As a multilocation independent pharmacy grows, identifying challenges and changing course based upon data makes it much easier to expand. In this interview, Blount Discount Pharmacy‘s president and co-owner, Phil LaFoy, and Retail Management Solutions‘ VP of sales and marketing, Mike Gross, discuss how leveraging store data can help manage the store, track employee behavior, understand product movement and pricing, and ultimately allow you to make better business decisions.
|Blount Discount Pharmacy: Four locations free-standing, one in a hospital professional building, and one closed-door pharmacy focusing on specialty and long-term care, located in the communities of Maryville and Alcoa, Tenn.
Co-owners: Phil LaFoy and Aaron Clark; 85 employees. Prescription volumes up to 500 or 600 prescriptions a day at busier locations.
Mike Gross: Phil, these days there’s such an opportunity for pharmacies of all sizes to put data at the center of their decision-making. Tell us what kind of critical data you look at on a daily, weekly, monthly basis.
Phil LaFoy: Well, we start with basic things. We are looking at our POS [point-of-sale] data to judge accuracy and performance. For example, especially at night, I might have two cashiers on, and they’re both working the same shift in the same place, and I’ve got one that does 20 transactions and one that does eight. Now with Retail Management Solutions POS, I don’t have to be there to see that. I can look at the store reports and the data doesn’t lie. Then I can ask some questions to find out what’s going on. It may be “She was sick in the bathroom.” Well, okay. Because I need to know that. But the data in our POS is kind of like my eyes when I’m not there. You have to take a deeper dive and get some additional information, but it helps you ask the right questions.
|Find out more about how Blount Discount Pharmacy is leveraging POS
Gross: Do you take a look at things like customer demographics, times of day sales occur?
LaFoy: Yes, we do. One of the big things is you look at productivity from a scheduling standpoint and, again, you may not be getting the full story if you don’t have the right data. Sometimes you look, and you say, “We only filled X number of prescriptions between eight and nine o’ clock.” But then we pull the RMS data and see that we processed three times that many transactions between eight and nine o’ clock. So we wouldn’t know how busy the store really was just looking at prescription-filling volume. Maybe people called prescriptions in earlier in the day and now, when they have time in the evening, they’re picking them up. And that shows you the value of being open at that hour. So just looking at pharmacy system data is not the end-all to be-all for deciding how many of your people are working or when you need to schedule, in this example. That’s a good use of two sets of data, really, and pulling them together. CT