Campaign Analytics

Overview

Monitor campaign performance through comprehensive analytics that track point earning, point claiming, and overall engagement during your campaign periods. Compare campaign performance against your store's average to measure effectiveness and calculate return on investment.

Analytics are available during active campaigns (real-time updates) and after campaigns complete (final results), allowing you to monitor progress and learn from results.

Accessing Campaign Analytics

1. Navigate to Loyalty Program > Campaigns
2. Click on campaign name
3. View Analytics tab
4. Review all metrics and charts

Available For:

• Active campaigns (updates in real-time)
• Scheduled campaigns (no data yet)
• Completed campaigns (final results)
• Deleted campaigns (historical data preserved)

Key Metrics

Average Points Earned Per Day

Track the average number of points customers earn per day during the campaign.

Calculation:

Total points earned during campaign ÷ Number of campaign days

Example:
Campaign: 7 days
Total points earned: 35,000 points
Average: 35,000 ÷ 7 = 5,000 points per day

Comparison:

• Shown alongside store average for same time period
• Indicates campaign effectiveness
• Higher than average = successful campaign

What This Tells You:

• Campaign driving point earning
• Customer engagement level
• Earning velocity during promotion

Example:

Campaign Period Average: 5,000 points/day
Store Average: 3,000 points/day
Impact: 67% increase in daily earning

Average Points Claimed Per Day

Track the average number of points customers redeem per day during the campaign.

Calculation:

Total points claimed during campaign ÷ Number of campaign days

Example:
Campaign: 7 days
Total points claimed: 14,000 points
Average: 14,000 ÷ 7 = 2,000 points per day

Comparison:

• Shown alongside store average
• Indicates redemption behavior during campaign

What This Tells You:

• Are customers redeeming during campaign?
• Do they save for later or redeem immediately?
• Campaign impact on reward fulfillment

Insight Patterns:

High earning + high claiming = Strong engagement
High earning + low claiming = Customers saving points
Low earning + high claiming = Using previous balance

Example:

Campaign Period Average: 2,000 points/day
Store Average: 1,500 points/day
Impact: 33% increase in daily claiming

Points Earned Per Day (Time Series)

Daily breakdown of points earned compared to store average.

Features:

• Shows daily earning throughout campaign
• Comparison to store average baseline
• Empty dates filled with 0 (if no activity)
• Multi-day grouping for long campaigns (e.g., 30+ days grouped by week)

What To Look For:

Peak Days:

• Which days had highest earning?
• Weekend vs. weekday patterns
• Campaign launch surge
• End-of-campaign rush

Trends:

• Growing momentum over time?
• Declining interest mid-campaign?
• Consistent performance?
• Unusual spikes or drops?

Example Analysis:

Day 1: 8,000 points (launch surge)
Day 2-3: 5,500 points (settling)
Day 4-5: 4,000 points (weekdays slower)
Day 6: 7,000 points (weekend spike)
Day 7: 10,000 points (last-day rush)

Insight: Launch and urgency drive peak performance
Action: Communicate "ending soon" for future campaigns

Points Claimed Per Day (Time Series)

Daily breakdown of points claimed compared to store average.

Features:

• Shows daily claiming throughout campaign
• Comparison to store average baseline
• Empty dates filled with 0 (if no activity)
• Multi-day grouping for long campaigns

What To Look For:

Redemption Patterns:

• Do customers redeem during campaign or after?
• Are they using earned campaign points immediately?
• Any delayed redemption behavior?

Correlation with Earning:

• Does high earning lead to high claiming same day?
• Lag between earning and claiming?

Example Analysis:

Earning peaked Day 7 (10,000 points)
Claiming peaked Day 8-10 (after campaign ended)

Insight: Customers earn during campaign, redeem after
Action: Extend future campaigns or add "claim bonus" period

Additional Points Issued

Total bonus points awarded due to the campaign.

Calculation:

For Points Per Order Campaign:

Number of orders × Bonus points per order

Example:
Campaign: 500 points per order
Orders during campaign: 150 orders
Additional points: 150 × 500 = 75,000 points

For Multiplier Campaign:

(Total points with multiplier) - (Total points without multiplier)

Example:
Points earned with 2× multiplier: 100,000 points
Points without multiplier would be: 50,000 points
Additional points: 100,000 - 50,000 = 50,000 points

What This Tells You:

• True cost of campaign in points
• Campaign investment vs. return
• Budget for future campaigns

Compare To:

Additional Points Issued: 75,000 points
Points-to-Currency Ratio: 100:1
Cost in Discounts: $750

Revenue During Campaign: $15,000
Campaign ROI: $15,000 revenue for $750 in future discounts

Using Analytics to Improve Campaigns

Scenario 1: Strong Start, Weak Finish

Data:

Day 1-2: 8,000 points/day (well above average)
Day 3-5: 4,000 points/day (at average)
Day 6-7: 3,000 points/day (below average)

Analysis:

• Launch excitement strong
• Interest declining over time
• No urgency toward end

Actions for Future:

