Pharmaceutical commercialization is getting more challenging every day. Analytics talent is in high demand to support informed and insightful business decisions across a global landscape. Yet, despite the increasing market
complexity, most commercialization leaders are facing cost reduction pressure resulting in the exploration and assignment of work to lower-cost alternatives.
In terms of analytics support, there are typically two resource plans to choose from:
Plan A: Continue the analytics function with onshore support for a high quality solution
Plan B: Replace the analytics function fully or partially with offshore support for cost savings
Does either plan provide a viable solution for commercialization leaders to have efficient and cost-effective analytics support? What challenges will one run into by choosing either plan?
Let’s take a closer look at what has happened in analytics groups.
PLAN A: Onshore support
Working with onshore analytics support appears to be an obvious choice if cost is not a concern. Who wouldn’t want the benefits of working side-by-side with a colleague or a consultant with no time zone difference? Then again, one also has to consider that having onshore support does not always ensure worry-free operations, and when is cost ever not a concern?
The common challenges often experienced by analytics managers with onshore support
1. Costs increase as the analyst gains more experience
Rising costs resulting from your analysts’ increased experience is inevitable, and these costs become significantly higher with top performers. LinkedIn has enabled your top talent – those you have spent years training and developing – to be fully exposed to recruiters. This makes keeping up with the compensation and career opportunities available to these top performers a costly
challenge that may be close to “mission impossible.” If you are outsourcing your analytical needs to a vendor, the costs for the same consultant will also increase over time, as the vendor company experiences the same challenges of retaining experienced consultants, or promotes or moves
them to other accounts. This spiraling of costs results in procurement constantly requesting cheaper alternatives through an RFP, which also causes large scale business disruption.
2. Business continuity is easily lost with turnover
The first reaction after receiving a two-week resignation notice is to think about backfilling and a transition plan. In reality, there is barely enough time to fully transition the work to other team members and still meet project objectives. Business and deliverables get disrupted, and dissatisfied clients may start complaining about losing service, while other team members become disgruntled about the increased workload. Unfortunately, an outsourcing model may not help much in this situation since consultants with matching skills are most likely not
available on short notice.
3. Significant time and effort to source and onboard
It always seems much easier to lose talent than to find the right talent. Not surprisingly, key analytical positions in the life science industry are often left vacant for three to nine months due to the high complexity of the work and depth of industry knowledge required. When you are lucky enough to find the right candidate or consultant to fill the gap, it then takes weeks for the onboarding process, and several more weeks or months for them to pick up the business.
PLAN B: Offshore Support
Although going offshore for analytics support is not a preferred service model due to time and cultural differences, it has become a mainstream practice for many large operations, as it is considered the ultimate cost-saving solution. Depending on how an offshore operation is set up and executed, however, will dictate the ultimate success of the operation.
Despite increasing market complexity, most commercialization leaders are facing cost reduction pressure resulting in the exploration and assignment of work to lower-cost alternatives.
Some critical challenges with offshore support commonly observed by working managers in analytics groups
1. Very slow uptake due to lack of knowledge and an insufficient talent pool
The US life science industry and the healthcare system it serves create an extremely unique and complex environment to navigate – one that requires a significantly long learning curve, made even more so for foreign employees having no prior experience or knowledge. In addition, depending on the offshore operation location and candidate selection process, resources with strong statistical education and analytical skills are not always sufficient. The foremost challenge
organizations encounter when moving work offshore is the hardship of getting their offshore peers to fully understand business needs and then to independently deliver and generate insights. Training an offshore team is an extremely time- and energy-consuming exercise for working managers in analytical groups, and very often it evolves into frustrating and detrimental situations if upper management does not effectively balance work quality and cost savings.
