate through another difficult year.

Retail strategy 2021: marketing metrics

Fundamentals: Day-to-day metrics

• Lower your bounce rate
• Increase views of pages per session
• Drive higher revenue-per-click
• Lower your unsubscribe rates
• Increase click-through-rates
• More items added to baskets

Key indicators: Mid-level metrics

• Control your trading ability
• Increase purchase frequency
• Increase average order values
• Increase customer lifetime value
• Reduce customer acquisition costs

Strategic: High-level management metrics

• An effective channel-mix strategy
• Converting one-time users into returning users
• Cost savings

Merchandising metrics that matter in 2021

Fundamentals: Day-to-day metrics

• Rate of sale
• Weeks of cover
• GP%
• Gross profit variance

Key indicators: Mid-level metrics

• EBIT contribution by line
• GPV vs GP%
• Markdown cycle
• Net margin/profit
• Average Transaction Value (ATV)
• Range planning/space planning
• Pounds per square metre/foot
• Sell-through

Strategic: High-level management metrics

• Time saving
• Working capital employed (ROCE/GMROI)
• Regional web sales vs store locations
• Promotions to ease working capital

Supply chain: key metrics for your retail strategy

Fundamentals: Day-to-day metrics

• Average daily pack wait time
• Cost-per-minute and daily pack wait time cost
• Average wave closure time
• Cost-per-minute of wave
• Average pick time and number of picks of hour
• Average sort time
• Average time between picks
• Put-away time
• Throughput per item

Key indicators: Mid-level metrics

• Picks per person, per hour
• Wave planning
• Pick and pack efficiency
• Pick face optimization
• Distribution resource planning
• Put-away strategy
• Zone planning
• Workforce planning
• Truckload utilization
• Transport planning
• Pick-route planning
• Mis-service
• Adherence to customer SLAs and service upgrades
• Capacity and utilization planning
• Cost per wave
• Operational downtime
• Returns planning and reverse logistics
• Time to receiving and pick location

Strategic: High-level management metrics

• Cost per item
• Item throughput
• Total order numbers
• Expedited service
• Cost of mis-service and unfilled orders
• Increased return on capital employed (ROCE)

Hopefully the majority of these metrics will already be on your business’ radar for the year to come, but this list should act as a handy checklist for you and your colleagues as we all navigate our way through the coming months.

If you think we’ve missed any important ones, or would like to chat through some of the ways we’re helping the likes of ASOS, boohoo and PrettyLittleThing win big with these metrics, we’d love to hear from you!

There are two main problems that those working in these high pressure roles face – this year more so than ever. The devastating impact of 2020’s ongoing coronavirus pandemic and the temporary closure of high street stores has played havoc from a stock point of view. It’s resulted in huge amounts of terminal stock being left untouched in warehouses and shops; stock that is tying up valuable capital. How can teams shift this stock in the most profitable manner? How can they ensure they start the new year with an optimized inventory?

This leads us to problem number two: unpredictable consumer behavior. How can teams even begin to make the right decisions around products, quantities, sizes and styles when the future is so hard to predict? This year, customer shopping habits have, and will continue to, change at a rapid pace. This makes the time-poor merchandising team’s already difficult job all the more complicated.

Shifting stock profitably and predicting customer behavior

Over the coming months, the priority for merchandisers now has to be on acting quickly to solve these two core problems – clearing stock profitability and better understanding unpredictable customer behavior. 

For us, it’s about using your business’ data to its full potential to help drive invaluable insights and assist with efficient and effective decision making. Merchandisers today need to work in a more agile way when it comes to making calls on stock allocation, replenishment, buying and markdowns. While teams may know and trust the spreadsheets that power their day-to-day role – and with good reason, given the years they’ve spent perfecting these crucial tools – they simply don’t have the time to comb through every single cell, row and column to guarantee each decision – across every single item – is the right one. A new way of working is needed – and new technology is ready to power the future of merchandising as we approach 2021.

Inventory optimization with AI

Step forward AI-powered forecasting and inventory optimization. This has the potential to do the heavy-lifting and number-crunching for merchandisers, providing intelligent decisions taking into account all of your data – even factoring in sources like point of sale data and web data that merchandising teams normally wouldn’t have access to.

