Buyer Journey Analysis: 2024 Data Deep Dive

What We Learned from 146 Cart Additions

Document Version: 1.0 Created: January 15, 2026 Author: Nick + Claude Data Period: January - December 2024 Data Source: Arlem Analytics (excludes Adelaide/internal traffic)

Executive Summary

We analyzed 122 real visitors who added items to cart in 2024 (excluding internal Adelaide traffic). This data reveals critical insights about the Arlem buying journey that directly inform our personalization and retargeting strategy.

Key Findings

Part 1: Session Patterns

How Many Visits Before Cart?

SESSION DISTRIBUTION FOR CART ADDERS
══════════════════════════════════════════════════════════════

Sessions    Count    %       Interpretation
────────────────────────────────────────────────────────────
1 session    77     63%     Impulse/decisive buyers
2 sessions   23     19%     Quick returners
3 sessions    5      4%     Considering
4+ sessions  17     14%     High-intent, stuck
────────────────────────────────────────────────────────────
Total       122    100%

KEY INSIGHT:
├── 63% add to cart on FIRST visit
├── But 37% need multiple visits
└── 14% visit 4+ times before cart add

What This Means for Personalization

Part 2: Time to Decision

For visitors who needed multiple sessions, how long did they take?

TIME FROM FIRST VISIT TO CART ADD (multi-session only, n=45)
══════════════════════════════════════════════════════════════

Time Period     Count    %       Interpretation
────────────────────────────────────────────────────────────
Same day         10     22%     Returned later that day
1-3 days          6     13%     Quick decision makers
4-7 days          7     16%     Week-long consideration
8-14 days         6     13%     Extended research
15-30 days        8     18%     Long consideration
30+ days          8     18%     Very long cycle
────────────────────────────────────────────────────────────
Average: 18.7 days

KEY INSIGHT:
├── 35% decide within 3 days
├── 36% take 1-4 weeks
├── 18% take MORE than a month
└── One visitor took 126 days (23 sessions!)

The 126-Day Visitor: A Case Study

One Sydney visitor visited 23 times over 4 months before adding to cart:

VISITOR JOURNEY: Sydney, iPhone user
══════════════════════════════════════════════════════════════

First visit: July 1, 2024 (via Google search)
├── Viewed Linen Bedhead Cushion
├── Compared colors (Stone, Natural)
├── Selected King Size, French Seam
├── Browsed to Boucle Bedhead
├── Compared Ecru vs Ivory
├── Added Neapolitan Cushion to cart
├── Added Boucle Bedhead (Ivory/King/French) to cart
└── Left without purchase

Then returned 22 MORE times over the next 4 months...

Last visit: November 4, 2024

Total time in funnel: 126 days
Total sessions: 23
Products in cart: 2 items (~$450)

What went wrong? This visitor was EXTREMELY interested but something blocked them:

• Price concern? (no discount offered)
• Uncertainty about fit/size? (viewed size guide?)
• Waiting for a sale?
• Life got in the way?

With personalization, we could have:

• Detected their high intent after 4-5 visits
• Shown a “Still deciding?” message
• Offered a 10% discount after visit 7-8
• Sent a cart recovery email (if we had their email)

Part 3: Traffic Sources

Where do cart adders come from?

TRAFFIC SOURCE BREAKDOWN
══════════════════════════════════════════════════════════════

Source              Count    %      Notes
────────────────────────────────────────────────────────────
Other                43     35%    Mostly direct/unknown
Google               24     20%    Search intent = buying intent
Instagram Click      20     16%    From link in bio
Instagram            20     16%    Direct IG traffic
TikTok                6      5%    Growing channel
Direct                6      5%    Returning visitors
Instagram Shop        2      2%    IG Shopping feature
Retarget              1      1%    Retargeting ads
────────────────────────────────────────────────────────────

COMBINED INSTAGRAM: 42 visitors (34%)
COMBINED GOOGLE: 24 visitors (20%)

Key Source Insights

Critical finding: Only 1 cart addition came from retargeting ads. This confirms what we identified in the Instagram Ads Strategy - retargeting was massively underutilized ($32 of $1,527 spend).

Part 4: Geographic Distribution

Where are cart adders located?

