Data doesn’t just inform decisions, it transforms them. Yet most ecommerce brands approach peak season planning like a basic spreadsheet exercise, missing the strategic insights that separate successful Q4s from disappointing ones.
With peak season planning underway, we’ve revisited key insights from our webinar “Data: Bring Clarity To The Peak Season Chaos” (part of our Peak Season Playbook series). As attribution becomes more complex and competition intensifies, the frameworks we explored have proven essential for navigating the challenges ahead.
Here’s what we’ve learned works best, with practical guidance to help you optimise when it matters most.
Standard Forecasting Falls Short
Most brands approach peak season forecasting like accountants: take last year’s numbers, add a growth percentage, and build a plan around it. But this approach misses crucial context.
The Challenge: Revenue patterns aren’t just seasonal, they’re the result of specific marketing decisions, promotional strategies, and how you allocate your budget. Without adding the context of these drivers, forecasts become unreliable.
As our Head of Data & Analytics explains: “The typical finance analyst approach can give us some context, but what we’re really looking to do is add the context of changing marketing budgets. We need to understand how much of that revenue increase was driven by our spend and marketing decisions versus natural seasonality.”
A Better Approach: Context-Driven Forecasting
Instead of starting with revenue targets, build forecasts that account for:
- How channels respond differently during high-demand periods versus business-as-usual
- The impact of promotions on both customer acquisition volume and quality
- Spend scalability limits across different channel types and campaign objectives
- External factors like competitive activity, inventory constraints, and market conditions
Practical Implementation: Create scatter plots with your historical spend on the x-axis and revenue on the y-axis. Colour-code the data points, red for peak season days, black for regular periods. This visualisation reveals how your efficiency curves shift during high-demand periods, helping you plan more accurately.
Key Insight: During business-as-usual periods, channel effectiveness often caps out after a certain spend level. But during peak season, higher demand allows you to scale spend significantly while still hitting your target performance metrics (e.g. CPA or ROAS).
Peak Season Customers Behave Differently (And Your Targets Should Too)
Here’s what many brands overlook: Customers acquired during peak season often have different lifetime value profiles than those acquired during regular periods. Using the same acquisition cost targets year-round can lead to suboptimal decisions.
Three Critical Considerations
1. The Impact of Discounts on Customer Quality Deeper peak season discounts often attract more price-sensitive customers. These cohorts may have different repeat purchase behaviours and lifetime values compared to customers acquired at full price.
2. Dynamics of a Gifting Business For brands where gifting is significant, customers may concentrate their repeat purchases around peak periods. A customer acquired in November might not make their second purchase until the following November, and this can skew your LTV and total revenue forecasts.
3. Choosing the right Analysis Window Standard 30-day LTV windows can miss important differences between customer cohorts. Some variations in customer behaviour only become apparent after 60+ days, particularly for subscription businesses where initial discounts might affect long-term retention.
The Solution: Analyse previous peak season customer cohorts separately from your overall customer base. If their LTV patterns differ significantly (and they often do), adjust your acquisition cost targets accordingly. This ensures you’re optimising for long-term profitability, not just short-term volume.
The Channel Strategy That Still Works Best
An Important Distinction: While Google Search should respond to demand spikes during peak season, Meta and other top-of-funnel channels perform better with consistent, steady spending patterns.
Why This Matters: The temptation during peak season is to react quickly to conversion spikes by increasing all channel budgets. However, different channels serve different purposes in the customer journey.
Understanding Channel Roles
Google Search: Captures existing demand. When people search more during peak season, it makes sense to increase search budgets to meet that demand, responding quickly to daily changes.
Meta/Top-of-Funnel: Builds awareness and creates demand. These channels work best when they can maintain a more consistent learning and audience development over time.
What Works in Practice:
- Scale gradually: Begin increasing Meta budgets in the weeks before peak conversion periods
- Maintain consistency: Keep steady daily spend rather than following the typical daily “spike-dip-spike” sales pattern
- Use appropriate objectives: Consider brand awareness and engagement campaigns to build presence cost-effectively ahead of peak when consumers are holding back on converting, planning for the sales ahead.
Attribution Consideration: Meta often appears disproportionately effective during sales periods, as faster customer actions and conversions amplify platform-reported performance. It’s important to account for this dynamic when setting targets or making budget allocation decisions based on ad-platform or website data, to avoid overvaluing short-term spikes at the expense of sustained growth.
The Attribution Triangle: Getting the Complete Picture
No single measurement approach gives you the full story of marketing performance. The key to better decision making is to combine three complementary measurement approaches, and analyse them within the context of your business.
The Three Points of Evidence
Platform Data (Touchpoint Attribution) provides real-time feedback that’s excellent for day-to-day optimisation within channels and tactical adjustments like creative testing. However, it struggles to compare very different channel types, such as Google Search versus Meta ads. Tools like Google Analytics, platform reporting, and advanced attribution solutions like Triple Whale and North Beam fall into this category.
Media Mix Modeling (MMM) helps with strategic budget allocation between channels by capturing the full funnel effect, including impression-driven conversions that touchpoint attribution misses. It requires rich data with significant budget mix changes over time and must carefully account for external factors like seasonality, promotions, and market conditions to provide valid insights.
Experimentation validates causal relationships and tests new approaches, providing definitive proof of what’s working. It’s essential for confirming MMM insights and testing hypotheses before making significant strategic changes.
The Foundation: Business Context
All three measurement approaches must be interpreted in the context of market conditions, your competitive landscape, business objectives, historical performance, and a timeline of events that impact marketing performance, such as PR mentions or influencer posts.
