Seasonal Patterns

Understanding Seasonality in MMM

Seasonality refers to regular, predictable variations in your KPI that occur at specific times. Decomposition helps isolate and quantify these seasonal effects.

Purpose: Identify seasonal patterns, quantify their impact, and align marketing strategies with natural demand cycles.

Types of Seasonality

Annual Seasonality

Yearly Patterns:

• Holiday periods (Q4 for retail)

• Summer vacation season

• Back-to-school period

• Tax season (for financial services)

In Decomposition:

• If you have a "Seasonality" group, shows these patterns directly

• Or visible in total KPI (black line) as regular annual rhythms

Quarterly Seasonality

Business Cycles:

• End-of-quarter budget spending (B2B)

• Fiscal year patterns

• Seasonal product demand

Example:

Q1: Baseline performance
Q2: 10% above baseline
Q3: 5% above baseline
Q4: 25% above baseline (holiday season)

Monthly Seasonality

Within-Year Patterns:

• Payday cycles

• Month-end effects

• Specific holiday months

Weekly Seasonality

Within-Month Patterns:

• Weekend vs. weekday

• End-of-week patterns

• Specific day-of-week effects

Finding Seasonality in Decomposition

Explicit Seasonality Group

If You've Grouped Seasonal Variables:

Look for "Seasonality" group contribution:

January Seasonality: $10,000
July Seasonality: $5,000
December Seasonality: $40,000

Pattern: Strong December peak, moderate January, low summer

What's Included:

• Holiday indicator variables

• Monthly dummy variables

• Seasonal indices

• Weather effects

Implicit Seasonality

In Base Group or Overall KPI:

View the black line (Actual) in main chart:

• Regular up-and-down patterns

• Peaks at same times each year

• Predictable cycles

Example - Retail:

Every year:
- January: Post-holiday dip
- Spring (Apr-May): Moderate
- Summer (Jun-Aug): Low season
- Fall (Sep-Nov): Building
- December: Peak (holiday)

Quantifying Seasonal Impact

Seasonal Lift Calculation

Compare Peak to Baseline:

December (Peak):
Seasonality Contribution: $50,000
Base Contribution: $100,000
Total: $150,000

Average Month:
Seasonality Contribution: $10,000
Base Contribution: $100,000
Total: $110,000

Seasonal Lift: $50,000 - $10,000 = $40,000
Percentage Lift: 36% above average month

Seasonal Index

Normalize Each Period:

Average Monthly KPI: $500,000

January Index: $400,000 / $500,000 = 0.80 (20% below average)
December Index: $700,000 / $500,000 = 1.40 (40% above average)

Use: Budget allocation, forecasting

Marketing Strategy by Season

Peak Season Strategy

High Natural Demand Period:

Characteristics:

• Higher absolute sales

• Strong seasonality contribution

• Competitive advertising

Marketing Approach:

Maintain Presence:

Don't go dark during peak demand
Capture your fair share
Defend against competitors

But Don't Over-Invest:

Marketing ROI may be lower (crowded market)
Focus on efficiency
Natural demand is already high

Example - Holiday Retail (Q4):

Seasonality drives: +$200,000
Marketing contribution: +$150,000 (lower ROI than off-season)
Total lift: $350,000

Strategy: Moderate marketing, let seasonality work for you

Off-Season Strategy

Low Natural Demand Period:

Characteristics:

• Lower baseline sales

• Negative or minimal seasonality contribution

• Less competitive advertising

Marketing Approach:

Strategic Investment:

Marketing can have higher incremental impact
Less competition for attention
Opportunity to build brand
Smooth demand cycle

Example - Summer for Winter Products:

Seasonality effect: -$50,000 (negative)
Marketing contribution: +$100,000 (higher ROI)
Net: +$50,000

Strategy: Invest to counteract seasonality, build off-season demand

Shoulder Seasons

Transition Periods:

Build Momentum:

• Ramp up before peak season

• Capture early demand

• Pre-season promotions

Extend Season:

• Keep demand going after peak

• Capture late buyers

• Clear inventory

Seasonal Budget Allocation

Traditional Approach (Follow Seasonality)

Allocate Budget to Match Demand:

Q1: 20% of annual budget (baseline)
Q2: 25% of annual budget (spring increase)
Q3: 20% of annual budget (summer dip)
Q4: 35% of annual budget (holiday peak)

