It is difficult to think of an area where the pharmaceutical industry is not under siege these days. Regulators spawning new rules, governments putting price caps in place, safety advocates demanding more information on side effects, health maintenance plans implementing their own cost controls, and expiration dates on patents coming around -- all are putting the squeeze on Big Pharma, and it has been reflected in industry earnings.
Patent expirations alone will wipe out US$80 billion in projected revenue, Patrick Homer, life science sales 
and marketing practice principal at SAS 
, told CRM Buyer.
As a result, pharma is now looking to analytics technology and applications to do the same for its industry that they did for the financial services and retail industries five to seven years ago, he said. The goal, as always, is to maximize margins.
If this sounds like a game of catch-up, that is essentially because it is.
Rooting Out Hidden Data
Until recently, pharmaceutical companies did not have to focus too deeply on maximizing every margin point, Homer noted. "Pharma has traditionally been a high margin business, and so is only now starting to look at optimizing its existing assets and infrastructure."
A new mindset has developed in the industry over the last year or so, said Justin Davidson, associate analyst with Datamonitor.
"Traditionally, all pharma had to do to sell more drugs was employ more sales reps and equip them with the right technology, such as mobile solutions," he told CRM Buyer. "Now, because their main drivers of profits are being squeezed -- the end of blockbuster drugs' patents and the dearth of new blockbuster drugs coming to market -- pharma has come to the realization that is has to be more effective in selling drugs."
Integrating existing data more effectively is the obvious starting point -- and most pharma companies have made great strides in this never-easy quest, given the many places where data can hide in an enterprise .
Other analytics implementations aim to guide sales reps to focus on the highest-value physicians for the products; they even extrapolate on existing data to help reps better understand the end customer instead of merely focusing on the prescribing physician, as pharma companies have been doing for decades.
Multiple Data Sets
The primary goal of many organizations is to synthesize multiple data sets -- external pharmaceutical industry data sources from IMS and Verispan, as well as internal call or sample data from Siebel and Dendrite (Nasdaq: DRTE) CRM systems -- and then extract combined intelligence, said Ted Snyder, product marketing manager for Tibco Spotfire.
"To some extent, the day-to-day activities of reps have changed little. They are still visiting physicians for instance, but there are a lot of challenges in reading that information at the institutional level," he told CRM Buyer.
"New data is pouring into the organization every month," SAS' Homer agreed. "Firms are trying to put in some automation to support that, in order to measure evolving trends across the enterprise."
Pharma companies are looking to predict physician-subscribing behavior, he added. They're looking to measure return on investment of industry-specific sales force automation applications in specific states or even cities, for example.
Pharma companies are also applying analytics to more basic, competitive issues, Snyder said. "They want to be able to react quickly to events. Scheduling something like a product launch is never a sure thing, because you can never depend on an approval's timing."
On the other hand, if an already-approved product comes under review by the Food and Drug Administration , pharma companies need to be able to react with as much data on hand as possible, he continued.
In these scenarios, "a lot of data needs to be analyzed very quickly. Then they have to execute on that strategy and enable their sales force appropriately," Snyder explained. Ancillary applications like call planning and and territory realignment come into play as well.
A speedy analytics process has become a competitive advantage for these firms, he said. "Now they can analyze data within days instead of weeks."
Meet Your Customer
One of the more interesting trends in pharma companies' analytics deployments is the emergence of projects designed to shed light on a previously very amorphous and elusive group: the end customer.
"Few industries try to sell without at least a basic grasp of the end customer -- but pharma has been operating like that for years, due to regulatory and industry constraints," Colin Shearer, senior vice president for market strategy at SPSS (Nasdaq: SPSS) , told CRM Buyer.
In one project he cited as an example, a customer used its data-mining technology -- SPSS Clementine -- to predict the variables that drive consumer behavior for a certain treatment type.
Mainstream projects, though, tend to focus on the doctors -- the only legal channel through which patients can receive pharmaceuticals. Another SPSS client used its text-mining application to collect and analyze both structured and unstructured data -- notes, essentially, that doctors kept in their diaries. Analyses turned up patterns that accompany prescribing behaviors, Shearer said.
"Pharma companies will always be removed from their end users," he observed, "but now they are learning to leverage what data they have available to them."
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