AUSTRALIAN STATE BREAKDOWN (n=68 Australian cart adders)
══════════════════════════════════════════════════════════════

State     Count    %      Population Share    Over/Under Index
────────────────────────────────────────────────────────────
NSW        29     43%         32%              +34% over-index
VIC        22     32%         26%              +23% over-index
QLD        13     19%         20%              On par
WA          3      4%         11%              -64% under-index
TAS         1      1%          2%              Under-index
SA          0      0%          7%              (excluded - internal)
────────────────────────────────────────────────────────────

KEY INSIGHT:
├── NSW and VIC dominate (75% of AU cart adders)
├── QLD on par with population
├── WA significantly under-represented
└── Consider WA-targeted campaigns

Part 5: The Purchase Gap

The Problem: We Can’t Track Purchases

Our funnel shows:

• ViewContent: 2,867
• AddToCart: 146
• Checkout: 0
• Purchase: Not tracked

We have no idea how many of these 146 cart adders actually purchased. The checkout happens on Shopify’s domain where our tracking can’t fire.

Cross-Reference Attempt

We tried to match cart adder emails with Shopify orders, but:

• 0 of 122 cart adders had email captured in 2024
• Email capture only happens now via the prelaunch modal
• This is a massive gap in our data

What We Know from Shopify

From Shopify orders in 2024:

• ~25 real orders (excluding internal)
• Average order value: ~$340
• Total revenue: ~$8,500

If we assume 20% cart-to-purchase conversion:

• 146 cart adds × 20% = ~29 purchases expected
• Shopify shows ~25 real orders
• This suggests our conversion rate is roughly 15-20%

This means 80-85% of cart adders abandoned.

Part 6: Implications for Personalization

Based on this data, here’s how we should segment and personalize:

Segment 1: First-Visit Cart Adders (63%)

These people know what they want. Don’t get in their way.

Segment 2: Multi-Session Browsers (23%)

These people are considering. Help them decide.

Segment 3: High-Intent Stuck Visitors (14%)

These people WANT to buy but something is blocking them.

Part 7: Recommended Actions

Immediate (Before Relaunch)

1. Implement Purchase Tracking

- Set up Shopify webhook for orders/create - Send purchase events to Arlem Analytics - Close the funnel loop

1. Capture Email Earlier

- Don’t wait until cart modal - Consider exit-intent email capture for browsers - Offer value exchange (styling tips, discount code)

1. Build Visitor State System

- Track session count in localStorage - Detect returning visitors - Enable personalization

Short-Term (Month 1-2)

1. Implement Segment-Based Messaging

- First visit: Standard experience - Return visit: “Welcome back” acknowledgment - 4+ visits: Discount offer

1. Increase Retargeting

- Only 1 cart add from retargeting in all of 2024 - Should be 30-50% of ad budget - Hammer cart abandoners

Medium-Term (Month 3-6)

1. A/B Test Discount Timing

- Test: Discount on visit 3 vs visit 5 vs visit 7 - Measure: Cart conversion rate, revenue per visitor

1. Personalized Product Recommendations

- Track products viewed - Show “You were looking at…” on return - Cross-sell based on cart contents

Part 8: Success Metrics to Track

Once we implement personalization and purchase tracking:

Appendix: Data Quality Notes

What We Have

• ✅ Full session and event data for 2024
• ✅ Geographic location
• ✅ Traffic source attribution
• ✅ Product interaction details
• ✅ Cart addition events

What We’re Missing

• ❌ Purchase events (not implemented)
• ❌ Email addresses for most visitors
• ❌ Revenue attribution
• ❌ 2023 data (tracking not implemented)
• ❌ Checkout abandonment details

Data Exclusions

• Excluded Adelaide, SA and Morphett Vale (internal traffic)
• Excluded Mountain View, CA (likely Google bots)
• Excluded visitors with no location data

Conclusion

The 2024 data reveals a clear opportunity: 37% of cart adders need help deciding, and we’re currently giving them nothing.

The most striking example is the Sydney visitor who came back 23 times over 4 months. With proper personalization, we could have:

1. Recognized their high intent after visit 4-5
2. Offered help or a discount
3. Captured their email for cart recovery
4. Potentially converted a $450+ sale

Multiply this by 17 high-intent visitors (4+ sessions) and dozens of 2-3 session visitors, and the revenue opportunity is significant.

The personalization system proposed in VISITOR-JOURNEY-PERSONALIZATION.md directly addresses these gaps.

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