AI-nativePromo Co-Pilot for CPG Sales Teams
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.
One Promotion Plan Cannot Work for Every Outlet
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.
The Right Promotion for Every Outlet, SKU and Business Goal
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.
How the SalesCode AI Promo Co-pilot creates a promotion recommendation
The Co-pilot turns raw sales history into a specific promotion recommendation in four steps.
Ingest the data
Combine historical secondary sales, discounts and promotions already applied, and external variables such as seasonality and in-store stock.
Model the response
Measure sensitivity, elasticity, and threshold per outlet-SKU pair, finding the inflection beyond which more discount changes nothing.
Cluster the pairs
Group outlet-SKU pairs into sensitivity and threshold segments, so similar responders are treated together.
Recommend and publish
Generate the hyper-personalized promotion for each cluster and pass it to the Promo Coupon Engine for publishing to retailers.
The discount science behind every recommendation
Discount sensitivity modelling
Measure how responsive each outlet is to a discount change, so spend only lands where it moves order value.
Discount elasticity and associativity
Model outlet-SKU and SKU-SKU correlationto discounts, so one SKU's offer accounts for the basket around it.
Discount threshold detection
Find the point of inflection per outlet-SKU, the max discount beyond which order value stops rising, so spend never overshoots.
External-variable impact scoring
Score how outside factors move discount performance: seasonality, stock, SKU cannibalization,competitor promos, and wholesaler sales.
Outlet-SKU clustering
Place every outlet-SKU pair into one of four clusters by sensitivity and threshold, from high-sensitivity to low-sensitivity.
KPI-mapped coupon mechanics
Match each objective to a coupon: TOV for order value, NXP for frequency, Bundle for lines per order, CSOP for distribution.
From AI recommendation to redemption at the retailer
A recommendation becomes a live, redeemable offer through the connected engine. The Co-pilot recommends, the Promo Coupon Engine publishes coupon-linked banners with weightage and priority, personalized coupons reflect in the retailer ordering app, coupon competition is resolved by defined logic at ordering, and order data with applied coupons is pushed to DMS at total-order and item level for impact tracking.
The commercial gains a compliance layer builds on
Higher order value
Retailers already ordering through the eB2B platform show a lift in average order value over traditional ordering, the same wallet a verified reward is credited into and spent from, so a compliance program compounds into more sales for you.
New SKUs discovered
ML-based basket recommendations already surface SKUs a store has never ordered before; you can point a compliance or reward campaign at exactly those SKUs to accelerate trial.
Repeat compliance scans
The uplift in how often a retailer returns to scan and get verified once a reward has been credited once is being measured in current pilots.
Put the AI Promo Co-pilot on your discount spend
Bring a market where you discount today and we will show you how the AI Promo Co-pilot would score its store-SKU pairs and target the spend. Salescode's platform carries a Sales Uplift Guaranteed promise.
Book a demoQuestions about AI Promo Co-pilot
The Salescode AI Promo Co-pilot is the AI layer of Salescode's route-to-market platform that generates hyper-personalized promotions for CPG and FMCG brands. It scores discount sensitivity, elasticity, and threshold for each store-SKU pair, then recommends the coupon most likely to lift a target trade KPI, which the Promo Coupon Engine publishes to retailers as coupon-linked offers.
It analyzes historical secondary sales, past discounts and promotions, and external variables, models the discount response for each outlet and outlet-SKU pair, clusters the pairs by sensitivity and threshold, and recommends the coupon type mapped to the objective (TOV, NXP, Bundle, or CSOP).
A discount threshold is the point of inflection for an outlet or SKU, the maximum discount value beyond which a larger discount no longer changes order value. The Co-pilot detects this per store-SKU so promotions stop short of overspending.
The Co-pilot targets four trade KPIs: average order value, order frequency, lines per order, and SKU distribution, plus premium and focus-product distribution, each through a mapped coupon type.
The recommendation is published by the Promo Coupon Engine as coupon-linked banners, reflects in the retailer ordering app, and applies at ordering with coupon competition resolved by defined logic. Order data with applied coupons is pushed to DMS at total-order and item level for coupon traceability and impact tracking.
Al-Native Sales Execution, With A Guaranteed Uplift.
3% minimum uplift, contractually guaranteed. Includes a 110% refund if the technical success criteria are not met.
The AI-native route-to-market platform for CPG & FMCG enterprises — delivering a contractually guaranteed sales uplift, backed by IEEE peer-reviewed ML.
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