Channel data management: Diving deep in the channel data mine
While 'data mining' is the catchphrase of the moment for business analytics, some pharma companies are finding value in mining their channel data assets
Data mining, and now, "deep” data mining, are this year's buzzwords for the latest and greatest in software tools and services to derive marketing and other business value from sales data and other commercial activity. For many pharma companies, the business analytics has focused mostly on tracking and studying sales and scrip data—first and foremost, for incentive plans for field sales personnel. But an increasing number of pharma companies are learning the value of "data they already own,” as one industry source puts it—the channel data that comes from EDI transactions with wholesalers and some retailers.
Channel data has been around for a while now in pharma; it got a big boost in the mid-2000s, when the major wholesalers converted to fee-for-service (FFS) arrangements with their pharma suppliers. As part of the service, wholesalers now routinely forward inventory and shipment data back to the manufacturer, usually in the form of EDI datasets known by their numerical designations—867, 852, 180 and others. Initially, manufacturers used these data to monitor the performance of the wholesaler contract, ensuring both that sufficient inventory and service levels were being maintained, and that wholesalers weren't overstocking to take advantage of future price increases with lower-cost inventory held in reserve.
But in fairly short order, manufacturers and some pioneering IT firms recognized that there was informational gold in the mass of EDI transaction data—getting visibility down the supply chain on what was happening in the marketplace, and how various channels—retail pharmacy, hospital, mail order, etc.—were performing. In some cases, these supply chain views could match, or even exceed, the insights gained from traditional scrip data from pharmacies.
At this point, the distinctions between data analytics derived from EDI channel data, and those derived from scrip data, are mostly academic; vendors of both sorts are at work merging these (and yet other datasets) into vast data repositories on which sophisticated analytics can be run.
A good example of the momentum in this area can be seen from Mu Sigma, a data analytics firm in Chicago that originally cut its teeth on handling market-trend data for Microsoft. At the end of last year, it garnered an eye-popping $108 million in venture cap funding from some blue-chip VC firms, including Sequoia Capital and General Atlantic Partners, on top of $42 million in earlier rounds. According to the company's website, it is working with unnamed pharma companies on such core marketing activities as optimizing sample-distribution programs, promotion planning, returns analysis and market segmentation. But it also provides supply-chain analytics, covering such topics as inventory optimization, replenishment planning and demand forecasting.
At ValueCentric, company founder and president, Dave Janca, says that the company's cloud-based ValueTrak 5.0 platform is built into so many manufacturers and wholesalers that it has become a de facto industry connecting point—that is, if a manufacturer engages with a new wholesaler, the manufacturer is likely already to find a ValueTrak connection in place. "We got some flak from customers early on for offering only a hosted (cloud) system, but we see the industry has become so comfortable with it now that it's a preferred platform,” he says. One of the main advantages—common to nearly all cloud-based systems—is that a software upgrade for one client can become the same upgrade for all clients; there isn't the need to go from site to site installing a next version of the software.
ValueCentric has pushed hard to overcome one of the key stumbling blocks of FFS data—the fact that it is "blinded” (i.e., locations and identities are removed) for many retail customers. Last fall, it announced a major agreement with Walgreens, the second-largest pharmacy chain, to make Walgreens EDI data available to manufacturer-suppliers via ValueTrak. Walgreens will employ this access to closer business relationships with manufacturers and others—organizing, for example, in-store promotional programs with a manufacturer, in hopes of attaining a preferred trading-partner agreement with manufacturers. For the manufacturer, in turn, having ValueTrak and the right contracts in place will enable it to gain a granular view of marketplace activity, delivered in near real-time (Janca says that in-store EDI data is updated at least daily).
Another enhancement of ValueCentric's service is to enable mobile data access. "More people than just those in business analytics want to be able to use the reports we generate, and they want to access those reports wherever they are,” says Janca.
At Integrichain, Josh Halpern, VP of marketing and business development, points to the company's latest upgrade of its cloud-based platform, Dynamic NextGen Analytics (DNA), with modules to process wholesaler data directly (that is, the raw wholesaler data goes through DNA before being delivered, in cleansed form, to the manufacturer). Tailored reporting is now being generated from datasets based on EDI 180 (product returns); EDI 849 (reconciled chargebacks); and EDI 850 (purchase orders).
Halpern says that DNA users benefit from the spade work Integrichain has done to create a near-universal master customer data file, containing information on 300,000 points of sale, 14,000 business organizations, and a wide swath of the integrated delivery networks (IDNS—hospitals and related health-delivery outlets) and physician networks. Definitions and rules are written to organize 82 distinct classes of trade (manufacturers and others create their own definitions of COTs; with the already-defined classes, the assumption is that a manufacturer could match its COTs with those in the Integrichain data file).
