Sales Forecasting Capabilities
Transcript of Sales Forecasting Capabilities
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To some contemporary B2B sales leaders, the idea of investing in
a sales analytics solution let alone understanding how itactually works represents an unwelcome detour from the daily
business of closing deals. Thats a good thing for their Best-in-
Class competitors. This Research Report defines nine
competencies that accrue to analytics adopters, and drive better
business results as well.
WHY ANALYTICS? NINE MUST-HAVE B2B
SALES FORECASTING COMPETENCIESAugust, 2015
Peter Ostrow, VP/Research Group Director,
Customer Management, Sales Effectiveness
Report Highlights
Sales analytics users
report 10% more
reps achieving
quota, and 10%
better customer
retention rates
Best-in-Class
companies are 13%
more likely to apply
weighted variables
to their sales
forecast
Are you able to walk
away from bad
deals? Analytics-
enabled sales leaders
are twice as skilled
in this as non-users
Users of sales
analytics are 2.2
times more
competent at
delivering trusted
forecasts to the rest
of the company
p2 p3 p5 p9
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
2We already know fromSales Analytics: The Path to Better, Faster
Forecasting(August 2015) that companies deploying sales
analytics solutions perform better than non-adopters. Around
both current key performance indicators (KPIs) such as team
attainment of quota and customer retention, as well as stronger
year-over-year trends such as growing total company revenue
and CRM adoption, these organizations report better business
outcomes that help justify the analytics investment itself and
strengthen the enterprise as a whole. Let's take a look under the
hood of this technology stack, in order to understand howanalytics products really work, and the impact they have on both
tactical and big-picture daily business activities.
More Mr. Spock Less Captain Kirk
The most important tenet to consider, when using sales
analytics tools to improve on forecasting accuracy, is how the
solution helps sales leaders and corporate executives mitigate
the predictably subjective nature of the sales forecasting process
itself. Business-to-business (B2B) sales professionals are humanbeings, deliberately motivated by the dual emotions of greed
and fear, represented by the prospects of accelerated
commissions when successful, and job insecurity when they miss
their number. It naturally follows that when formally
introducing their closely guarded revenue opportunities to their
managers, generally by entering deals into the customer
relationship management (CRM) platform, salespeople will
portray the facts of their pipeline in a way that supports and
protects themselves first, and the company's best interests
second. Thus, at the very beginning of many identified
opportunity journeys within the sales forecast, the validity of the
facts being used to determine business outcomes is already
questionable.
How do
enterprises best
mitigate the
predictably
subjective nature
of the sales
forecasting
process itself?
Sector Definition: Sales
Analytics
Sales analytics refers to
technology enablers deployed
by organizations to better
understand their business.
Beginning with the use of
predictive analytics capabilities
to better inform sales forecasts,
analytics are increasingly being
used to support real-time dealcoaching and sales messaging,
as well.
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
3In order to counteract the understandable human tendencies
around such "sandbagging" or rose-colored views, companies
invest in analytics solutions in order to reduce subjectivity and
give sales leaders more clarity around which data-driven factors
are more reliable in predicting how sales opportunities will turn
out. Most analytics products include a predictive capability,
which analyzes historical sales activity in order to identify
commonalities within current deals that can signal more or less
likely outcomes. For example, a sales rep enters a CRM
opportunity to sell red widgets to a biomedical company inTexas, anticipating a $30,000 deal that will close on November 1.
As implemented and instructed by the sales operations team,
the analytics solution recognizes that: red widget deals
traditionally close for smaller deal sizes than green ones,
biomedical companies tend to make decisions faster than the
average of buyers within other industries, regulatory barriers in
Texas tend to slow deals down, and that this particular sales rep
tends to under-estimate the length of their sales cycle.
Adjustments to the original CRM entry are made accordingly,and the deal is entered into the forecast from a more objective
perspective.