With a more holistic view of data from across the business, you can more accurately predict consumer behavior, even in these turbulent times, and get real-time, intelligent recommendations to make better decisions around buying, rebuying, allocation and pricing. This is all about augmenting and enhancing the merchandiser’s day-to-day role, taking away some of the more manual elements and freeing up valuable time which allows you to focus on strategy. For instance, this could be sending the perfect amount of a certain line to a particular store to capitalize regional demand, or it could be marking those trouble lines down to the perfect level in order to achieve the desired rate of sale, without giving away any unnecessary margin. In short, it’s about technology working in tandem with the human elements of context, creativity and empathy.

AI, of course, could not have helped retailers predict the impact of COVID-19. However, what it can do is ensure you’re making the right decisions in response to it. With AI, you can quickly and accurately leverage your data to improve forecasting and optimize stock and allocation during this important period. This will ensure you’re heading into the business end of the retail calendar in the strongest position possible, simply by being able to extract every last drop of value from your masses of data currently scattered across your business.

Take a step back and ask yourself some very important questions. Are you completely confident that you’re buying the right products, in the right quantities? Are you in a position to react to changing customer behaviors in an agile way? Do you have enough time to focus on planning and strategizing in your role as a merchandiser? 

If the answer to any of these is ‘no’, you need to rectify this while there’s still time. A failure to address the two main problems highlighted in this article will have a detrimental impact on your business’ health and how you’ll be able to operate over the coming months. The time for AI is now.

You probably feel like Black Friday 2020 has only just been and gone, but we’d generally recommend starting your preparations for the next one more or less a year in advance! If you could do with a helping hand for Black Friday 2021, we’ve pulled together a handy list of some of the key metrics that your supply chain team needs to be focusing on in order to make a success of this crucial weekend. 

Presenting the metrics that matter for your Black Friday supply chain strategy in 2021…

Fundamentals: Day-to-day metrics

• Average daily pack wait time

• Cost-per-minute and daily pack wait time cost

• Average wave closure time

• Cost-per-minute of wave

• Average pick time and number of picks of hour

• Average sort time

• Average time between picks

• Put-away time

• Throughput per item

Key indicators: Mid-level metrics

• Picks per person, per hour

• Wave planning

• Pick and pack efficiency

• Pick face optimization

• Distribution resource planning

• Put-away strategy

• Zone planning

• Workforce planning

• Truckload utilization

• Transport planning

• Pick-route planning

• Mis-service

• Adherence to customer SLAs and service upgrades

• Capacity and utilization planning

• Cost per wave

• Operational downtime

• Returns planning and reverse logistics

• Time to receiving and pick location

Strategic: High-level management metrics

• Cost per item

• Item throughput

• Total order numbers

• Expedited service

• Cost of mis-service and unfilled orders

• Increased return on capital employed (ROCE)

Win in these areas, and you win the Black Friday battle

Ideally, your supply chain, operations and logistics teams will already be tracking the majority of these metrics, exploring the different areas and processes you can optimize in order to get those Black Friday orders out of the door as efficiently as possible. If not, there are a number of ways artificial intelligence (AI) and a more intelligent view of business-wide data can help to make your Black Friday supply chain strategy highly effective. 

At Peak, we’re working with brands like ASOS to shape warehousing and supply chain strategies across Black Friday and beyond. From maximizing truck capacity to optimizing wave planning and pick face processes, AI can play a crucial role in ensuring you’re meeting your OTIF requirements and keeping your customers happy at this important time of year.

We always advice our retail customers to start planning for Black Friday more or less a year in advance, so hopefully, you’re well on top your 2021 Black Friday plans. If not, we’ve compiled a handy list of some of the key metrics that you need to be focusing on across your merchandising and planning not just over the weekend, but all year around. 

Merchandisers, presenting the metrics that matter for your Black Friday markdown strategy in 2021…

Fundamentals: Day-to-day metrics

• Rate of sale 

• Weeks of cover

• GP%

• Gross profit variance

Key indicators: Mid-level metrics

• EBIT contribution by line

• GPV vs GP%

• Markdown cycle

• Net margin/profit

• Average Transaction Value (ATV)

• Range planning/space planning

• Pounds per square metre/foot

• Sell-through

Strategic: High-level management metrics

• Time saving

• Working capital employed (ROCE/GMROI)

• Regional web sales vs store locations

• Promotions to ease working capital

Win in these areas, and you win the Black Friday battle

Hopefully you’re already tracking the majority of these metrics, and exploring different avenues and approaches to help you achieve success across the board. If not, you need to be looking at how you can win in these areas to make your Black Friday markdown strategy as effective as possible.

