Comprehensive analysis of potential ROI from implementing automation flows and bad actor filtering for high-volume SMS campaigns.
Monthly Savings
€103,000
Revenue Increase
€30,000
Annual Impact
€1.6M
Based on the client dialog and preliminary research, we have identified key operational metrics and potential optimization opportunities.
No automation flows implemented
Missing opportunity for targeted engagement
No blacklisting strategy
Continuous spend on bad actors
No systematic exclusion
Bad numbers remain in rotation
Multiple SMS providers
No centralized optimization
To refine the ROI calculation and create a comprehensive optimization strategy, the following data points are critical.
• Total SMS volume per month
• Database size (total contacts)
• List growth rate
• Bounce & unsubscribe rates
• Average message length
• Overall click-through rate
• Conversion rate from click
• Average revenue per click
• Time-to-conversion patterns
• Repeat customer rate
• % of spy numbers identified
• % of non-engaging numbers
• Click but never convert %
• Cost per spy detection
• Duplicate send frequency
• Campaigns per month
• Acquisition vs retention split
• Geographic distribution
• Time-of-day patterns
• Cost per SMS by provider
• Cost by geography
• Volume distribution
• Performance differences
• Average lifetime value
• First-time vs repeat revenue
• Churn rate
• Re-engagement success
Conservative projections applying industry benchmarks and confidence factors to preliminary findings.
SMS Spend
€350,000
midpoint estimate
Revenue
€1,000,000
monthly total
Current ROI
286%
€1M / €350K
Profit
€650,000
current margin
Client's Preliminary Finding
Additional Waste Reduction
| Impact Area | Monthly Savings | Revenue Increase | Total Value |
|---|---|---|---|
| Bad Actor Filtering | €50,000 | - | €50,000 |
| Automation Flows | €53,000 | €30,000 | €83,000 |
| TOTAL | €103,000 | €30,000 | €133,000 |
| Metric | Current State | With Automation | Improvement |
|---|---|---|---|
| Annual SMS Spend | €4,200,000 | €2,964,000 | -€1,236,000 (-29%) |
| Annual Revenue | €12,000,000 | €12,360,000 | +€360,000 (+3%) |
| Annual Profit | €7,800,000 | €9,396,000 | +€1,596,000 (+20%) |
| ROI | 286% | 417% | +131 pp |
| Scenario | Monthly Impact | Annual Impact | Probability |
|---|---|---|---|
| Conservative | €80,000 | €960,000 | 80% |
| Base Case | €133,000 | €1,596,000 | 60% |
| Optimistic | €200,000 | €2,400,000 | 30% |
Phased approach to validate assumptions and maximize ROI with minimal risk.
Export last 90 days of campaign data, analyze click-through and conversion patterns, identify spy numbers and bad actors.
Create exclusion list of identified spy numbers, implement basic frequency capping. Estimated savings: €20-30K/month
Evaluate automation platforms compatible with current providers, assess integration requirements.
Launch one automation flow (recommend re-engagement), A/B test against current bulk approach.
Even with a highly conservative approach, implementing automation flows and bad actor filtering can reasonably be expected to generate:
Monthly Impact
€80K - €133K
Annual Impact
€960K - €1.6M
Payback Period
< 1 month
The client's preliminary finding of €95,000/month in savings from bad actor filtering alone appears realistic and potentially understated when combined with automation flow benefits.
Risk-Adjusted Recommendation
Expect €1,000,000 - €1,500,000 in first-year net benefit with high confidence (70%+), representing a 20% increase in annual profit and ROI improvement from 286% to 350-417%.