• Add "last chance" communication
• Introduce escalating bonus (higher points toward end)
• Shorten campaign duration
• Create mid-campaign reminder

Scenario 2: Low Claiming During Campaign

Data:

Average Points Earned: 6,000/day (100% above average)
Average Points Claimed: 1,200/day (20% below average)

Analysis:

• Customers earning bonus points
• But not redeeming during campaign
• Saving for later use

Actions for Future:

• Add limited-time reward during campaign
• Communicate exclusive campaign rewards
• Create urgency around redemption
• Consider "claim bonus" during campaign

Scenario 3: Declining Weekend Performance

Data:

Weekdays: 5,000 points/day average
Weekends: 3,000 points/day average

Analysis:

• Unexpected pattern (weekends typically higher)
• Possible communication gap
• Customer shopping habits different

Actions:

• Increase weekend-specific communication
• Send Friday reminders about weekend campaign
• Analyze if product/service is weekday-focused
• Test weekend-only campaigns

Scenario 4: Excessive Point Liability

Data:

Additional Points Issued: 500,000 points
At 100:1 ratio: $5,000 in future discounts
Revenue Increase: $2,500

ROI: Negative (-$2,500)

Analysis:

• Campaign cost more than it generated
• Multiplier or bonus too high
• Need to adjust economics

Actions:

• Lower multiplier for future campaigns (2× → 1.5×)
• Reduce bonus points per order
• Shorten campaign duration
• Better target high-value customers

Comparing Campaigns

Use analytics to compare multiple campaigns:

Campaign Comparison Matrix

Campaign	Duration	Type	Avg Earn/Day	Avg Claim/Day	Bonus Points	Revenue
Holiday 2023	30 days	2× multiplier	6,500	2,800	195,000	$45,000
Black Friday	3 days	1,000/order	12,000	4,200	150,000	$38,000
Spring Sale	14 days	1.5× multiplier	4,200	1,900	42,000	$18,000

Insights:

• Black Friday: Highest intensity (short, focused)
• Holiday: Best overall revenue
• Spring Sale: Most efficient (lowest bonus point cost)

Best Performing Campaign Attributes

From Your Data:

• Optimal duration: 7-14 days
• Best multiplier: 1.5×-2×
• Bonus per order: 500-750 points
• Best timing: Black Friday, December holidays
• Communication: 5 days advance + daily reminders

Campaign Performance Benchmarks

Earning Lift

How much did earning increase during campaign?

Calculation:

((Campaign Avg - Store Avg) ÷ Store Avg) × 100

Example:
Campaign: 5,000 points/day
Store: 3,000 points/day
Lift: ((5,000 - 3,000) ÷ 3,000) × 100 = 67% lift

Benchmarks:

• Excellent: 50%+ lift
• Good: 25-50% lift
• Moderate: 10-25% lift
• Weak: <10% lift (consider adjusting)

Claiming Lift

How much did claiming increase during campaign?

Calculation:

((Campaign Avg - Store Avg) ÷ Store Avg) × 100

Example:
Campaign: 2,000 points/day
Store: 1,500 points/day
Lift: ((2,000 - 1,500) ÷ 1,500) × 100 = 33% lift

Interpretation:

• High earning + high claiming lift = Complete engagement
• High earning + low claiming lift = Building point balances
• Low earning + high claiming lift = May need different approach

ROI Calculation

Was the campaign worth the point cost?

Simple ROI:

Revenue During Campaign ÷ Point Cost = ROI

Example:
Revenue: $15,000
Point Cost: $750 (in future discounts)
ROI: $15,000 ÷ $750 = 20× return

Considerations:

• Points redeemed later (not immediate cost)
• Customer lifetime value increase
• Brand awareness and engagement value
• Competitive positioning

Monitoring Schedule

During Campaign

Daily:

• Check points earned vs. target
• Monitor claiming patterns
• Track order volume
• Review customer feedback

Actions:

• Adjust communication if needed
• Extend if performing exceptionally
• Add mid-campaign boost if underperforming

After Campaign

Immediately After (1-7 days):

• Review final metrics
• Compare to goals
• Calculate additional points issued
• Document key learnings

1-2 Weeks After:

• Monitor post-campaign claiming
• Track if customers redeem earned points
• Measure lasting impact on engagement

1 Month After:

• Calculate true ROI
• Review customer retention
• Compare to other campaigns
• Plan improvements for next campaign

Related Pages

• Campaigns Overview - Campaign configuration and management
• Earning Rules - Base earning affected by campaigns
• Customer Tiers - Tier multipliers stack with campaign multipliers
• Transactions - View campaign-tagged transactions

Summary

Campaign analytics provide comprehensive visibility into campaign performance, allowing you to measure earning lift, claiming patterns, and return on investment. By monitoring daily time series data and comparing against store averages, you can identify what works, optimize future campaigns, and ensure your promotional investments drive meaningful engagement and revenue.

Use these insights to refine campaign duration, multiplier values, timing, and communication strategies for maximum impact while maintaining sustainable program economics.