Convert data into insights and actionable decisions.
SOLUTION THAT enables you to convert retail data into insights and decisions.
SUMATUS- Two in one Advanced Analytics solutions for retailers in combination with state-of-the-art category management consulting.
We developed AI software which ensures accurate demand planning and Optimal Assortment development - both at macro and SKU levels. It is achieved using Data Science and Machine Learning algorithms enabling concider customer needs, store financial targets, product merchandising rules, capacity constraints, supplier commercial terms, syndicated market data.
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Macro – level product mix
Prepare each category assortment strategy.
SUMATUS will recommend quantity of assortment matrices and their width at product attribute level considering available assortment, shopper needs, store size, space effeciency, market trends.
Allocate each store space to different merchandise categories.
SUMATUS will recommend allocate appropriate store space to each department, section, category as well as assign appropriate category assortment matrix considering shopper demand and store space.
Allocate appropriate category assortment matrix in the new store.
SUMATUS will recommend grouping existing stores into category specific clusters according to the store size and location attributes. Those insights could be used in selecting best matching category assortment matrices when opening new store.
Be sure about the optimal assortment mix at any point of time.
SUMATUS will provide automatic alerts about the need of corrective actions considering significant and permanent deviations from existing assortment.
SKU – level product mix
Choose correct SKUs in each category assortment matrix.
SUMATUS will provide listing/delisting recommendations in order to maximize shopper satisfaction and sales considering customer demand, assortment strategy, active and regional items, product substitution rules.
Allocate appropriate space for each SKU.
SUMATUS will provide space allocation recommendations in order to maximize financial store targets considering SKU profitability, commercial terms with suppliers, product merchandising rules, space constrains, product merchandising space elasticity rules.
Prepare in-store visual merchandising guidelines.
SUMATUS will recommend how many facings to give to SKUs in order to maximize productivity of shelf space, ensure visibility, appropriate shelf stock and avoid Out of Stock, considering assortment strategy, shelf space constrains, product merchandising rules.
Take informative decisions in all the times.
SUMATUS will help simulate “what if…” scenarios and evaluate decision effect to sales, gross profit, inventory turnover before its implementation – listing/delisting, shelf space changes, understand purchase power during negotiations with suppliers, etc.
FEATURES OF OUR SOLUTION
Demand (sales) estimate:
- base demand and regular demand forecast;
- identified and considered Out of Stock and Phantom inventory effects;
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Rules estimate:
- item substitution;
- item elasticity to merchandising space;
- item promo cannibalization;
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Optimal assortment matrices in each category:
- assortment strategy: quantity of assortment matrices, their width and structure at product attribute level;
- optimal assortments at SKU level;
- visual in-store merchandising guidelines with facings and shelf stock;
- local assortments;
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Assortment optimization options:
- sales;
- gross profit (incl. retro discounts, logistics and WC cost);
- inventory turnover.
Product merchandising rules:
- all stock just in sales area;
- merchandising in units or secondary packaging;
- placement on each other;
- replenishment frequency;
- supplier/brand/SKU shelf share;
- product lifetime;
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Store space and fixtures:
- capacity constrains;
- efficiency;
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Terms of cooperation with suppliers:
- retro discounts;
- shelf share;
- order and delivery terms;
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Store clustering by location attributes at category level.
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What if scenarios…
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Automatic early warnings & insights .
WHY US
Satisfied customers:
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Segmented stores to category specific clusters
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Localized assortment
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Satisfied maximum shopper needs
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Higher On-Shelf Availability
Better financial results:
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Higher sales up to 4%
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Higher gross profit margin up to 2.5 pp
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Less capital invested in stock for about 10 – 20%
EFFECTIVE assortment MANAGEMENT:
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Faster data - driven decision making. Autmated category manager's work
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Efficient utilization of store space
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Shoppers' needs satisfied with less quantity of SKUs
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Evaluation of decision effectiveness before its implementation
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Understanding purchasing power during negotiations with suppliers