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Pricing that’s healthy for your bottom line
Pharmacy products carry strong seasonal demand: cold and flu in winter, allergies in spring, sunscreen and travel medicine in summer, and many SKUs are sensitive to expiry dates. 7Learnings combines short- and long-term forecasts for demand, profit, and revenue, taking into account shelf life, expected returns, and the salvage value of stock. Set optimal prices throughout the lifecycle and create the best possible markdown sequence on products approaching their expiration date.
Pharmacy regulations, taxation, and reimbursement schemes vary by country and channel. With 7Learnings you can go granular: set the optimal price per region, country, store, or sales channel, with hard rules that respect the local regulatory ceiling or fixed price where one applies. We use machine learning to understand price elasticity per channel and account for different cost structures, including marketplace fees.
Search behavior for health categories spikes around seasonal events and flu waves. Our PPC campaign optimization tool forecasts the impact of pricing and marketing decisions on traffic, conversion, and cost per click. Align your marketing and pricing strategies, optimize coupon and bundle distribution, and make sure every euro of paid budget goes to products with both demand and margin to justify it.
Pharmacy catalogs are long-tail by nature: tens of thousands of supplements, dermocosmetics, and medical accessories with low individual transaction counts. 7Learnings' algorithms learn elasticity across categories and attributes: active ingredient family, brand, dosage form, so we predict the impact of price changes even on low-selling SKUs, new launches, and variants. The result: better margins on best-sellers and fewer overstocks on the long tail.
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You have questions? We have answers!
Yes. You can configure price floors, ceilings, and fixed prices per SKU, brand, or category. The system will never recommend a price that breaches a regulated price point, a reference price, or your internal pricing policy. The optimization focuses on the portion of your assortment where you can actively steer pricing.
Yes. Our algorithm does not depend on competitor data, which makes it especially well-suited to private-label supplements, dermocosmetics, and medical devices that have no direct online benchmark.
Our forecasts include shelf life and the salvage value of remaining stock. As products approach expiry, the system recommends the markdown sequence that maximizes recovered value without giving away margin too early.
Yes. 7Learnings adapts to and learns from the products in your portfolio, so we deliver results even with limited data. We typically need around 100 overall price changes and 2-3 adjustments for high-selling items. Our models already understand the elasticity drivers in health categories and can identify them even without explicit price alterations.
Discover the power of AI in retail optimization with us. Book a demo now and together, let’s dive into how our solution can transform your business.
