How to Make Sure You're Obtaining the Very Most Out of Your Critical Prescription Data In the first piece of the series, we discussed how important the data is that you collect and how to best start using it (See Article Among "How to Make Sure You're Obtaining the Very Most From Your Prescription data)
Partly two we going to drill into your data and find out what we can find....
How can you identify the very best targets?
Script history can also help your business identify targets. Many times, smaller pharmas will contract with third party sources to get this done. Generally, these sources will request just one cut of IMS data to assist identify targets, and they'll massage the information to generate a target list and maybe territory alignments. Why not put this single cut to make use of?
Especially with startups, this cut could be a baseline to compare against, perhaps Six months or a year later, to find out bonuses for that reps. This data may also be used to tweak territory alignments and targets long afterwards the 3rd party is finished with their work.
During target identification, it's vital that you know who the "early adopters" are when offering a new drug, especially for a smaller pharma company. This data, particularly with 2 many years of history, enables analysis to determine what doctors are "quick to switch" to new drugs as they come out, or which ones stick to the "tried and true". You don't need to heavily concentrate on the "tried and true" until later inside your campaign rolling out a brand new drug.
With the appropriate analysis tools in place, pharmas be capable of 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 can you determine the effectiveness of sampling and call frequency?
Another reason to combine and analyze information is to determine effectiveness of sampling and call frequency. Carefully crafted queries can display certain doctors receive too many samples for the scripts they write. Exactly the same kinds of queries can correlate that particular doctors respond perfectly to frequent calls while others just don't need the interest to drive their script writing.
How best to investigate all of this data?
The important thing to successful analysis is building the proper data repository where any kind of prescription data mining associated with marketing and advertising can be carried out. In very large operations, often these power tools are made in house and managed by an interior Information Technology (IT) department. In this section, we'll discuss a few methods to provide the analysis, whether done in-house or outsourced. Next, we'll take a look at why strong consideration should be given to outsourcing this data repository to some third party.
For any tool to be successful, it has to deliver enough detail so that your sales organization has enough information on each doctor they ask, but must also have the ability to roll-up these details towards the highest level. So, for targeting and actual calls, information should be easily available towards the sales reps allowing them to know key information that will help them on their own call, such as script background and some
automated trend analysis. But, for compensation, doctor detail might be an excessive amount of and instead a territory level detail report (a consolidation of doctor detail) is required. In larger organizations, with several sales layers, additional "rollups" may be needed for districts, regions, and areas.
Finally, for every level, graphical "dashboards" are very helpful to explain trends. Legitimate effectiveness, these graphs can then be "drilled upon", letting you see details, either graphical or tabular, that comprise a high level graph.
PharmasThis data needs to be "fresh", with monthly extractions from IMS or Verispan quickly built-into the information repository. This really is critical as the marketplace is quickly changing, especially if you also analyze group plan track data.
Let's consider a good example of how drill downs can easily result in information. Imagine that the national sales manager examines a drug's 2 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 what about a certain group of plans is
lagging behind others. The Group Plan Manager could then get involved and maybe new incentives to groups specializing in certain territories may help fix the problem.
But there are lots of different ways to help analyze the information to determine trends. One is the ability for the tool to "group" doctors in small customized entities and analyze the trends.
SupplementsFor example, if your company support speaker's bureaus, then it might be good to determine how effective they are. Patient registry participation may 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 do with doctors (and also the current political weather conditions are for additional stringent and possibly government oversight), analysis of data that relates to adjunct activity becomes much more important.
Finally, a proper analysis tool helps bring together data that may be disparate and controlled by various people inside the organization. For instance, in one organization we serve, one person controls the spreadsheets which contain data relating to the sales roster. Someone else (or 3rd party) controls the data associated with territory alignments (usually by zip code).
Maybe even another examines targeting and call frequency data. It is easy to observe that all of this data correlates but can easily get out of sync if a centralized system for management sits dormant. We find that organizations that consolidate these details (so far as data) have much more accuracy in most data involved.
Adding even more complication to a quickly changing environment, turnover becomes a problem when key sales ops staff move in one drug company to a different. If an effective system is in position business rules are loaded and also the tribal knowledge issue is minimized.