Sales Optimization by Controlled Negative Profit

Maximizing Expected Value through Strategic Pricing Decisions

This interactive dashboard demonstrates a fully operational framework for optimizing sales decisions using expected value rather than per-transaction margin. The system explicitly allows for controlled negative unit economics where strategically rational, enabling more sophisticated pricing strategies.

Core Objective Function

Formula

$$E = \sum_{i=1}^{N} (P_{\text{lender},i} - P_{\text{advertiser},i}) \cdot p_s(P_{\text{lender},i})$$

Where $E$ represents the total expected profit across all leads, $P_{\text{lender},i}$ is the price to the lender, $P_{\text{advertiser},i}$ is the cost from the advertiser, and $p_s$ is the probability of sale.

Sale Probability Model

Formula

$$p_s(P_{\text{lender}}) = e^{-k(P_{\text{lender}} - P_{\text{min}})}$$

Exponential decay model where $k$ represents price elasticity and $P_{\text{min}}$ is the minimum viable price. This captures the intuitive relationship between higher prices and lower conversion probabilities.

    💡 Key Insight: The price that maximizes per-transaction margin is rarely the price that maximizes expected value. 
    Sometimes accepting a small loss on individual transactions leads to greater overall profitability.
  

Self-Optimizing Pricing Engine

This real-time widget computes the optimal lender price that maximizes expected profit per lead. Adjust the parameters below to see how different scenarios affect the optimal pricing strategy.

Advertiser Payout ($P_{\text{advertiser}}$)

Minimum Price ($P_{\text{min}}$)

Price Elasticity ($k$)

Adjust parameters above to see the optimal pricing decision...