Once is Not Enough
Creating a better, fact-driven understanding of what a B2B sales
deal looks like from the startis just a start. The Best-in-Class
companies within this Aberdeen research study (sidebar) are
13% more likely than All Others (88% vs. 78%) to indicate that
they continue to apply data-oriented weighting to sales deals at
one or more points throughout their lifecycle. This is where the
real power of predictive analytics creates value, in real-time, for
organizations that recognize the importance of accurate
forecasting and the impact that poor visibility into the sales
pipeline can have on overall company performance. In Figure 1,
The Sales Effectiveness Best-in-
Class Defined
In March through May 2015,
Aberdeen surveyed 322 end-user
organizations to understand their
sales effectiveness best practices.
The performance metrics used to
define the Best-in-Class (top 20%),
Industry Average (middle 50%),
and Laggard (bottom 30%) among
these sales teams are:
72% of sales reps achieving
quota, vs. 48% among Industry
Average and 37% for Laggard
firms
7.4% average year-over-year
increase in average deal size, vs.
a 2.8% increase for the Industry
Average and a 5.7% decline
among Laggard respondents
6.7% average year-over-year
improvement in (reduction of)
the average sales cycle, vs. 0.4%
and 15.8% worsening of (growth
in) cycles, respectively, for
Industry Average and Laggard
res ondents
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
4we look at the first comparison between users of sales analytics
solutions and non-adopters, which reveals a consistently
stronger emphasis on re-applying weighting to current sales
deals.
Figure 1: Forecast Weighting is Like Voting: Do it Early and
Often
Here, we see that a small percentage of organizations actuallyapply forecast weighting to opportunities before the corporate
marketing team assigns them to sales activity, but that analytics
users are more than four times as likely as non-adopters to kick
off the data-driven process at this early stage. They average a
50% more aggressive stance over the next two portions of the
sales cadence and, tellingly, neglect to use weights among only
5% of responding organizations. The takeaway here is that the
research clearly correlates better performance with the use of
these solutions, especially when more logic, and less emotion, isrelied upon to understand throughout the sales cycle what the
most likely outcome of the B2B sales deal will be. Now, let's take
a look at a series of Best-in-Class competencies that more
effectively accrue to those organizations taking such an
approach.
Most analytics
products include a
predictivecapability, which
analyzes historical
sales activity in
order to identify
commonalities
within current deals
that can signal more
or less likely
outcomes.
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
5Using Sales Analytics to Perform Our Jobs Better
No sales manager alive is immune from the traditional, end-of-
month onslaught of pleas and requests from their reps: Come
on, boss, let me offer just a little bit more of a discount, and I
know I'll close the deal"Can we please get the CEO to make a
call to my buyer? "I need to change the Ts and Cs in our
standard contract or I'm going to lose the opportunity. Sound
familiar? Instead of randomly providing support to the reps who
let them win at golf or complimented their outfit, Best-in-Class
sales leaders bestow goal-line resources and make objective go
or no-go decisions based on data not their gut feelings.
Figure 2: Deal-Making Competencies: Better Data Drives
Stronger Opportunity Management
As we see in Figure 2, the same data-based decisions are more
aggressively adopted by analytics-enabled organizations, who
understand that the overall team attainment of quota 66% for
analytics users, compared to 61% for non-adopters depends onmaking decisions that will yield the best outcome for the overall
company, as opposed to for the benefit of those reps who are
most in their face. Of particular note is the third element in
Figure 2, showcasing the fact that sales analytics users are
exactly twice as likely (30% vs. 15%) as other companies to
determine that certain deals are simply not worth pursuing. This
Results are always
better when more
logic, and less
emotion, is relied
upon to understand
throughout the sale
cycle what the mos
likely outcome o
the B2B sales dea
will be
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
6gap significantly exceeds the 45% difference between Best-in-
Class and All Other firms (29% vs. 20%) in "knowing when to hold
em, and when to fold em, a lesson supported by research
published in"The Price is Right"and Other TV Classics that
Teach Us Smarter Selling(July 2015). This study showcases how
price optimization technologies support relatively new trends in
sales management, that emphasize profits, not merely top-line
revenue, in delivering overall business value from the sales
organization.
A second set of core competencies adopted more aggressively by
Best-in-Class companies an average of 63% more often than
under-performers are also more frequently nominated by
analytics users as management strengths, that relate to the all-
important care and feeding of front-line sellers (Figure 3).
