In the first article of the series, we discussed how important the data is that you collect and the way to best begin using it (See Article One of "How to Make Sure You're Getting the Very Most Out of your Prescription data)

In part two we likely to drill to your data and find out what we will find....

How can you identify the very best targets?

Script history will also help your organization identify targets. Often, smaller pharmas will contract with third party sources to get this done. Generally, these sources will request just one cut of IMS data to help identify targets, and they will massage the information to come up with a target list and perhaps territory alignments. Why don't you put this single cut to use?

Especially with startups, this cut can be a baseline to compare against, perhaps Six months or perhaps a year later, to determine bonuses for that reps. This data may also be used to tweak territory alignments and targets long after the 3rd party is finished using their work.

During target identification, it's important to know who the "early adopters" are when bringing out a new drug, specifically for a smaller pharma company. This data, especially with 2 many years of history, allows for analysis to determine what doctors are "quick to switch" to new drugs because they emerge, or which of them stick to the "tried and true". No need to heavily concentrate on the "tried and true" until later inside your campaign rolling out a brand new drug.

Using the appropriate analysis tools in position, pharmas have the ability to quickly change territory alignments, product market definition and campaigns. To stay competitive in this ever changing market it is important to eventually be capable of reload fresh data monthly so you can identify trends very quickly.

How do you determine the effectiveness of sampling and call frequency?

Another reason to combine and analyze data is to find out effectiveness of sampling and call frequency. Carefully crafted queries can show certain doctors receive too many samples for that scripts they write. Exactly the same types of queries can correlate that certain doctors respond very well to frequent calls while others just have no need for the interest they are driving their script writing.

Just how to analyze all of this data?

The important thing to successful analysis is building the proper data repository where any type of prescription data mining associated with marketing and advertising can be performed. In very large operations, often these tools are made in house and managed by an interior It (IT) department. In this section, we'll go through a few ways to deliver the analysis, whether done in-house or outsourced. Next, we'll check out why strong consideration should be given to outsourcing this data repository to a 3rd party.

For any tool to achieve success, it must deliver enough detail which means that your sales organization has enough info on each doctor they ask, but should also be able to roll up these details towards the highest level. So, for targeting and actual calls, information should be readily available to the sales reps allowing them to know key information that can help them on their call, for example script background and some
automated trend analysis. But, for compensation, doctor detail may be an excessive amount of and instead a territory level detail report (a consolidation of doctor detail) is needed. In larger organizations, with several sales layers, additional "rollups" may be required for districts, regions, and areas.

Finally, for every level, graphical "dashboards" are extremely useful to point out trends. For real effectiveness, these graphs can then be "drilled upon", allowing you to see details, either graphical or tabular, that make up a high level graph.

Pharmas

This data must be "fresh", with monthly extractions from IMS or Verispan quickly built-into the information repository. This really is critical because the market is quickly changing, especially if you also analyze group plan track data.

Let's consider an example of how drill downs can easily result in critical information. Imagine that the national sales director examines a drug's two year trend and sees moderate growth. By "drilling down", the manager understands that the drug's growth curve vary dramatically by district. By drilling down further, the manager could see that certain territories might be outperforming others. By focusing on certain areas and searching at plan data, a manager often see that perhaps a certain group of plans is
lagging behind others. The audience Plan Manager could then get involved and maybe new incentives to groups specializing in certain territories may help repair the problem.

But there are many different ways to assist analyze the data to determine trends. One is the power for that tool to "group" doctors in small customized entities and analyze the trends.

Supplements

For instance, in case your company support speaker's bureaus, then it might be good to see how effective these are. Patient registry participation could also be integrated using the oral appliance analysis/focus be give to those doctors in the registry. As guidelines tighten as far as what pharma's can perform with doctors (and also the current political weather conditions are for additional stringent and perhaps government oversight), analysis of data that pertains to adjunct activity becomes a lot more important.

Finally, a proper analysis tool helps gather data which may be disparate and controlled by various people inside the organization. For instance, in a single organization we serve, one individual controls the spreadsheets that contain data concerning the sales roster. Another person (or third party) controls the information associated with territory alignments (usually by zipcode).

Maybe even another examines targeting and call frequency data. You can easily see that all this data correlates but could easily get free from sync if your centralized system for management is not used. We find that organizations that consolidate this information (so far as data) have much more accuracy in most data involved.

Adding even more complication to some quickly changing environment, turnover becomes a problem when key sales ops staff move in one drug company to a different. If the effective product is in position business rules are loaded and the tribal knowledge issue is minimized.