Ryan Fuller
April 19, 2016
At its most basic, productivity is the amount of value produced divided by the
amount of cost (or time) required to do so. And while this equation seems
simple enough on the surface, the strategies for optimizing it have evolved
dramatically over the last two decades. Technology has enabled massive personal
productivity gains computers, spreadsheets, email, and other advances have
made it possible for a knowledge worker to seemingly produce more in a day then
was previously possible in a year. It s tempting to conclude that, if
individuals are able to perform their work much better and faster, overall
productivity must be soaring.
And yet there s a problem. U.S. government data suggests overall labor
productivity has only grown 1-2% per year during the tech boom. With trillions
invested during this time period, that s a hard number to reconcile. My strong
hypothesis is that we re focusing on the wrong kind of productivity and, in
turn, the wrong kind of management. It turns out that enterprise productivity
is different than just the sum of personal productivity. This difference
matters. A lot. And an example from my company, VoloMetrix (now part of
Microsoft), can help illustrate exactly how.
We recently worked with a multi-billion dollar technology firm, where the
majority of the company s revenue comes through a large ecosystem of partners
(e.g., resellers, manufacturers, etc.). They have enjoyed strong growth for
many years, but recently made the decision to put more emphasis on growing
profitably rather than just growing. One of the things that they wanted to
understand was the cost of managing their partner ecosystem they had a
hypothesis that there might be ways to do so more efficiently.
They began by providing us a list of around 700 employees that they believed
represented the population of partner-facing roles across their organization.
They asked us to confirm that these employees were indeed partner-facing, and
to let them know if they missed anyone. (For a bit of background context, we
performed data mining on anonymized email and calendar header data, in
combination with HR and customer relationship management data, to build a
robust factbase of just how much time is spent in direct communication with
each partner, by each team, across the entire organization, among other
things.)
It turns out they were a bit off on the employee population involved. In
reality, around 7,000 employees directly interacted with partners for at least
one hour per week over the course of a year. Roughly 2,000,000 hours of time
were spent in these direct partner interactions (emails, meetings). This
equates to approximately $200M of employee time per year, which doesn t even
include any of the internal discussions or preparations.
This is a big number. Worse, it s an order of magnitude bigger then what
company management had expected. In reality, they had no clue how their
employees were collectively spending their time with regard to one of their
single most important revenue-generating activities.
That said, big isn t necessarily bad given how much revenue comes through their
partners. However, we were then able to look for correlations between time
invested in each partner and that partner s success. We looked at growth, total
bookings, strategic value and other outcome measures segmented by geography,
partner type and length of relationship. Using some natural language processing
we were also able to derive a decent estimate of the topics of each interaction
(e.g., sales-related, product-related, program-related, etc.). The hope was
that the time and cost invested in each partner paid dividends.
It didn t.
To be sure, some correlations with partner value creation emerged, but only
when we got quite specific about the type of partner and the type of
communication. After much iteration, the general conclusion was that at least
50% of the total time employees spent engaging with these partners had no
correlation with enterprise value. That s one million hours annually (not
including internal prep time), or the equivalent of 500 full-time workers.
Every day, they were dedicated to activities that appeared to be at best
redundant or potentially even value destroying, with multiple employees from
multiple teams engaging with the same people at the same partners in an
uncoordinated way.
But here s the rub: most of the employees at the company were doing their jobs
and, by all accounts, doing them well. From an individual management
perspective, they were highly productive, but from an organizational
perspective their productivity was effectively zero or negative. Management had
an inkling that they might be missing opportunities, but they underestimated
the problem by an order of magnitude. This is a prime example of why measuring
individual productivity on its own is insufficient.
To really improve productivity and to be honest about what it means you
first have to gain a level of organizational self-awareness to understand what
work actually drives value at your company, and then direct employees towards
these tasks. This is pretty straightforward for manual work (e.g., assembly
lines), but extremely complex when it comes to knowledge work. Knowledge work
gets done through networks of individuals working together with frequently
changing goals and varying degrees of context. The many great
productivity-enhancing technologies referenced earlier (email and other
technologies) actually contribute to making this task even more complex, as
they ve enabled companies to become ever more distributed, global, and
real-time. How can you truly understand what your employees are doing at an
enterprise level if you rely on a set of backward-looking, inflexible financial
and operating metrics delivered to you weekly, if not monthly or quarterly?
To be clear, individual productivity is often a worthy goal. But leaders also
need to stop thinking about productivity at an individual or even team
level. It s time to start shifting to an organizational mindset and set of
tools that can provide full visibility into what work is actually getting done
in aggregate, and where it does (or doesn t) create value, however you define
it. Approaching management in this way will require new approaches and won t
always be easy, but the technology firm example indicates that deep
understanding of what s really happening from day-to-day will likely have broad
impacts on corporate structures, processes, and teams (indeed, the company in
question is rethinking all of this).
The ultimate albeit difficult-to-achieve goal is a large organization in
which all knowledge workers have full context, tools, and support to focus
their time on the biggest value drivers of the business without being bogged
down by overhead and bureaucracy. That s exciting not only for the actual
productivity gains that will result at an organizational level, but also for
each employee who will finally have a clear sense of what matters and how to be
successful. A company will know that they ve achieved this state when personal
productivity gains actually do add up to enterprise ones.
Ryan Fuller was the CEO and co-founder of VoloMetrix, a leading people
analytics company acquired by Microsoft in 2015. Within Microsoft, Ryan leads a
business unit focused on making organizational analytics capabilities broadly
available. Previously he was a management consultant at Bain & Company.