At Peak, we’re working with a wide range of retailers to help them shape their strategies for the coming months. Since the pandemic, we’ve seen an increasing number of businesses begin to better understand the importance of their data in driving a competitive advantage. When this data is leveraged by artificial intelligence (AI), it drives smarter decision-making and optimized processes across the business, helping you to positively impact all of the above metrics in your merchandising efforts as part of your Black Friday markdown strategy.

However, now it’s time to look ahead, and ensure that you’re focusing on building brand loyalty across the course of what will no doubt be another difficult year. To help you get a head start on your 2021 Black Friday plans (that’s right – we recommend starting to plan your Black Friday marketing strategy for 2021 more or less straight away!) we’ve compiled a handy list of some of the metrics that you need to be measuring throughout the year to make the weekend a success. 

Presenting the metrics that matter for your Black Friday marketing strategy in 2021…

Fundamentals: Day-to-day metrics

Lower your bounce rate: Are you driving qualified traffic to your website and dropping them in the right places?

Increase views of pages per session: Optimize the user journey to drive increased traffic across your site.

Drive higher revenue-per-click: Maximize the effectiveness of your website to drive sales. Enhancing your customer’s brand experience will result in more effective traffic.

Lower your unsubscribe rates: Keep customers engaged throughout the year to win big on Black Friday – blanket email campaigns are no longer good enough!

Increase click-through-rates: Drive and optimize more engagement on posts and ads to drive site visits.

More items added to baskets: Affect multiple purchases by providing customers with intelligent recommendations.

Key indicators: Mid-level metrics

Control your trading ability: Optimize recommended products across your digital network, promote margin-rich items and know which items have stock issues.

Increase purchase frequency: With the right nurturing throughout the year, you can ramp up customer spend on Black Friday and beyond.

Increase average order values: Tempt customers to purchase more with intelligence-powered recommendations and a seamless user experience.

Increase customer lifetime value: Drive brand loyalty amongst your customers with personalized comms throughout the year.

Reduce customer acquisition costs: Know who your ideal customers are and reduce CAC by targeting them at the right time, with the right message, via the right channel, and take into account EBITDA contribution by channel.

Strategic: High-level management metrics

An effective channel-mix strategy: Effectively optimize your channel mix strategy over time, ensuring long-term profitability and enhanced customer experience.

Converting one-time users into returning users: Build a loyal customer base who keep coming back for more.

Cost savings: Reduce costs across the business as a result of improving the customer experience. Increased economies of scale and cost savings across the customer journey touchpoints are a result of a far greater experience.

Win in these areas, and you win the Black Friday battle

Hopefully you’re already tracking the majority of these metrics, and exploring different avenues and approaches to help you achieve success across the board. If not, you need to be looking at how you can win in these areas to make your Black Friday marketing strategy as effective as it can be.

At Peak, we’re working with a wide range of retailers to help them shape their strategies for the coming months. Since the pandemic, we’ve seen an increasing number of businesses begin to better understand the importance of their data in driving a competitive advantage. When this data is leveraged by artificial intelligence (AI), it drives smarter decision-making and optimized processes across the business, helping you to positively impact all of the above metrics in your marketing efforts.

Who needs to care about AI and machine learning in a retail business?

Tom: I think it depends on the solution. Leadership buy-in isn’t crucial, in my experience, but you’ve got to be able to take people on the journey with you and educate continually. It spreads then. What I found was, if there’s people who don’t get it straight away, the business case and the numbers do the talking. Like anything, backing things up soon starts generating interest internally, which I think is the case for any new tech adoption in the business. 

How would you take the customer on the AI journey? How do you get buy-in from teams?

Tom: It’s all about empathy. Everyone says “I understand you can optimize with AI,” but how does it come into my world?” There’s the vision piece, which is important, but you need to break it down with end users and how it’ll super-power their processes. When I was at Footasylum, it was about giving people ownership of elements of the AI journey. It can often feel a little ambiguous at first in its exact application, but get clarity early and people will start to see the value and embrace it. You can then start to see that culture shift across the business, that AI is a cool thing that adds value – especially when it’s backed up by results.

What is the explainability of a system like CODI, and how important is that part of it?

Tom: It’s a black box for us. That’s where we win at Peak, because there is full visibility of the data sources and all the metadata that lives in there. 

