How to improve retail pricing with the power of AI

Does this price strike the right balance between boosting sales and protecting margins? That's the question category managers in retail are constantly asking. And it's understandable. They often struggle to find the sweet spot between competitive pricing and timely execution.

But it's not their fault. When data is fragmented, systems are siloed, and workflows are manual, making an informed decision can feel near impossible.

We believe AI has the power to empower. Applied in the right way, AI can help retailers get the price right. Here's how.

Before you get into the weeds of product pricing, retailers need to look at their overall pricing strategy. For AI to find the optimal price, retailers must first unify their data and connect their systems – that's where true transformation begins.

AI brings order to chaos. It can combine data from multiple sources into a single, unified system. By automating the process of extracting and standardizing information, AI eliminates inconsistencies and creates connections between systems that may not naturally communicate. The result? A data foundation to fuel smarter pricing decisions.

Everyone has their favorite branded products. And we're all guilty of a quick Google search to find the cheapest price. This makes brand-led pricing a fierce battleground. Being even a few hours late on a price response can result in lost foot traffic or margin leakage for retailers.

Once a solid data foundation is in place, retailers can act faster and smarter. AI can be applied to reduce manual steps and give teams greater visibility to help increase confidence and strengthen market position.

Private label products – commonly referred to as store brands – are owned and sold exclusively by a retailer under its own branding. These products typically provide significant margin headroom due to lower production and marketing costs compared to national brands. Retailers also have greater control over pricing, packaging, and distribution, which protects profitability.

However, it's hard to match private label products or make comparisons against their national or name-brand counterparts. This makes it difficult to benchmark against competitors, affecting price competitiveness and promotional strategies. And because private labels often have unique formulations, packaging, or positioning, they don't map easily to syndicated retail data or third-party catalogs, limiting price intelligence.

Using AI to support unit normalization for product matching makes it possible to draw accurate and meaningful comparisons between private label and national brand products. By standardizing product attributes – like unit size, weight, volume, or quantity – across different SKUs, retailers can make pricing decisions on a like-for-like basis rather than relying on superficial comparisons.

Better still, category managers can compare true cost-per-unit metrics, evaluate value propositions more effectively, and set competitive prices that align with shopper perception and margin expectations. They can confidently position private label products to deliver greater value than branded alternatives, optimize promotional strategies, and protect profit while staying price-relevant in a competitive market.

AI-powered pricing isn't just nice to have for retailers – it's the new normal. So, there's no time to waste. AI is a valuable tool for helping retailers maximize value for their customers for branded and private label products.

This approach will help retailers improve margins, boost sales, and unlock a sustainable competitive edge.