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How Finance can positively optimize store labor spend and drive sales

Historically, Finance has been seen as an optimizer of store payroll but hasn’t been regarded as a driver of sales in retail organizations. Sitting in HQ, Finance is stereotyped as the group that either cuts store payroll or sets sales goals and labor budgets that may not be realistic. But with the right tools, Finance can transform itself into a positive optimizer of store payroll and a major driver of store sales.

Finance controls two levers that directly influence sales and optimize labor spend: sales goals and labor budgets. Sales goals are the numbers that everyone watches, especially store managers. This number is the guiding North Star. It determines both inventory allocations and the motivation of store teams. Labor budgets back these goals with manpower, telling store managers what they have to work with to hit their goals and serve as the foundation of payroll calculations.

Getting these numbers right is important. A sales goal that’s too high will feel unattainable to associates, zapping the motivation to even try to hit it. A goal that’s too low doesn’t ask enough of associates, almost insulting their selling skills. Why should they try to do better? Will they be recognized for the achievement? Motivation fizzles, labor spend is wasted, and store performance suffers.

Once sales goals are set, Finance has to perfectly allocate the number of labor hours needed to reach those goals. If sales goals and labor budgets are out of sync, customer experience and the bottom line will be compromised. For example, if a store isn’t allocated enough labor hours to hit an aggressive sales goal, team members are overwhelmed and frustrated while customers will take their dollars to a competitor who does have sales goals and labor aligned. If stores are allocated too much labor, the entire chain suffers financially due to resources being put where they are not needed.

Finance’s tool to positively optimize store labor spend and drive sales

Finance’s superpower lies beyond basic goal-and-budget alignment. The department can make a bigger impact both on store payroll optimization and sales effectiveness by setting attainable targets that are customized to each store and providing the necessary labor hours to hit those goals. The complexity and uniqueness of each store in a large retail organization makes a blanket approach to sales goal planning and labor allocation ineffective (and potentially disastrous). 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.

Customizing sales goals and labor hours to individual stores eliminates the pitfalls of asking too much or too little of store teams’ selling ability and ensures that each store is putting its payroll to best use.  Yet few Finance departments take this approach because the challenges of analysis seem insurmountable. First, there’s often too much data in too many places without enough time or manpower to analyze it. Second, since Finance is removed from operations, they often have to guess why things didn’t go as planned. Why did one store not hit its sales goals over the summer while a store two miles away did? Is one store using its labor more efficiently than another store? Is one store team more effective at selling than another? They can only look at past results and guess what went wrong. Finance departments have the data to make better decisions but analyzing the numbers and distilling them into actionable insights is a major stumbling block.

AI is what unlocks Finance’s store labor spend optimization and sales-driving ability. With massive computational power, AI tools can crunch the data necessary to understand the unique patterns of each store and make better future forecasts. That level of detail gives Finance the information it needs to optimize labor spend and drive sustainable growth.

For example, with AI, Finance can identify performing stores that at first glance appear to be failing. Consider a store where sales are down 7% vs LY, but traffic is also down 20% because the anchor tenant in the mall closed. To only be down 7% in the face of 20% fewer shoppers shows this is a great team that can optimize the traffic they do get and is efficiently using its payroll. But if the store’s sales goal is set to 3% above LY, being 7% down will be a downer. Store associates will likely be demotivated, unenthusiastic, missing bonus, and possibly on the verge going to another retailer where they can feel successful. Using insights from AI, Finance would recognize that this is a great team worth retaining and save the organization from unnecessary employee churn costs.

AI changes the narrative about retail Finance teams. Instead of being a giver of blanket forecasts and allocations, or payroll cuts, the back office department becomes a trusted strategic partner. Their superpower of data analysis and forward-looking insights helps  store teams run efficiently and effectively while ensuring payroll dollars are not wasted. They aren’t demanding dramatic swings in spending but guiding smarter allocations that keeps the retail ship stable. Complexity is now an opportunity to finely tune sales goals and labor budgets, setting up the organization for long-term selling and profit margin success.

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