Generate hyper-personalised promotions for every outlet-SKU pair using discount sensitivity, thresholds, associativity, and real-market simulations to increase sales, distribution, and promo ROI while reducing wasted discount spend.
Traditional promotion planning applies the same schemes across outlets and SKUs with different buying behaviour, leading to wasted discounts, missed incremental sales, and lower promotion ROI.
SalesCode.ai AI Promo Co-Pilot identifies which outlet-SKU combinations will respond to a promotion, recommends the right offer, discount, duration, and frequency, and predicts the expected sales, distribution, ROI, and budget impact before activation.
The AI Promo Co-Pilot analyses outlet-SKU response, identifies where discounts can change buying behaviour, simulates expected business impact, and recommends the right promotion for every retailer.
Identify which outlets are most likely to respond to a promotion and which ones do not need additional discount.
Select the products with the highest sales, distribution, or cross-sell opportunity for every outlet.
Recommend the best offer type, discount slab, duration, frequency, and eligibility conditions for each outlet-SKU pair.
Simulate expected sales uplift, distribution growth, promotion ROI, and budget consumption before activation.
Create personalised total-order-value, bundle, and cross-sell promotions that encourage retailers to purchase larger and more valuable baskets.
Identify retailers likely to reorder and generate time-based or nth-purchase incentives that bring them back sooner.
Target outlet-SKU gaps with personalised promotions that increase new SKU adoption, premium-product distribution, and assortment depth.
The AI Promo Co-Pilot publishes personalised promotions to eligible retailers, applies offer logic during ordering, and measures redemption and business impact automatically.
Identify where discounts can actually change buying behaviour, helping brands avoid unnecessary promotional spend.
Optimise discount depth, promotion timing, retailer selection, and offer structure to generate more incremental sales from the same budget.
Create a different promotion for every outlet-SKU opportunity instead of applying the same offer across all retailers.
An AI Promo Co-Pilot is a promotion decision-intelligence platform for CPG and FMCG companies. It analyses outlet behaviour, SKU performance, discount sensitivity, promotion history, and business goals to recommend the right promotion for each outlet-SKU pair.
The AI Promo Co-Pilot analyses historical sales, previous promotions, discount levels, seasonality, inventory, competitor activity, and outlet-SKU buying patterns. It then recommends the promotion type, discount slab, retailer audience, duration, frequency, budget, and expected business impact.
Traditional promotion planning often applies similar discounts across outlets and SKUs with very different buying behaviour. The AI Promo Co-Pilot helps reduce wasted discount spend by identifying where a promotion can influence demand and where additional discounts are unlikely to create incremental sales.
Hyper-personalised promotions are offers designed for a specific outlet-SKU combination rather than a broad retailer segment. They reflect the outlet’s purchase history, SKU opportunity, discount response, threshold, and the business KPI the promotion is expected to improve.
The platform can support item-level discounts, total-order-value offers, bundles, free-of-charge products, percentage discounts, amount-based discounts, Nth-purchase promotions, cross-sell offers, and KPI-specific coupons.
3% minimum uplift, contractually guaranteed. Includes a 110% refund if the technical success criteria are not met.
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