We monitor new products introductions, follow the increase in distribution levels, analyzing and comparing the rotations and market share of the products at store level, identifying the points of sale with the greatest development potential.
At POS Potential we take pride in being straightforward and results oriented. Hereafter are some examples of innovation cases that you have surely met before. Thanks to the seamless integration and valorization of sell-out data we can help you solve them and always have at hand the right information to sell more and better.
New products introduction case #1: evaluating the results of an innovation/launch
To better assess innovation, its effects should be measured not only at SKU level but also at brand and category levels.
Objective: to ensure the presence of innovation in key outlets. Monitor launch sales at the most detailed level and minimize out-of-stocks.
Key Metrics (KPIs): weighted and numerical distribution level, point-of-sale and store clusters presence, daily rotations levels per point of sale, daily/weekly out-of-stocks levels.
Action: assortment decisions per store cluster, adaptation of replenishment parameters (orders quantities) and if necessary, of minimum order.
The availability of sell-out data helps monitor sales and make decisions about the contribution of Innovation.
Monitor category sales.
Identify level of presence (numerical and weighted) of innovation brands and SKUs.
Compare sales of new SKUs by cluster and store with respect to comparable references.
Identify out-of-stock levels for new SKUs.
Best practice identification benchmarking results between stores.
New products introduction case #2: improving the results of an innovation/launch?
To improve innovation, its effects should be measured not only at SKU level but also at brand and category levels.
Objective: boost the category, detect new opportunities, optimize results after a period of presence on shelves and improve the weaker points identified.
Key Metrics (KPIs): unit and value sales to track category, brand and SKU growth, mix and ROI calculations, evaluation of out-of-stocks per store and SKU based on sell-out.
Action: decisions to enhance and improve innovation, improvements/assortment changes by store cluster, improvement in profitability/mix, adjustments in terms of replenishment quantities and minimum order.
Results Image enhancements and chain results.
Detailed sell-out and P&L data enable fact-based decisions on innovation improvement.
Monitor category sales, identify the contribution in volume, value and profit of the innovation.
Compare distribution levels (numerical and weighted) of brands and references with respect to similar existing ones.
Compare sales by cluster and by store of brands and references with respect to similar existing ones.
Changes and improvements in the replenishment/minimum order quantity of those references.
Calculate the return on investment of the category, identifying the contribution of innovation.
Recommendations for mix changes to improve results.
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