Driving accountability and
predictability is key for any sales
organization. So knowing as early as
possible whether sellers and managers
will meet their sales goals allows
management to be pro-active and turn the
situation around before it's too late.
That's why we're excited to introduce
native bottoms-up and AI driven sales
forecasting capabilities that will help
you improve pipeline accountability and
drive more revenue. First, let's walk
through how I, as a sales manager or
director, can extract the information I need.
The forecast grid provides full
visibility into the roll ups and
metrics that impact my business at
every level of the hierarchy. I can
monitor exactly when my team is calling
across all layers of management. In this
example, I could see that my whole
organization is only meeting 70% of our
current monthly target. Diving deeper
into the hierarchy the problem stems
from one specific location that is
completely missing its quota. In fact, the
subjective commitment from the team
seems to be at odds with the objective AI
driven prediction. Leveraging
historical data and existing pipeline
signals, a day one prediction of the
forecast is provided at each level of
the hierarchy. Extra details explain the
underlying drivers for the projection. In
this case, although it seems that the new
revenue will come from deals not yet in
the CRM, the team seems to be
over-estimating how much of their open
opportunities they will actually close.
Let's see what deals my team may be
falsely committing to. Selecting the row,
a grid appears with all deals that
contributed to the committed value. When
sellers are submitting their forecasts
they get a very similar experience,
except that they only see their data and
although they can modify their forecast
by quickly updating the underlying deals
in the grid, a visual Kanban provides a
much more intuitive and visual
experience for managing their pipeline. A
simple drag-and-drop ensures that the
right deals are in the
correct forecast category. Back in the
manager view, I remember that the
forecast last week was not significantly
off. What happened? The flow chart gives
visibility into what has changed between
those two periods of time. I can see the
exact opportunities that were moved and
the granular changes that impacted the
forecast. In this example, it seems that
the team may have been on track had they
not lost a very big deal during the last
week. Going back to the grid, they may
have tried compensating for that loss by
being overly optimistic on a few key
deals. As a manager, I do not want to
bring changes to the underlying records
but I am not ready to sign off on the
team's commitment. I can therefore bring
an adjustment at an aggregate level,
leaving a note with reasons for the
override, and just like that my whole
team can be held accountable to their
forecast and I can drive accuracy.
Now how did we build this forecast? We
recognize that no two organizations are
the same. While some may want to forecast
based on the reporting hierarchy, others
may prefer to use a territory hierarchy
both types are supported.
Once a hierarchy type is chosen, it's important
to select the person or territory that
sits at the top of the hierarchy. Usually
this is the sales director or Chief
Operating Officer. Determine the period
breakdown for your forecast and select
who will get access to the forecast.
Although most organizations share a
similar shell of a sales process, there
are small differences that are important
and need to be accounted for. In the
layout step, I can choose the
out-of-the-box forecast category to
bucketize my opportunities or choose
any other option set that meets my needs.
I can control the visibility and the
order of the columns and even create
brand-new columns to say calculate the
gap to quota or
even the pipeline coverage ratio. Finally,
it's time to upload quotas for my team
so that they can benchmark their
performance. And just like that I am done.
With the new bottoms-up sales
forecasting capabilities in Microsoft
Dynamics 365, you'll be able to model
your unique forecast requirements,
drive more predictability and ultimately
win more sales.
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