Meet Cathy. Cathy is the VP of retail for Goodwill of Metropolis USA.
She is responsible for retail operations in the 44 stores in her territory. But Cathy has a problem. The board has just given her the goal of growing net revenue by 10% in the next twelve months.
Fortunately, Cathy’s just implemented Solutions DGR, which has the tools and data Cathy needs to solve the problem. Cathy knows that she needs to increase her average transaction value to hit this target, so she calls her solutions architect at her DGR partner, Solutions ITW.
Together, they craft a 4-part strategy.
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First, Cathy locks down pricing and promotions using the built in best practices available in the Solutions DGR platform. With Solutions DGR, she’s now able to enforce pricing and promotions in the front AND the back of the store, preventing employees from accidentally (or intentionally) giving discounts or selling items too cheaply.
Next, Cathy decides she needs to maximize the value of the donations coming in the back door of her stores. Because she now has valid, accurate, and honest production data, she knows which processors are producing VALUE, not just the ones that are putting out a lot of stuff.
She starts working with her store managers to coach and train processors to maximize the value of the donations coming in. Together, they start to shift processor mindsets. Instead of focusing on just quantity, they now are starting to think about quality and value. Suddenly, the quantity, quality, and value of what is being produced starts to go up.
Cathy’s next step is to implement a processor incentive program. Based on what she learned from Dave, another Solutions DGR customer, and using the data and information Solutions DGR collects for her, she’s able to help her processors make more money and drive far more value from existing donations.
She’s shocked to see how processor behavior changes so quickly and so easily, and she sure has fun reporting her growing sales number to her executive team.
Finally, Cathy decides to do a pricing study. She’s never been able to do that before. But with the data she has now, she can figure out how long products take to sell and what her sell through percentages by category, by store, by processor, and even by season of the year. Armed with this data, Cathy starts a series of pricing experiments and learns that with a few small pricing and promotion changes, she can improve her bottom line without having to find more donors, customers, or employees.
Over the next year, Goodwill of Metropolis realizes a 14% jump in net revenue, and the board and the whole organization is excited about all of the new mission initiatives they can fund with their new revenue.
After celebrating their results with the executive team, Cathy goes to bed happy that night. But she’s already dreaming about using her data to optimize store layouts, staffing schedules, promotions, and even implementing a new AI-driven loyalty program.