As we know from Aberdeen'sSales Performance
Managementresearch, the importance ofretaining top
sales talentcannot be overstated. Considering that the
average cost to replace a B2B sales rep exceeds $29,000,and the time it takes to locate and onboard a full-
capacity seller is 7.3 months, every percentage point of
sales employee turnover that can be avoided matters
significantly to any contemporary sales leader concerned
with the cost of goods sold that includes payroll. The
correlation between the use of sales analytics and a 39%
stronger competency (53% vs. 38%) around this
management imperative is easy to identify: Sales leaders
required to make difficult decisions about their reps
employment are far better empowered when they have
more data in hand about their performance, beyond
simply who made quota. For example, the very definition
of an A-player, traditionally judged by a one-
dimensional view of gross revenue sold, is nowadays
Instead of randomly providing
support to the reps who let
them win at golf or
complimented their outfit,
Best-in-Class sales leadersbestow goal-line resources and
make objective go or no-go
decisions based on data not
their gut feelings.
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
7viewed in the additional context of whether theysell
profitable deals,contribute to the greater good of the
company,or provide a bettercustomer experience.This
represents a whole new world of complexity for sales
leaders, one that is more manageable with the help of
data analytics solutions to provide more context and
color to what previously were black-and-white
managerial approaches.
Figure 3: Human Capital Management Competencies:
Because People Still Buy from People
Much as Figure 1 earlier demonstrates the need to apply
analytics to sales forecasting processes at multiple touch
points through the buyer's journey, so too are these tools
valuable to coaching sales repsaround their messaging
and tactics, in real-time as their deals progress. In
Sheldon Cooper, Sales Whisperer: Applying the Science ofData to the Art of Selling(September 2014), this concept
is explored in depth, and pre-sages the current market
trend toward sales enablement technologies that seek to
provide a sort of "guided selling" or wizard-driven
application that purports to help sales professionals
continuously provide the right message, to the right
Sales analytics user
are twice a
competent as non
adopters i
knowing when t
hold em, and whe
to fold em.
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
8buyer, at the right inflection point during their deal-
specific journey. Think of the all-too-familiar scenario few
of us have failed to experience in selling: a seemingly
interested buyer has suddenly gone dark on us. Most of
the time, we respond with an Orwellian approach I will
work harder and proceed to lob more messages,
across more communications channels, as we grow more
desperate with each day that approaches the forecasted
opportunity-close date, without true progress on the
deal. With better data gleaned from an analytics-enriched, CRM-housed deal library, however, savvy
managers can guide their reps more precisely around the
cause-and-effect relationship between certain types of
"buyer darkness" and various outcomes. They can also
more objectively advise sellers, as indicated above,
around when to walk away from such deals and focus
their energies on more promising opportunities.
Finally, we know from extensiveresearchthat sales
training continues to be an area of both great expense
and significant frustration for modern sales leaders. While
the average company in our study spends $146,417
annually to on-board and train sales staff, less than
two-thirds of survey respondents (63%) indicated high or
very high satisfaction with the services they received from
external training providers. This may in part be due to the
fact that so many of these vendors show up with their
own methodologies and sales pitch practices, but do not
have deep insight into the actual products, deals, and
people within their individual client companies. This is
where in-house analytics come into play: When the sales
operations team is able to correlate different learning
and development curricula to various levels of eventual
sales rep performance, tinkering with training tactics
Every percentage
point of sales
employee turnover
that can be avoided
matters significantlyto any contemporary
sales leader
concerned with the
cost of goods sold
that includes
payroll.
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
9becomes less art, and more science. Similarly, a keen
understanding of which product education, skills
development, or other on-boarding resources resulted in
more or less profitable deal management, can further
influence how the overall sales team is empowered by a
data-driven knowledge set.
Creating a Win-Win fo r All Constituencies
Lets not forget that the act of B2B selling fulfills not only the
needs of its practitioners and managers, but also of thecustomers that ultimately fund all of this activity. In Figure 4, we
note a final set of competencies that sales analytics adopters
more frequently nominated as strong or very strong current
commercial abilities.