Pros: Maximize absolute sales, capitalize on demand Cons: Higher costs in Q4, competitive market

Counter-Seasonal Approach

Invest More in Off-Season:

Q1: 30% of budget (build demand)
Q2: 25% of budget (maintain)
Q3: 30% of budget (combat summer dip)
Q4: 15% of budget (ride natural demand)

Pros: Better ROI, less competition, smooth demand Cons: Lower absolute sales, fight natural patterns

Balanced Approach (Recommended)

Combine Both Strategies:

High Season (Q4): 
- 30-40% of budget
- Maintain presence
- Don't over-invest

Shoulder Seasons (Q2, Q3):
- 25-30% of budget
- Build momentum
- Capture opportunity

Low Season (Q1):
- 20-25% of budget
- Strategic investment
- Efficiency focus

Seasonal Marketing Insights

Campaign Timing

Align with Seasonality:

Launch Before Peak:

Holiday season peaks in December
Launch campaigns in November
Build awareness before purchase intent peaks

Extend Seasons:

Back-to-school traditionally August
Start promotions in July
Extend through September

Channel Mix by Season

Different Channels Perform Differently by Season:

Example:

Summer (Low Season):
- TV less effective (people outdoors)
- Digital more efficient (targeted)
- Strategy: Shift to digital

Winter (High Season):
- TV more effective (people home)
- All channels competitive
- Strategy: Balanced mix

Promotional Timing

Leverage or Counter Seasonality:

With Seasonality:

Holiday promotions when demand is naturally high
Amplify natural demand
Example: Black Friday, Christmas sales

Against Seasonality:

Summer sales to combat low season
Create artificial demand peaks
Example: Summer clearance events

Year-Over-Year Seasonal Comparison

Track How Seasonality Changes:

December 2023:
Seasonality Contribution: $180,000

December 2024:
Seasonality Contribution: $210,000

Change: +17% seasonal effect

Insight: Holiday season getting stronger
Action: Plan for continued growth

Helps Identify:

• Strengthening seasonal patterns

• Weakening effects

• Structural market changes

• Consumer behavior shifts

Forecasting with Seasonal Patterns

Use Historical Patterns to Predict:

Step 1: Calculate Seasonal Indices

Each month's index = Month Average / Overall Average

Step 2: Apply to Forecast

Base forecast: $500,000/month
January index: 0.80
January forecast: $500,000 × 0.80 = $400,000

Step 3: Add Marketing Plans

Planned marketing contribution: +$100,000
Total January forecast: $500,000

Seasonal Analysis Checklist

Identify Patterns:

• [ ] Clear seasonal group in decomposition?

• [ ] Regular patterns in total KPI?

• [ ] Same peaks each year?

Quantify Impact:

• [ ] Peak vs. baseline difference?

• [ ] Seasonal lift percentage?

• [ ] Contribution by period?

Strategic Decisions:

• [ ] Budget allocation by season?

• [ ] Campaign timing optimized?

• [ ] Channel mix adjusted seasonally?

Year-Over-Year:

• [ ] Seasonal patterns strengthening?

• [ ] New patterns emerging?

• [ ] Historical trends continuing?

Common Seasonal Patterns by Industry

Retail:

• Q4 peak (holidays)

• January dip (post-holiday)

• Back-to-school (Aug-Sep)

Travel:

• Summer peak (Jun-Aug)

• Holiday periods (Thanksgiving, Christmas)

• Spring break (March)

Financial Services:

• Tax season (Jan-Apr)

• Year-end (Dec)

• Quarterly patterns

B2B:

• End-of-quarter budget spend

• Fiscal year-end

• Trade show seasons

E-Commerce:

• Cyber Week (Nov-Dec)

• Prime Day (July)

• Monthly payday patterns

Summary

Key Takeaways:

Seasonality Shows:

• Predictable demand patterns

• Natural business cycles

• Market rhythms

Use Seasonality To:

• Time campaigns effectively

• Allocate budgets wisely

• Forecast accurately

• Plan inventory and resources

Strategic Approaches:

• Follow seasonality (maximize peaks)

• Counter seasonality (smooth demand)

• Balance both (optimal approach)

Best Practices:

• Track year-over-year changes

• Adjust marketing by season

• Plan 12 months ahead

• Don't fight strong seasonality

Next Steps:

• Identify your seasonal patterns

• Quantify seasonal impact

• Develop season-specific strategies

• Monitor and adjust over time