Promo Analytics

promo analytics description provides retrospective and forward looking promotion analysis to help pricing managers assess which products and mechanics generate incremental commercial value it connects historical promotion outcomes, product level sales patterns, cannibalization, and scenario based forecasts so promotion decisions can be evaluated beyond headline revenue promo analytics overview sub parts & functions clicking a product in this list opens the same product detail documented under price management docid\ rs0z8lfkdpage86wko0qc — the tabs and fields there apply unchanged individual promotions compares past product promotions within the selected category and date range as a packed bubble view bubble size represents article revenue during the promotion, while color represents article revenue uplift; switching to including cannibalizations evaluates the promotion after sales shifted from other products in the category are considered key elements product promotion uplifts — shows promotional uplift without the cannibalization adjustment including cannibalizations — evaluates uplift after sales shifted from other category products are included date range — limits the past promotion observations included in the analysis product impact profiles segments products by their observed promotion characteristics and plots average margin against average revenue the accompanying table exposes each product's impact profile, avg revenue, avg margin, and avg uplift ratio so managers can filter and compare groups such as ideal, margin driver, revenue driver, neutral, and weak product impact profiles key elements impact profile filters — include or exclude profile groups from the chart and table avg uplift ratio — compares average promotional sales performance relative to the regular baseline avg margin — supports comparison of promotion value alongside average revenue product sales history shows a selected product's past sales as weekly or daily volume, revenue, or profit, split between regular sales and standard promo sales average selling price and cost or margin can be shown as secondary measures, while the past promotions timeline ties observed performance to specific promotion periods; yoy total compares matching periods but does not provide price or margin product sales history key elements weekly / daily — sets the aggregation interval for historical transactions volume / revenue / profit — selects the primary commercial measure in the chart price / margin — selects the secondary measure when it is available yoy total — compares equivalent periods across years all zones — scopes the product history to one or more zones past promotions — identifies historical promotion windows and their displayed uplift percentages promo simulation forecasts the expected effect of a future promotion for a selected promo type and zone under pessimistic, mean, or optimistic assumptions the expected sales comparison contrasts during promo with without promo for promo price, buying price, margin, volume, revenue, and profit, while the cannibalized products section identifies sales expected to shift away from related products promo simulation key elements promo type — selects the promotion context used for the forecast zones — scopes the forecast to the selected store group pessimistic / mean / optimistic — changes the forecast assumption set promo price — adjusts the simulated promotional selling price cost (avg ) — sets the buying price assumption used in margin and profit estimates cannibalized products — lists products expected to lose sales because of the simulated promotion connections & integrations promo analytics reports promo price outcomes created in promo planner docid\ swfaxzwu2fj8vbgay6dou and uses product and zone level sales data to evaluate regular sales, promo sales, uplift, and cannibalization its promo simulation estimates future promotion impact for comparison with the historical performance shown in sales history and individual promotions; multibuy planning instead relies on quantity in basket for threshold selection key actions select a category and date range to review historical promotion uplift switch between gross product promotion uplift and uplift including cannibalizations open a product from the promotion list to inspect its sales history or run a promo simulation compare weekly, daily, one year, two year, or year over year product results by volume, revenue, or profit filter product impact profiles to identify products with preferred historical promotion characteristics test pessimistic, mean, and optimistic promotion scenarios before planning a future promotion best practices & success stories historical uplift analysis helps identify products and promotion patterns that generate meaningful incremental revenue cannibalization adjusted evaluation supports more profitable category level promotion choices product impact profiles help managers prioritize products with stronger revenue, margin, and uplift characteristics scenario simulation enables promotion proposals to be tested before they are implemented important considerations review cannibalization before treating a large product level uplift as net category growth use the past promotions timeline with the sales chart to distinguish promotional effects from normal sales variation compare pessimistic, mean, and optimistic simulation results rather than planning solely from the mean forecast price and margin are unavailable in the year over year comparison using product promotion uplift without cannibalization can overstate the net commercial value of a promotion selecting a promotional price based only on expected volume growth can reduce profit when margin deterioration is not evaluated treating historical promo performance as comparable without aligning zones, time periods, and promotion context can lead to incorrect decisions related modules price management docid\ rs0z8lfkdpage86wko0qcpromo planner docid\ swfaxzwu2fj8vbgay6dou