Return Signals
Return Signals
Financial Impact

ROI Calculator

See how Return Signals can impact your bottom line by reducing returns, increasing exchanges, and improving customer retention.

Learn how we calculate ROI

Business Metrics

Percentage of customers who share their phone number

Cost to acquire a repeat order from existing customers

Return Rates

Orders with multiple sizes where all but one are returned

Return Signals Impact

Percentage of customers with problems who engage when contacted

Percentage of happy customers who engage when contacted

Lift in repeat purchases for customers who engaged with Return Signals

Annual Profit Increase

$0

0% margin expansion
Baseline Annual Net Sales $0

Value Breakdown

Exchange Value

Returns converted to exchanges

$0

Keep Item Value

Returns avoided entirely

$0

Retention Value

Repeat purchases from engaged customers

$0

Other Metrics

Eligible Returns 0
Engaged Customers 0
Returns Prevented 0

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How We Calculate ROI

Our calculator uses a transparent methodology based on your business metrics to estimate the financial impact of reducing returns and improving customer retention.

Input Variables

These are the values you enter in the calculator. Understanding them helps interpret the results.

Business Metrics

N Monthly e-commerce units sold (before returns)
AUR Revenue per unit after discounts, before refunds
AUC Cost per unit including shipping, duties, and taxes
U Average number of units in an order
S Fraction of customers who share phone number
Mr Marketing cost to acquire a repeat order

Costs

Co Outbound shipping cost per order
Cs Per-unit shipping (Co / U)
Cr Cost to process and ship a return

Return Rates

rf Fraction of units refunded for fit issues
ro Fraction of units refunded for non-fit reasons
m Fraction of fit orders with multiple sizes ordered
p Probability a returned unit can be resold

Return Signals Impact

Ep Fraction of problem customers who engage when contacted
Eh Fraction of happy customers who engage when contacted
Lexch Uplift in customers who exchange
Lkeep Uplift in customers who keep the item
Lret Increase in 180-day repeat purchase rate

Step 1: Calculate Eligible Returns

First, we identify which returns Return Signals can potentially convert to exchanges or keeps.

\[N_{\text{elig}} = N_f + N_o\]

Where:

\(N_f = N \times r_f \times (1 - m)\) Fit returns excluding intentional multi-size orders
\(N_o = N \times r_o\) Non-fit related returns (quality, changed mind, etc.)

Why? We exclude multi-size fit returns because those customers intentionally ordered multiple sizes to find their fit. They found it and returned the rest as expected - this is normal behavior we don't need to intervene on.

Step 2: Exchange Value

Value created when customers exchange instead of requesting a refund.

\[V_{\text{exch}} = N_{\text{elig}} \times S \times E_p \times L_{\text{exch}} \times \Delta V_{\text{exch}}\]

Where:

\(\Delta V_{\text{exch}} = AUR - C_s - AUC\) Incremental value per exchange vs. refund

Why this formula works:

  • + Nelig × S × Ep = customers with return intent who engage with us
  • + Multiplied by Lexch = engaged customers who convert to exchange
  • + Each exchange keeps original revenue (AUR), pays for replacement shipping (Cs), uses one more unit of inventory (AUC)

Step 3: Keep Item Value

Value created when customers decide to keep the item instead of returning it.

\[V_{\text{keep}} = N_{\text{elig}} \times S \times E_p \times L_{\text{keep}} \times \Delta V_{\text{keep}}\]

Where:

\(\Delta V_{\text{keep}} = AUR + C_r - AUC \times p\) Incremental value per kept item vs. refund

Why this formula works:

  • + Nelig × S × Ep = customers with return intent who engage with us
  • + Multiplied by Lkeep = engaged customers who decide to keep the item
  • + Each kept item preserves revenue (AUR), avoids return processing cost (+Cr saved), but loses inventory we would have recovered (AUC × p)

Step 4: Retention Value

Value from increased repeat purchases by customers who engaged with Return Signals.

\[V_{\text{ret}} = N_{\text{eng}} \times L_{\text{ret}} \times V_{\text{repeat}}\]

Where:

\(N_{\text{eng}} = (N_{\text{elig}} \times S \times E_p) + (N - N_{\text{elig}}) \times S \times E_h\) Total engaged customers (problem + happy)
\(V_{\text{repeat}} = U \times (AUR - AUC - C_s) - M_r\) Contribution margin per repeat order

Why this formula works:

  • + Problem customers engaged = Nelig × S × Ep
  • + Happy customers engaged = (N - Nelig) × S × Eh
  • + Each repeat order contributes margin minus marketing cost

Total Monthly Value

\[V_{\text{total}} = V_{\text{exch}} + V_{\text{keep}} + V_{\text{ret}}\]

Sum of all three value components

Frequently Asked Questions

Common questions about e-commerce returns, their costs, and how to measure ROI on returns management.

What is a good return rate for e-commerce?

Return rates vary significantly by category. The NRF reports U.S. retail returns total about 16.9% of sales overall. However, apparel tends much higher: many brands see 30%+ return rates. Fit-related returns alone can account for up to 60% of all apparel returns.

What are typical costs to process a return?

Radial estimates merchants pay an average of $27 to process a return on a $100 order. This includes return shipping, receiving, inspection, and restocking. Only about 30% of returned merchandise gets resold at full price. The rest goes to liquidation, donation, or disposal.

How do returns affect customer retention?

The return experience has a significant impact on whether customers come back. NRF reports 67% of consumers say a negative return experience would discourage them from shopping with that retailer again. On the flip side, Narvar found 70% say an easy return or exchange experience makes them more likely to become repeat customers.

How does Return Signals calculate ROI?

We calculate value from three sources: (1) Exchange Value, which captures returns converted to exchanges that preserve revenue and reduce refunds, (2) Keep Item Value, where customers decide to keep items after receiving guidance or support, avoiding the return entirely, and (3) Retention Value, the increased repeat purchases from customers who engaged with Return Signals. See the methodology section above for the complete formulas.

What causes most apparel returns?

Most apparel returns are not quality failures but information gaps. PowerReviews found 39% of apparel returns are due to fit issues (the garment does not work on the customer's body), and 28% are because the item did not look as expected (color, style, or fabric differs from what was shown online). These are exactly the kinds of issues that proactive post-purchase engagement can address before they become returns.

Where can I learn more about reducing returns?

Read our in-depth article The End of Reactive Support to learn how proactive post-purchase engagement can prevent returns, convert refunds to exchanges, and build customer loyalty. We cover the economics of returns, why most apparel returns happen, and how AI is changing customer support from reactive to proactive.