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It’s well known that beyond their role in filling prescriptions as an essential link between health care providers and patients, retail pharmacies have long offered a broad range of products aimed at helping people to maintain their health. More recently, drug stores have made significant advancements into the provider realm by offering services like minute clinics, optical care and vaccinations — a wave that continues with moves like CVS’ recent purchase of primary care provider Oak Street Health.
Garrett Sheridan
But as with many participants in the health care value chain, retail pharmacy chains need to utilize technology to its full potential. The rapid advancement of technology and the emergence of artificial intelligence (AI) present retail pharmacies with ample opportunities for improvement. For example, they can improve store operations through tech-enabled inventory management and ordering to reduce costs, avoid out-of-stock and minimize waste.
There is also a tremendous opportunity to reduce costs while increasing revenue using AI to optimize the store labor model. This effort can include maximizing the labor hours to roles and expected traffic. Additionally, automating repetitive tasks and processes can free up employees’ time to focus on high-value activities, such as providing patient care and counseling.
Learnings from other retailers
Some retailers, from boutique chains to big-box retailers, are unlocking significant benefits through digitally enabled store labor model optimization. The most successful and profitable retailers continuously monitor and manage their store operations and retail execution to better align staffing with demand.
Tom Hill
Retail pharmacies have evolved to offer a broader set of products and services than just medicine. Localized product assortments and health screenings have increased their need for specialist rather than generalist roles. Optimizing labor models with AI can help pharmacies to allocate resources better, reduce labor costs and improve customer service. Cross-training and upskilling can be used to ensure that someone in the store can help. The ability to grow the skills with existing staff is essential, particularly as many employers need help attracting and retaining retail team employees.
Automation can supplement AI to a mild extent, as it can be employed to perform some repetitive tasks and processes like automatically generating prescription labels and instructions after screening for patient drug interactions, allergies and dosage accuracy.
Applying AI to the staffing model
In our experience, the most successful and profitable retailers continuously monitor and manage store operations and retail execution to better align staffing with demand. Getting there is a multi-step process, but the reward of maximizing the labor return on investment is worth it now more than ever in an era of wage inflation.
The initial step to maximizing the return on the business’s labor investment is to leverage AI and advanced analytics to classify and manage stores based on operationally relevant attributes like units per transaction (UPT), average dollar sale (ADS), customer conversion rate, store revenue, net promoter score and other vital indicators. Then, analytics should be run to look for commonalities and correlations based on other, more secondary metrics. Foot traffic and customer dwell times should be analyzed as input to the floor and department coverage in the store. Separate from the labor model analysis, the results of this exercise can also be leveraged to inform the store layout and planogram for visual merchandising. These steps should be performed for all of a given chain’s retail locations to build out the store segmentation model. The store segmentation model clusters similar stores, based on the attributes, to inform how labor should be deployed, typically with three to five variations across the entire fleet of stores.
The next step is to define worker roles based on store characteristics to ensure optimal coverage at each location. People hours needed and the performance of current employees need to be analyzed as initial input. Roles should be defined based on store characteristics to cluster the work according to what needs to be done; blending roles and tasks will allow for greater flexibility in future staffing plans. Compensation and incentive programs that will support and encourage the desired employee behaviors, especially around workers’ ability to perform multiple tasks, should also be assessed and designed at this step.
Once a working model is in place, these more operationally focused measures should be used to answer critical questions, such as what the relationship between sales levels and employee coverage is and how team coverage should be concentrated during certain days and times that may be especially busy or slow.
Of course, proper optimization is never a “one-and-done” effort; store operations must be continuously monitored and managed to optimize the pharmacy labor model through periodic reassessments and adjustments. Once the initial optimization effort is complete, subsequent exercises become much easier. Many retailers will pilot the labor model to compare against a control group.
Stepping up to the challenge
The benefits of AI-driven store labor model optimization are tantalizingly clear; increasing revenue while reducing costs is a significant double-win. While some of what needs to be done is outside of the traditional core competencies of retail pharmacies, the ongoing move to digital means that every business needs to increase its ability to work with technology in general and data in particular. A clear strategic direction will be required to help overcome the technical barriers and costs associated with implementing new technology. As with any data-driven effort, privacy and security concerns must also be addressed to help safeguard customer data. Change management will be needed in tandem with workforce training to help ensure that pharmacy employees are engaged with the improvement and able to take on their newly expanded duties. Store management will benefit from fresh reporting to manage the business and utilize training materials to reinforce any new behaviors with the team. While these sorts of challenges need to be considered, the rewards of success far outweigh the risks of failure.
Garrett Sheridan is cofounder and chief executive officer of business transformation advisory firm Lotis Blue Consulting, and Tom Hill is a partner at RevenueShift. They can be reached, respectively, at gsheridan@lbconsulting.com and tom.hill@revenueshift.com.
