PlaceRight
Boost Customer Satisfaction and Sales with Data-Driven Recommendations
In an industry where product assortments can be overwhelming, PlaceRight uses AI to recommend the right SKUs at the store level. With PlaceRight’s AI engine continuously analyzing your primary and secondary sales data, we help you optimize product placement and empower your sales teams to focus on building relationships rather than crunching numbers.
Challenges
Common Challenges Facing In-store Sales
Bad forecasting
Running out of popular items is a constant headache. Too much stock waste space and money.
Vast SKU mix
Managing 100s or 1000s of SKUs can be daunting for both retainer and distributor
Cross & Upsell
Identifying the right items to suggest is tough without a data-driven approach
Our AI Solution
PlaceRight connects directly to your primary and secondary sales data to deliver real-time, store-level recommendations. Think of it as your “digital concierge” for retail execution:
- Store-Level SKU Recommendations
Our AI identifies which SKUs will perform best at each store, helping sales officers and merchandisers maximize revenue. This way, they can focus on building relationships with store managers rather than sifting through endless data. - Distributor-Focused Insights
By applying the same machine learning logic to historic regional data, PlaceRight uncovers patterns and suggests product mixes that drive higher sales for distributors.
Our engine ensures every touchpoint has the most relevant product options, whether online, in-store, or within your distributor network.
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Case Study
Kitchen Treasures
Scenario
Kerala-based Kitchen Treasures faced a common challenge in retail: underrepresented SKUs that rarely sold, limiting overall sales potential. With a product portfolio that includes spices, pastes, and ready-to-cook mixes, the brand needed a data-driven solution to ensure each store carried the right product mix.
What We Did
We integrated our AI-driven recommendation engine into Kitchen Treasure’s sales and distribution network. By analyzing real-time store-level data—such as purchase history, local demand, and seasonal trends—the platform identified which SKUs were ripe for introduction or expansion in each market. This ensured that underrepresented products reached the right shelves at the right time, based on consumer needs.
ROI
According to an article featured in The News Minute, Kitchen Treasure achieved:
- A 4x increase in sales of underrepresented SKUs—those that had never sold in certain stores.
- A 37% improvement in overall sales across product categories.
This remarkable uplift boosted revenues and gave Kitchen Treasure valuable insights into shifting consumer tastes, enabling them to continue refining their product lineup for each local market.