The silent margin killer: how data latency undermines your promotions

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• Merchandisers still working from spreadsheets that are a week out of date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.