2. Turnover is still an issue
Most offshore operations structure a large team around a single functional area to simplify training needs and allow for bench resources with similar skill sets to be available to meet unexpected needs. With such a set up, it would seem that the turnover and business discontinuity issues of onshore support teams may be solved, but this is often not the case. With single function responsibility for a long time period, offshore talent has a hard time broadening their knowledge and stepping up in their career development. As a result, on average, turnover and
business discontinuity actually happen more frequently than they do with an onshore team. When turnover occurs, an offshore bench resource is not able to pick up as soon as expected, since they were not heavily involved in prior deliverables. The time and effort US counterparts then need to invest to get them up to speed again are two or three times higher than compared to working with an onshore resource.
3. Low productivity and time difference
If not planned well, the time difference alone when working with offshore support will result in a drop in productivity of 30% to 60%. This is due to the fact that most tasks and changes, regardless of how small and easy, will not happen until 24 hours later. The situation becomes further exacerbated if multiple rounds of changes are needed, or if the offshore team is not
experienced enough to anticipate the client’s needs. This is one of the main reasons why offshore teams are often limited to providing routine deliverables rather than advanced analytics requiring constant exploration and adjustment based on outcomes or client interaction and feedback.
PLAN C: Quality and Value
So far, neither plan seems to be superior to the other. The critical challenges of both plans constantly put commercialization leaders in a situation where they must decide if they should compromise on quality by moving work to offshore or compromise on cost by keeping work onshore. Is there really no better plan? Is there a plan C when the other two plans fail? If yes, what features should this “Plan C” offer?
The top 5 features required in a Plan C as voted by commercialization leaders
1. High quality and productivity
One should never compromise on quality and lose significant productivity regardless of the location of the support. In fact, quality and productivity are largely dependent upon how the resources are selected, trained, structured and managed, as well as how the process is established. This approach requires much more thoughtful planning and constant monitoring and adjustment based
on the clients’ actual needs.
2. Significant cost savings
An economically viable solution should achieve longer-term business success by properly, and periodically, identifying the best opportunity and approach to save cost. Such a plan should also mitigate the potential risk for stakeholders and, at the same
time, provide scalability.
3. Maximum business continuity with bench resources readily available
Retaining top talent and creating bench resources can be achieved at the same time without creating heavy financial burdens. The key is to plan ahead of time instead of adding resources in an adhoc fashion only when turnover happens.
4. A sufficient “better and stronger” statistician talent pool
Top statisticians and analysts can come from anywhere, and commercialization leaders are not looking for one, but many. Hence, sourcing a sufficient number of resources can be a very tough job if the support location does not offer a sufficient talent pool with matching skills and education. If the support location is not chosen wisely, the overall team productivity and capability will
easily fall short simply due to a lack of available talent.
5. Ideal face-to-face working experience
Everyone has experienced one of those situations where a five-minute, face-to-face conversation can quickly resolve so much more than a thousand emails back and forth. A good support plan should include a designated support person to provide a “personal touch” to commercialization leaders throughout the client’s regular working hours.
KMK’s “Plan C” provides all five of the most wanted features to our valuable clients. With “Plan C” added to their tool boxes, commercialization leaders no longer need to compromise between quality and value. It empowers them to build more efficient analytics groups to drive the business and achieve success.
About Michael Karbachinskiy
Michael is President and founder of KMK Consulting, Inc. Michael is an expert in targeting, promotional evaluation, and technology and data warehouse design. He has been an instrumental force in designing and executing the Novartis Marketing Science data warehouse structure, campaign ROI evaluation, and overall analytics support. Previously, Michael was in charge of statistical development programming at AT&T and was responsible for setting up financial and marketing programming design and evaluation for credit card portfolios for several large banks.
About Ning Jia
Ning is Associate Principal at KMK Consulting Inc., leading KMK’s Analytics Division and acting as Head Consultant on customer projects. Ning has spent the past 10 years supporting and leading analytical and operational engagements with various types of clients in the life science industry. She provides in-depth experience in full-spectrum sales force effectiveness, marketing science, and advanced brand analytics, as well as commercial analytics and reporting. Ning earned double Master’s Degrees from Lehigh University in Statistics and Electrical Engineering.