Manjit: I think sometimes it’s always building towards it, so as you build your AI models to work with people and to work with some of that data. I worked with somebody that was a non-retail customer but they loved seeing that analysis before we started to build the models. It really helps them to go. “are we looking at the right problem? Have we got the right data? Is it showing us the things we expected to see?” Sometimes, taking people on that journey is really important and then you get to the point, working with some of the customers I have, where they are more interested in the output, and the AI is thought of as a given – but people need to go on that journey for themselves. 

Tom: When that explainability exists, it allows for more trading strategy to come from it because it kicks the retailer on to go “if we can do that, then what about this?” and so on. This is really exciting and adds that extra value to the explainability, the evolution of it and the collaborative approach that we use. The whole ambiguity around AI as a topic is resolved by its explainability.

Any advice around good use cases to start your AI journey and areas to avoid?

Tom: This depends on the business. I have found that starting small really pays off in the long run, and trying to take a lot on straight away is not that practical – and a reason why many companies fail when deploying AI. I think it’s important that you don’t become distracted and pulled onto a new project, but instead take small steps on a longer journey, where you see results pull through. This, in turn, will make it easier to get things moving throughout the company. 

Right now it would be project specific to the demands that are present in your business. But, starting too big is not productive, whereas breaking the process down into smaller pieces is advisable. Then, culturally in a business, an AI does draw attention and you can ride momentum through the business as people become more interested in what it is you are doing. That would be my one main piece of advice. 

We have challenges across our retail business that AI could solve – but where do we start? What’s your advice for taking those first steps?

Tom: At Footasylum, we started small. We were interested in personalization and were ready to learn about it. But once we started building the customer piece, we ended up with a kind of flywheel analogy, with the customer on one side and demand on the other. If you’re creating demand from the customer side, the flywheel speeds up and you need to meet the demand, if you introduce AI there too, it’ll speed up even more. We also looked at some supply stuff too, helping to optimize inventory through the last Christmas trading period, which was valuable to the overall ecosystem that led to a great period for us. Lots of people will say “we don’t know where to start,” but you can quickly break it down. At Peak, tangible results is the bit we want to see, and that’s what the customer wants to see too. It’s about starting on the journey to get there – and pace is key.

I work in insurance, where price comparison sites are king. There are some retail equivalents popping up but haven’t truly grained traction yet – do you think these will grow?

Tom: This is a question around delivery, where the battleground around websites is essentially in the checkout. Delivery is getting cheaper, you’ve probably done it as a consumer – I’ve done it myself; you hover over the pay securely button when the delivery isn’t free, depending on what it is. When you’ve been at the checkout of a website, the reason “I couldn’t get something delivered where I wanted, when I wanted,” needs to disappear for consumers as an excuse used by businesses, because it’s just not good enough. 

This is where we have found deploying AI for people can help optimize where their inventory is, in order to help the whole fulfillment process, whether it’s free or otherwise as long as it’s available. This deployment of AI is not going away, it will only get more advanced and be more of a help to businesses. It does get more complex if you have multiple distribution centers (DCs), if you’re fulfilling from stores or if you’re dealing internationally. But, we were talking to a retailer this morning that has multiple DCs around the world, and we’re trying to optimize the fulfillment process for them at this very moment. It’s something we are engaging in a lot of conversations about. 

Can you see a role for AI in marketing town centers and the diverse offers that exist there?

Tom: For multi-channel retailers specifically, it’s almost like the traditional P&L method is not fit for purpose in terms of keeping stores and websites separate, which still exists in most businesses. I think if you were starting from scratch today, you would not have that. In terms of delivering regional marketing, I have been talking about the idea of trading in zones, where you have channelless zones that allow you to focus on areas of particular interest. Maybe you have specific stores, products or brands that exist within your network and have an appetite towards them. 

Something we’ve done at Peak is we ingest multiple data sources into one place, which allows a visibility for localized digital marketing campaigns (although the actions can be physical), which comes back to CODI where all your data is interconnected, allowing for better visibility, agility, decision making and the actionable outcomes that come from it. I would say, on the town center example, whilst it is a micro-topic, we should be moving towards this more channelless vision and aggregation of data where we run the machine learning models across to help you make those marketing moves appropriately.

Merchandising is a great fit for AI but we are struggling to get it going. Do you have any thoughts on this? 

