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How retail AI helps finance make smarter budget decisions

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The Finance department rarely gets the credit it deserves for guiding the success of retail organizations. Keeping tabs on line items throughout the company can be a thankless job. But money talks, and Finance adds the smarts to retail resource allocation.

Retail organizations with a large brick-and-mortar footprint are under increasing pressure to maximize their in-store sales. Retailers know that stores are essential to omnichannel success, but declining traffic in many locations means that the financial model must adapt to match the new retail landscape.

Budget planning informed by data can change store profit numbers from red to black. Finance plays a key part in retail success by shaping smart sales goals and efficiently allocating resources against those goals. Determining those goals and the resources they require is not an easy task, even with many spreadsheets, cash flow statements, and databases.

The best decisions are informed by smart analysis of available information. Data abounds in retail. The challenge, as any Finance Manager knows, is pulling actionable insights from disparate collections of numbers.

When allocating budgets, Finance can pull two key levers. One is the sales plan; the single biggest number store managers watch. The sales plan drives both inventory allocations and store team motivation. The second lever is the labor budget; how many labor hours a store gets to achieve its sales plan.

The complexity and uniqueness of each store makes a one-size-fits-all approach to pulling those levers ineffective. Finance cannot uniformly dictate a 3% growth target to all stores and simply budget a 3% increase in their sales plan and labor. For some stores, 3% sales growth is out of reach, while for others, it is much too low.

A more effective approach is one tailored to each store and informed by analysis. How well does this store utilize labor to generate sales? Could it generate more sales if it had more labor? And at what point will the added labor not pay-back? Which stores could generate the same amount of sales, but with less labor? And how much less? Getting the right answers to these questions is incredibly impactful, but also requires a huge amount of data and analysis.

This is where AI can be most useful. Leveraging the computational power of the cloud, and advanced machine-learning algorithms, AI can perform a more detailed analysis than any human (and provide more insight than a static dashboard), turning a mass of data and information from individual stores into smart sales and allocation decisions.

When it comes to smart financial goals, it’s not about working harder. It’s about using retail AI to be smarter.

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