Revenue teams live or die by decisions on prices. Data science shifts pricing from gut feel to data backed experiments, elasticity models, and dynamic optimization unlocking 5–20% uplift without more sales headcount.
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Pricing analytics fundamentals include the below key levers
- Price elasticity: % change in demand per % price change.
- Elasticity = (%Δ Quantity) / (%Δ Price)
- >1 = elastic (sensitive), <1 = inelastic
- Willingness to pay (WTP): Max price segment will accept (conjoint analysis, surveys).
- Discount response: How offers decay over time/repetition.
Experimentation patterns
- Price A/B tests: Randomize pricing tiers across similar cohorts.
2. Discount ladders: Test $0 → $10 → $25 off impact on conversion/LTV.
3. Bundling: Feature + price combinations (core + premium vs standalone).
4. Surges: Dynamic pricing by demand (Uber, AWS Spot).
Gotcha: Account for cannibalization (did discounts just steal from full‑price?).
Revenue modelling
Customer Lifetime Value (LTV):
- LTV = Σ [Revenue × Retention × Marginata] / CAC
- Slice by cohort, segment, acquisition channel. DS forecasts LTV curves to compare pricing strategies.
- Optimal pricing: Maximize LTV – CAC across segments.
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Practical workflows
Reops + DS partnership:
- Weekly pricing experiments from sales/marketing hypotheses.
- Automated LTV dashboards by tier/channel.
- ML uplift models: “incremental revenue from discount X on customer Y.”
Example insight: “Enterprise segment: 12% price increase → 2% churn lift, +$2M ARR.”
Try this: Calculate elasticity from past price tests. Elastic segments? Test discounts. Inelastic? Test increases.
