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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