Halpern says that clients have progressed well beyond basic FFS scorecarding in their use of channel data. "The next generation of FFS agreements is considerably more complex, putting more of the fee at risk based on achieving defined performance measures. We address that by making our platform configurable to customers' requirements.”
Beyond basic scorecarding, Integrichain clients are gaining better control over such business scenarios as doing inventory planning for loss of exclusivity as branded products come off-patent; doing demand planning for seasonal products; and managing slow-moving products—which never have high inventory levels, but which sell better when sufficient product is on hand to meet demand. "An interesting example of these capabilities is to do demand planning based on the dosage form of a product—which is moving faster, the 30-count bottle or the 500-count bottle? Getting answers to questions like that, in time to take corrective action, is a new capability for most of the marketplace.”
In early February, Model N announced the acquisition of another business-intelligence firm, LeapFrogRx (Waltham, MA), which has been working on pharma applications since 2002, as it rolled out a rebranding of "revenue management” as "revenue management intelligence.” Model N has promoted its capabilities in contract management and detailed tracking of reveue based on trading partner relationships and government pricing policies; now it is adding analytic directed at marketing and brand planning, among others.
"Before, we had a compelling case to make to the CFO of a life sciences organization, in that we could develop a dashboard that showed the CFO not just how revenue was accruing, but also how specific customer contracts were performing,” says Gopkiran Rao, director of marketing. Now we are talking to other parts of the enterprise—sales operations, marketing and others. It's an end-to-end enterprise view of the business, rather than one or another silo.”
Fix the disconnect
Model N's tools (also cloud-based) are now rebranded as ImpACT, with modules for pricing, managed markets, brand management, field sales management and channel management. The company also highlights its ability to manage the Public Health Service 340b programs, which makes drugs available at a discounted price for federally funded "covered entities”—generally health facilities for the indigent.
Commenting on the Model N LeapFrogRx acquisition, Eric Newmark, program director at IT advisory firm IDC Health Insights (Framingham, MA) notes that "The current disconnect between sales and marketing and the revenue management side of pharmaceutical manufacturing exists at a fundamental level. Try asking a pharmaceutical company to tell you which US state is [its] most profitable.” According to him, aligning channel activity with the variety of sales and promotional actions that manufacturers are taking will result in better oversight into market performance, better tools for negotiating tier placement on formularies, and, not incidentally, better compliance with federal and state pricing and reimbursement policies.
At Revitas, Joe Marttila, senior director, puts an emphasis on the company's ability to match brand performance throughout the life cycle of a product, along with the efficiency of developing the right conract terms quickly to customers. The company's CARS system has long been used to reconcile chargebacks and rebates, and to provide the pricing calculations necessary for government pricing programs. Going back several years, Revitas acquired Edge Dynamics, one of the early FFS management firms, and uses that functionality to bring channel data into the contract administration mix.
Revitas' software offerings are now organized as a suite of services built on its Flex platform, which incorporates Oracle database software with tools developed by Revitas for contract administration; dynamic pricing analysis; membership (of GPOs and other classes of trade) management, and government pricing compliance. Marttila says that, with the right analytic tools, Revitas software can be used for aggregate spend analysis, the looming burden on manufacturers to report all types of reimbursements to prescribers (See Pharmaceutical Commerce, Jan/Feb, p. 1). "It's a question of being able to pull through our customer master data on royalties, milestone payments (to researchers) and the like, and then combine with the spending by sales and marketing teams,” he says.
In conversations with these data mining firms, there is a clear impression that providing solutions for business problems like aggregate spend analysis is just the beginning of new analytical powers that are being developed (see table). Valuecentric's Janca notes that as the data made available becomes more granular, channel data management begins to resemble the track-and-trace programs being developed in the supply chain management arena (see p. 26). "Track-and-trace requires more data elements than what are currently being collected, but when there is industry agreement on what the critical data elements are, we could be providing a solution,” he says. "Many of the connections with trading partners in the supply chain have already been made.”
One feature of many of these business-intelligence systems is that they are cloud-based. Although cloud-based computing is not automatically the "better” way to run an IT department, it is true that a key part of a cloud-based system is that trading partners or other participants have gone through the exercise of creating secure links to the data repositories operated by these vendors. With links in place, one of the key hurdles of track-and-trace systems—the ability to share data on demand with trading partners—is already in place. Another is that many of the trading-partner identities are already established in the master data files (although it is likely that only some of them have used the "global location number” (GLN) that the GS1 organization has espoused. One obstacle, however, is that in many cases channel data are blocked ("blinded”) by retailers from inclusion in a shared database.
Table: what you can do with channel/contract data