Get more from your pharma commercial spend using advanced
analytics
Predictive
analytics and data visualization offer pharma business leaders the opportunity
to transform commercial-spend optimization and lift returns by 10 to 25
percent.
We’ve all been in a meeting where someone says, “If I increase your marketing
budget by 10 percent, how much more top line would you commit to?” Maybe it’s,
“We’re not getting what we used to out of this commercial mix; how can we get
more bang for the buck?” Or, it could be, “This launch isn’t hitting our
preapproval sales projections; what can we do to drive faster growth?”
If you’re like most
pharma commercial leaders, you would traditionally respond to such questions
with a mix of experience-based hypotheses, situational judgment, or perhaps a
process of elimination (“We’ve tried everything else!”). But what if you could
answer each of these questions precisely, analytically, and confidently,
knowing why a change will result in improved performance—not just that it
theoretically could?
Today, the explosion of
new commercial data available to pharmacos, combined with new methods of
advanced analytics and data visualization, offer exactly this opportunity. This
article provides an overview of how leading companies are already answering
questions like the ones posed above, using predictive analytics rather than
possibilities and assumptions.
The typical situation
External commercial
spend (including all commercial-spend levers across marketing, the sales force,
and payor rebates) makes up the vast majority of operating cost for most
pharmaceutical companies today. For many, this spend has been increasing faster
than revenues over the past few years. For example, from 2013 to 2015, rebate
spend climbed at a compound annual growth rate of 34 percent and
direct-to-consumer spend grew 20 percent, but gross sales rose only 11 percent.
Additionally, companies continue to invest heavily in direct sales forces,
despite concerns that they are not as effective as they once were. Meanwhile,
ample evidence suggests that both patients and physicians are consuming more and more information (and increasingly connecting with one another) via
digital channels. Yet many pharmacos are struggling to shift their marketing
mixes to respond effectively.
Managers need tools to
help them adjust to the new commercial reality. These tools need to go beyond
the media-mix-modeling (MMM) approaches that have proved useful in industries
such as consumer packaged goods, since such approaches may not be suitable for
managing the complexity of pharmaceutical commercial models. Few traditional
approaches adequately reflect the interconnectedness of sales, marketing,
payor, patient support, and other commercial levers in pharma. For example,
disadvantaged managed-care access, relative to competition, leads to lower
responsiveness to sales details. In our experience, for the same brand,
promotion responsiveness for healthcare practitioners (HCPs) whose patients
have disadvantaged managed-care access can be up to a fifth of the impact per
detail, compared with HCPs whose patients have parity managed-care access. Insufficient
sales support leads to poor pull-through and failure to capture the full value
of access. By recognizing the interdependencies between managed-care access and
sales spend, we have seen companies generate up to 5 percent in incremental
revenue through better targeting with the same spend. Pharmacos need analytical
approaches that can appropriately capture these complex interactions.
A new approach to
commercial-spend optimization is needed—one that encompasses all these
variables and helps leaders make effective trade-offs.
A new era of capability
Fortunately, recent
technical and analytical advances have made it possible to take a comprehensive
approach. These advances have come in four main areas:
1. The ability to rapidly create and manage broad data sets. Central to any comprehensive analytical approach is
the creation of a foundational data set that includes all relevant sales,
marketing, and payor variables. This data set is most powerful when it is both
granular (that is, built at the HCP, account, and geographic-market levels) and
broad, including information on marketing (for all marketing channels), sales
force (for instance, visits, messages, and customer-relationship-management
data), and payor (for example, relative payor access, rebate levels, and
geographic reach by payor). Many business leaders worry that assembling a data
set like this implies a massive IT project that will take years and substantial
investment to complete. It’s true that companies that have invested in amassing
myriad disparate data types into a single data lake do have the potential to
create powerful insights. That said, we have found that in a short span of time
(weeks or months, not years), companies can assemble a data set that advances
their thinking by using a combination of “off the shelf” databases from third
parties and existing databases from within the enterprise. In most instances,
this is about structuring and combining data sets and metrics that already
exist, and stitching them together using off-the-shelf tools to reveal new
insights. For example, when one company realized that it did not have the
information it needed, it rapidly created a small team and built its data set
within eight weeks—and the result, created using data that was already being
collected, provided a level of transparency and objectivity that didn’t
previously exist. Companies can build on a foundational data set to create
broader data sets and generate further insights.
2. The leap in processing power offered by cloud computing. While Moore’s law, which defines the growth rate of
computing power, has begun to slow at the individual microchip level, the
amount of power that is available on demand—in the cloud—has only increased. This computing power, when paired
with innovative tools for data visualization, enables granular analyses at
scale to uncover a deeper, more nuanced understanding of performance. Adoption
of these technologies for commercial spend has been inconsistent; however, some
companies are beginning to deploy such tools in a rapid and repeatable manner.
One company, for instance, used them to answer questions like these in minutes:
Do HCP accounts tend to be more responsive to emails when they are also
detailed more often by the sales force? How does that change for HCPs whose
patients tend to have better managed-care access?
