Operations Research Analyst
8:10 am... After booting up my computer, I read through the emails that I received over the weekend.
8:30 am... Brenda, a BA on my project team, stops by my office. She is building an Excel® tool for her client to track how often physicians are hearing information about our client's products, compared to other products in the market. After giving her a few pointers about laying out the tool, she goes off to begin putting it together.
9:00 am... I receive an email from my project manager, Neil, with questions about my sample allocation project. Sales representatives typically leave samples of their products with doctors & hospitals that they visit. Other than sales people, samples are typically the largest promotional investment that our clients make. Since inadequate sampling can really hurt product sales, I have been working with the brand team to design a methodology to allocate samples for several of our client's drugs. I created a tool for each drug that would allow the regional directors to adjust the recommended sample allocations (in order to account for localized differences). Because the drugs are different, they have slightly different sample allocation methodologies. Using SAS®, I produce a monthly data cut that will answer many of Neil's questions. I export the data to Excel®, format it and produce several key graphs.
10:00 am... People start showing up in my office for our project's weekly conference call. The woman that I share the office with is also on the project, so we host the call. We are one of the largest project teams, with 15 full-time people (5 in Evanston, 10 in Princeton) staffed on this project. Each week, we have a conference call to discuss data issues and project updates. I hear that our client's task force has finally made a recommendation on how best to sample at institutions (hospitals, etc.). I have been waiting for that recommendation so that I can finish up the second quarter allocations.
10:30 am... After the weekly call, Neil and I chat briefly about the implications of the task force recommendation. After some discussion, we realize that there are a few more things that we should explore before contacting the client about the institutional sampling. I begin to look at the different ways that I can allocate the institution samples, but I am not familiar with institution level data. I need to talk to Natalie, the Business Information Specialist in charge of the data warehouse, so I leave her a voice message.
11:45 am... I grab my running gear and head out for my lunchtime run. I can't wait until the days are longer and I can run after work, but it is nice that I can work it into my schedule during the shorter days. After a 40-minute run and a quick shower, I buy lunch in the cafeteria and head back to my desk.
1:10 pm.... Natalie calls me back, and we talk for a few minutes about the different datasets that are available for my analysis and how they are all connected. I am glad that Natalie is extremely knowledgeable and always willing to help me out! Now I'm ready to begin looking at the institution data. Unfortunately, the results show that this is not going to be as straightforward as we had hoped. I run a few extra cuts and set up a time to sit down with Neil this afternoon.
2:30 pm... Next week, our team will be meeting in Evanston, to talk about our client's evolving needs. I am making a presentation about the upper respiratory market, including information about several drugs that our client promotes in that market. I am almost finished with the Microsoft® PowerPoint® slides, but I have a few more things that I would like to add. I look back through the analyses that we did for those products last year and pull some key graphs and information from the monthly presentations that we prepare for the brand team. I am excited to share some of our analyses with my team. It will also be interesting to hear what the other analysts have been up to. Perhaps I will be able to apply something that they have done to my future work.
4:00 pm... I head over to Neil's office. After some discussion about the optimal sampling strategy for institutions, I am ready to call our contact on the client's brand team.
4:30 pm... I call the client and discuss the institutional sampling strategy with him. He agrees with our recommendation. He also explains some changes that he would like to make to the methodology for next quarter. He feels that our methodology is missing one key element that is driving sampling. Before agreeing to these changes, I explain that I would like to do some historical analysis to see how much this will change the allocations and what the relationship between all of the different drivers is. I plan to call him back tomorrow and let him know what I find out.
4:45 pm... Using SAS®, I create the final sample distributions for Institution-based sampling. I export the data into Excel® and format it, before sending it off to the client.
5:30 pm... I begin looking at the new things that the client felt would impact sampling. I pull the necessary data in SAS® and run some correlations to determine if there truly is a relationship between these new elements, market share and sample utilization (the way sales reps have historically used samples). David, another OR Analyst in the office, has created an Excel® tool that makes it very easy to look at the correlations between different variables. I find a couple of problems with the way the tool works, so he stops by to help me out. He is trying to make his tool generic so that other people can use it easily (I am always impressed with the creative things David can do in Excel®!). He corrects the issues and I finish up the analysis. I leave Aaron a voice mail summarizing the results I have found.
6:30 pm... Neil stops by on his way out, and I show him what I have been working on. We stop by David's office and discuss some of the ways he could make his tool even better. After the brainstorming session, I head back to my office, organize my desk, check my voice mail and email and pack up.
7:00 pm... As I am headed out, I see that two consultants, Yvonne and Stacy, are also just finishing up their work. Since none of us want to cook, we decide to head into Princeton to find something to eat, before heading home for the night.