Glow Botanics
DemoExample analysis demonstrating strong LTV predictability for a beauty brand
Spearman Correlation
r = 0.520
strong
Top 20% Revenue Share
42.6%
of total LTV
Average 12-Mo LTV
$186.68
Median: $152.6
Qualified Customers
8,234
12,847 total
LTV Predictability Score
Spearman correlation between first order value and 12-month customer lifetime value
A correlation coefficient of 0.520 indicates a strong positive relationship between first order value and customer lifetime value. This means Glow Botanics can reliably predict which customers will become high-value based on their initial purchase.
First Order Value vs. 12-Month LTV
Each point represents a customer. Hover for details.
Tap a point to see details
Value Divergence Over Time
How customer value gaps compound over time between top and bottom segments
30 days
3.8x
90 days
5.8x
180 days
6.7x
365 days
7.6x
30 days
3.8x
90 days
5.8x
180 days
6.7x
365 days
7.6x
The gap between Glow Botanics' highest and lowest value customers grows from 3.8x at 30 days to 7.6x at 12 months. This divergence represents the compounding effect of repeat purchases and underscores the importance of acquiring the right customers from day one.
High-Value Customer Profile
What sets your best customers apart from the rest
Customer Value Gap
Top 20% vs Bottom 20% by first order value
Top 20% Avg LTV
$398.2
Bottom 20% Avg LTV
$52.3
| Metric | Top 20% | Bottom 20% |
|---|---|---|
| Avg First Order | $121.7 | $28.5 |
| Days to 2nd Purchase | 18 | 68 |
| Repeat Rate | 78% | 22% |
| Avg Orders | 4.8 | 1.4 |
Focus acquisition on customers matching Top 20% profile
Revenue Concentration by Quintile
Customers grouped by first order value, showing revenue share per group
The top 20% of customers (by first order value) generate 42.6% of total lifetime value. This high concentration means identifying and acquiring similar high-value customers could significantly impact revenue.
Marketing Efficiency Projection
Calculate the potential impact of pLTV-optimized bidding
Projected Annual Impact
with pLTV-optimized bidding
Monthly Recovery
$8,600
+17% ROAS improvement
Current Wasted Spend
$11,950
24% of budget
Current Avg LTV
$187
Projected Avg LTV
$219
Projections based on 17% ROAS improvement from pLTV-optimized bidding. Actual results depend on market conditions and implementation.
Industry Benchmark Comparison
Compare your metrics against industry averages
Data Readiness Assessment
How well your data supports predictive LTV modeling
Recommended Path Forward
Based on your data analysis
Your data is ideal for pLTV bidding
Strong predictive signals + complete data = maximum ROI potential
Strong
+0.52
Ready
Data
What to Expect
- 60-day controlled experiment on a portion of ad spend
- AdZeta connects directly to your ad platforms
- Progressively enhance model accuracy over time
Next Steps
- 1Discovery call with your ad & data teams
- 2Connect AdZeta to your data sources & ad networks
- 3Launch 60-day pLTV experiment and measure results
Schedule a call to unlock the full report and discuss next steps
Projections are estimates based on your data patterns. Results may vary based on implementation.