3. The emergence of advanced-analytics platforms that use an
ensemble of approaches. As mentioned above,
the three spend archetypes (marketing, sales, and payor) work in tandem to
drive performance (scripts, volume, share, and so on). It follows that any
response curves used to model performance should reflect this dynamic. In our
view, the best way to measure impact is by using an ensemble approach—one that
includes both regression analysis and test-and-control approaches—rather than
using one or the other exclusively. Companies that use a single approach
exclusively (such as econometrics or mix modeling) may come up with an accurate
representation of a single variable, “holding all else constant,” but in
reality, nothing is being held constant. Fortunately, new advanced-analytics
platforms offer a suite of approaches that can be used in combination. These
include not only standard statistical approaches but also machine learning, natural-language processing, and other techniques to
undertake analyses that use structured and unstructured data in
combination in the same analysis. By using an ensemble of approaches, companies
can also measure the impact of more variables simultaneously (for instance,
email campaigns) than would be possible if one relied on a single approach. One
company was able to measure the returns of 20 percent more variables by
adopting this approach rather than using econometric modeling alone. The
ensemble approach uncovered investment opportunities that would previously have
gone unnoticed. And, because it used both modeling and test-and-control
analyses, the company also had greater confidence in the insights.
4. New optimization algorithms and predictive analytics that
guide spend reallocation. The increase in
computing power has also enabled massive improvements in optimization
capabilities. New and emerging approaches now enable a degree of insight that
could only be dreamt of a decade ago. By applying these powerful algorithms to
granular response curves, we can unlock a set of concrete actions to implement
immediately. Packaging algorithms in easy-to-use tools leads to better
cross-functional engagement across both the business and analytics teams. For
example, one company used cloud-based tools, through a simple interface, to
identify specific sales territories that would benefit significantly from
increased digital spend against an important segment of customers. The
analytics team used these tools and worked side by side with the business team
to refine the value at stake. The outcome from this exercise was a
significantly stronger working relationship, greater confidence in the
analytics, and improved sales performance.
Capitalizing on this potential
It’s one thing to know
that a new level of analytical sophistication should be possible and yet
another to take advantage of that new potential. Those looking to start or
reset their approach should recognize that, while significant strides can be
made quickly, not everything needs to be done at once. This is a journey in
which organizational capability will evolve over time as new muscle is built.
Additionally, long-term success requires equal parts effort and mind-set. That
said, there are five key principles that increase the odds of success and can
drive impact in as quickly as three months:
1. Get the right leaders in place. The team driving this needs to be
multidisciplinary, so it is important to have strong champions
in analytics, marketing, sales, and payor and managed markets. Additionally, IT
will be critical to success, so you need commitment and partnership from that
team. These leaders should ideally have a clear and shared vision of success
and be thought partners to the working team along the way.
2. Start with the hypotheses; use that to guide data and
analytics, but focus only on what is relevant. Formulate hypotheses and business questions early,
and use that to guide data collection and analytics. Data are always messy to
integrate and no company has perfect data, but it’s important to not let the
perfect be the enemy of good. While truly “garbage” data will create garbage
results (as the old adage goes), advances in data-cleansing tools and
fuzzy-matching approaches mean that a lot can be done with data sets that previously
would have been onerous to work with. It will be important to focus on what
information is needed to answer the questions at hand rather than what’s nice
to have or theoretically optimal. Gathering data on every single metric that
might be needed creates both scope and timing problems.
3. Move quickly to refine the hypotheses and generate
insights through analytics, then iterate. Once
the initial data set has been built, quickly leverage it to pressure-test and
refine the hypotheses on customer segments and to prove or disprove commonly
held beliefs. Make use of the latest tools to get prescriptive insights, and
focus on what can be implemented in the market to capture quick wins, prove the
value, and, most important, learn. Real-world experience is the best way to
tune your algorithms and predictive models. Following a test-and-learn approach
will also help you set the right priorities for which data to fold into your
modeling and how to get your organization to apply the insights.
4. Along the way, lay the groundwork for a new approach. During your analytics journey, determine the
potential from creating a new approach to repeatedly generate meaningful
insights for the business. Implementing a new approach to commercial spend will
drive change only if it is used consistently; while adult behavior can be
changed, repetition is necessary to make it happen. Be purposeful about
creating moments along the way for the organization to practice living the new
approach, so that it becomes second nature. Additionally, identify ways to
integrate the insights generated into the existing work flow of your employees.
In some cases, this is as simple as centrally adjusting the sales force’s call
plan or directing your media-purchasing agency to solve for a different mix. In
other cases, it means adjusting the information the sales force’s
customer-relationship-management system presents or adjusting the content of
selling materials. Finally, focus extra
attention on the culture change that
may be necessary to adopt the new approach as standard business practice.
5. Implement the allocations to capture the value. Developing insights is only part of the answer—it’s
just as important to seed the actions in the market quickly to reap the value.
Best-in-class companies have built flexibility into their business processes to
allow for adjustments all through the year, should the opportunity arise. The
commercial leaders in these companies also tend to be champions of these
initiatives and are great role models for the collaborative and pragmatic mind-set
that it takes for this to be successful. These leaders also ensure the right
participation from both finance and IT—key partners on this journey.
Optimizing your
commercial spend is not easy; it takes investment in new processes,
capabilities, and, in some cases, technology. It also requires a new mentality
about how business decisions are made. The change, however, is worth it:
companies that have fully embraced the new, integrated approach to
commercial-spend optimization have seen improvements in returns of 10 to 25
percent during the first year of implementation. With further tuning, these can
rise even higher.
By Rishi Bhandari, Brian Fox, Laura Moran,
and Ziv Yaar August 2017
http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/get-more-from-your-pharma-commercial-spend-using-advanced-analytics?cid=other-eml-alt-mip-mck-oth-1708&hlkid=9f2492cefed94824818f2493b6873dea&hctky=1627601&hdpid=2c1e5663-0d4d-4d0d-8a04-2acfdcc324e0
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