Tom: What we’re seeing in merchandising is that, culturally, it can be a challenge to embed an AI system because merchandisers are probably already hamstrung by poor systems (generally speaking) and siloed data and so on. Whereas, it’s actually one of the most significantly valuable areas of opportunity, certainly in the work we’ve been able to do with people. There are opportunities around allocation of stock, markdown optimization, rebuying, buying, range planning; all disciplines that involve merchandising both multi-channel and pure play retailers. There is so much opportunity. Most of this is the common sense that the merchandisers already want to implement, but they don’t have the technical systems to implement these changes and outcomes. We currently have exciting things happening within merchandising for those traders and planners. It is super, super valuable and probably my favorite area – even though I’ve done a lot with Customer Intelligence, Demand Intelligence is just as interesting and really cool.

How robust have Peak’s AI models been during COVID-19? Did businesses working with Peak deploy their AI model and expect it to be totally hands off and fine without intervention? 

Manjit: I think the AI has become more critical, and some of our retail customers have been asking, “what is happening? Can you give us the early signals?” This has been by day and by week, whether that’s in the UK or international markets. What we’ve found is, yes, we realize AI models are built on historical data, but because they are live and working today, from a trading perspective it gives people a view of what happened yesterday and what’s going to happen in the next couple of days. Whether that’s on merchandise, rebuy products or whether it’s just on general trading decisions.

Tom: To build on that, it gives a vital insight into the future, built on data that already exists. But because it can augment that data and help it look into the future for certain business outputs, it’s so crucial. With Customer Intelligence, aspects like customer facing advertising, segmentation within databases, how you maintain customer retention and new customer acquisition, it’s about how you can tweak the models to optimize for certain outputs. Take Ad Optimization for one; companies like Facebook and Google do not optimize ads for profit, whereas we do help customers optimize for profit, so there’s been a pivot from “we just want more exposure” to “how much money can we gain from each pound spent?” Helping to optimise profit has been a key focus in the current market.

We hope you have enjoyed our AI in retail blog and most of your questions have been answered! If you want to learn more about the relationship between retailers and AI, check out a few other pieces we have written below ?

? How to optimize your retail markdown strategy

? Maximizing e-commerce performance during lockdown

? Why looking to the future is more important than ever for retailers

The big question, now, is how do retailers shift all of that excess, terminal stock that has been gathering dust in the warehouse throughout the COVID-19 pandemic? This is the short to medium term battleground for most retail businesses.

Sales bonanzas?

This will no doubt be the go-to solution for many businesses looking to shift terminal stock as quickly as possible. But, is dramatically slashing the prices of those unused spring wardrobe essentials really the best approach to take? While this stock does need selling, it’s imperative for businesses to get rid of this surplus in the most profitable way. To do this, retailers must ensure that the margin lifespan of every single product is maximized, avoiding unnecessary discounting by taking a “right price, first time” approach to their discounting.

Optimizing your markdown strategy

Step forward artificial intelligence (AI), and a data-driven approach to defining your pricing and markdown strategy. Of course, merchandisers, traders and planners making data-driven decisions around pricing strategy is nothing new. But doing this without AI isn’t easy; many teams find themselves entrenched in legacy processes and disparate business systems, with the bulk of their working day spent number-crunching in spreadsheets, relying on time-poor personnel and ‘gut feel.’

This is where AI can play a crucial role in helping to make better, data-driven decisions that drive tangible commercial outcomes. By leveraging data, in any format or structure, from across the entire retail business – whether it’s customer, transactional or website data – AI can forecast the predicted demand of products to a higher degree of accuracy. We call this a Predictive Demand View. With this AI-powered view, you can truly plan for profit and make more informed pricing decisions.

Businesses can utilize AI-driven markdown scenario analysis to identify the optimal pricing for each product. The technology recommends an advisory “perfect price range” on an individual product level, based on a wide range of factors and demand signals that wouldn’t normally be visible. This helps merchandisers with their markdown decision making and ensures that initial markdowns aren’t too severe.

The results can be truly amazing. Our merchandising solution, Inventory Intelligence, was recently utilized by a leading UK multi-channel retailer. By applying AI-powered pricing recommendations to a segment of its inventory across online and in store, the retailer has enjoyed some huge results. Utilizing price range suggestions on just 15% of the stock file, the merchandising team was able to optimize its markdowns to drive a huge saving of $3 million (£2.4 million). To put this into perspective, this figure equates to additional margin worth approximately 1% of the retailer’s overall turnover. Demand Intelligence is also leading to increased team productivity and significant time savings, with AI effectively super-powering the end user’s output.

We’re at a crucial juncture in retail at the moment, and making the right decisions to maximize your business’ profitability has never been more important. Using AI, you can super-charge your merchandising team’s output and make smarter, profit-driving decisions to navigate this important period successfully.