In order to meet customer demand and expectations, it is essential for retailers and CPG companies to carry the right product assortment. Effective localization of assortment and space planning can be a very challenging task, and most retailers often fall short of harnessing the true potential of technologies such as artificial intelligence (AI) and modern approaches in machine learning (ML) and data science (DS).

In this report, Coresight Research analyze key industry trends and discuss how retailers and CPG companies can achieve the goal of customer-centricity through the hyper-localization of assortment and how you can run rapid retail simulation to strategies like:

• Run rapid vendor/JBP collaboration strategies – to simulate the impact of adding/deleting item(s), brand(s), package type(s)
• Compare category strategies – on sales, profit, or volume at any cluster dimension or count; all at a store-level, eliminating the averages of averages approach.
• Determine the impact of clustering vs. store specific – with the ability to find the right balance (where the juice is worth the squeeze)
• Product innovation distribution – see which store(s) new product innovations should be distributed to while considering demand transfer impacts or comparing pack-out and/or holding power strategies (e.g. 1.5 DOS → 2.0 DOS, case pack +1 → 2 case packs)

The report explores how be combining ML and DS into “single learning engine”, you can harnesses store-level data for category management optimization, enabling precision in category management and localized assortment.

By increasing accuracy and removing the manual process, these new methods and approaches offer a competitive advantage to both CPG companies and retailers; transforming Joint Business Planning (JBP) sessions between the retailer and CPG arming them both with win-win category strategies rapidly.

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