In our November installment, “Consolidation at the Core,” we discussed the emergence of Multi-Channel 2.0 (MC2). In this installment we begin to peel back the onion on MC2, exposing some of the critical process elements and their extraordinary value throughout the sample management continuum.


As its moniker implies, Multi-Channel 2.0 is designed to manage a much broader array of inputs originating from various business channels. In the earlier models of multi-channel 1.0, systems were often relegated to the simple processing of either personal or non-personal outreach, or requests for samples. In MC2, however, the process is much more sophisticated, offering far greater overall program efficiency. The business logic integrates many more input types (events). This is critical to ensure the most efficient paths for processing the inputs and identifying the most effective responses. Inputs pass through analytical layers, with each one categorizing and assigning a greater level of detail in order to respond in the most efficient manner.

An early processing layer might assign a simple input type, such as Action or Request to an event. Actions often apply to events such as a prescriber’s response to a survey, a sales rep’s record of a sample drop, or a prescriber’s acknowledgement of content (AOC) after receipt of a self-initiated e-sampling order. Actions do not necessarily require a direct response to the input, but they often present excellent opportunity for a subsequent action.

Requests go far beyond those of simple product samples; they include such events as an outreach for basic product information, a desire to see a sales representative, or questions involving off-label use. Unlike actions, requests typically do require a direct response to the input; another layer of processing may be required in order to determine the most appropriate or effective response. Best-of-breed MC2 solutions provide a cost-effective platform for properly identifying inputs and easily configuring a rich depth of process layers—layers that add value and effectiveness to each touch opportunity, providing the highest levels of service and building the best possible relationship with each prescriber. And these systems “learn” through the continued inputs, responses, and subsequent reactions throughout the life of the program.


Another layer of the MC2 business logic involves channel prioritization. In a multi-channel environment, prescribers often have access to more than one channel of opportunity. This may or may not be by design. But, in the burgeoning multi-channel world, the opportunity often exists, whether intended or not. This raises two important questions. The first is, “Should we weight the input channels with respect to priority?” And the second is, “Which (weighted) response channel is most appropriate?” A great deal of information may be germane to such decisions, highlighting the importance of a very robust data structure. Some of these critical data elements may include the input channel itself, the decile of the prescriber, the prescriber’s geographic location, and/or the recorded history of the prescriber with respect to channel preference. There are in fact a multitude of variables that could add value to this layer of business logic (see Table 1 for additional examples).

Channel prioritization also helps to focus effort with respect to each prescriber eliminating mixed messages that may differ across various media. In conjunction with the closed loop process, channel prioritization also provides a degree of insurance against over- or under-sampling, as each request is inspected and matched against both existing open requests and more highly weighted sample opportunities. Channel prioritization is undoubtedly a key process layer that helps ensure the best possible chance of affecting the most positive outcome for the prescriber, while ensuring maximum cost efficiency within the program through further elimination of disparate-process waste.


• Territory status
• Rep’s right of first refusal
• New prescription (NRx) data integration
• Membership in a group practice
• Status as a “no see” practice
• Facility type
• Sample type (e.g. live vs. alternative)
• Prescriber specialty (in certain brand-specific instances)


Next time: A high-level look at some of the critical data elements involved in a best-of-breed MC2 platform and a discussion of how such data elements are critical to achieving the highest levels of efficiency and increased ROI throughout a program.


  • Michael Laferrera

    Mike Laferrera is President of J. Knipper and Company, Inc., a leading pharmaceutical marketing and fulfillment company specializing in complete sample management solutions and dedicated to the pharmaceutical and life science industries. With a top track record for developing domestic and global sales and marketing solutions over his 20 years in Pharma, Mike leads all of Knipper


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