Figure 4: Commercial Competencies: Achieving More than
Sales Quota
The first two items speak to lessons learned inThe 21st Century
Buying Experience: Say Farewell to the Sales Cycle(July 2014):
Modern buyers of B2B goods and services are far more educated
than ever before, and demand an enhanced degree of
personalization and experience from the messages and
messengers to which they are exposed. Sales analytics solutions
With better data gleaned fro
an analytics-enriched, CR
housed deal library, sav
managers can guide their re
more precisely around t
cause-and-effect relationsh
between certain types of "buy
darkness" and variooutcome
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
10help quota-carriers hone in on the specific product features that
are most likely to interest their prospects, based on previous
behavior and purchases from both their target buyer and similar
organizations in the past. In addition to this type of formal,
historical audit trail that can point toward future behavior, many
analytics platforms can also collect unstructured data such as
social media content, which can further guide a seller toward an
understanding of their buyer's motivations, needs, and
concerns. There's nothing like that feeling when a prospective
customer says, "Wow, it's like you read my mind!"
Finally, 42% of Best-in-Class companies indicate high or very
high proficiency in Delivery of a sales forecast perceived as
accurate and trustworthy by senior management and the
company as a whole,compared with 22% of Industry Average
companies and only 17% among Laggard organizations.
Analytics-enabled sales teams are over three times more likely
than non-adopters to indicate the same (44% vs. 14%) because,
simply, they are providing a more objective and accurate set of
data points that the C-suite and other line-of-business leaders
need to run their respective departments. Why is this important?
After an eternity of delivering widely criticized and mistrusted
sales forecasts, modern sales leaders and also individual
contributors are finally being held accountable for providing
believable guidance to the rest of the enterprise around their
pending business results. Indeed, we know fromWhat Do You
Mean, "There's a Debit on My Commission Check"?(July 2015)
that compensation for both personas is increasingly being
influenced by sales forecast accuracy. After all, the fastest way to
change the behavior of sales professionals is through their take-
home pay.
When the sales operations team
is able to correlate different
learning and development
curricula to various levels of
eventual sales rep performance,
tinkering with training tactics
becomes less art, and more
science.
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
11Conclusion and Recomm endations
Once considered little more than a joke by the rest of the
enterprise, the sales forecast is maturing into a crucial tool with
which contemporary business leaders run their business more
effectively. By minimizing the impact of flawed human emotions,
and instead focusing on lessons that can be learned from the
seemingly endless flow of data about their customers and
prospects, Best-in-Class companies are turning to the power of
sales analytics solutions to drive better activities, results, and
customer successes. The research in this study proves the value
of focusing these analytics solutions around the real-life, daily
activities of sales deal management in the B2B space, as well as
the challenges of motivating and managing high-performance
quota-carriers. If your organization is considering an analytics
solution, be sure that it offers:
Integration with the CRM, so that a single platform
interface is demanded of your sellers, and so that
historical data about prospects and customers can bemined for insights into how to manage current and future
deals in the pipeline.
Real-time analysis of existing sales deals, based on past
business results, that can guide sellers and managers
alike toward the smartest and most profitable behaviors.
The opportunity to run if-then scenarios, e.g. if we
discount this product by 5% more, how does that change
the likelihood that the buyer will sign the contract withinthe current month?"
Data to help sales leaders manage not only opportunities,
but people. Your reps will respond much better to
coaching that is based on the tribal knowledge of the
organization, as opposed to "when I carried a bag, this is
how I handled things."
There's nothing lik
that feeling when
prospectiv
customer sayWow, it's like yo
read my mind
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Why Analytics? Nine Must-Have B2B Sales Forecasting Competencies
12For more information on this or other research topics, please visitwww.aberdeen.com.
Related Research
Sales Effectiveness 2015: How in the World Are We
Going to Hit Our Number?; May 2015
Once is Not Enough: Why Sales Training
Reinforcement is a Must-Have;May 2015
Flash Forward: Remember When we Called it
"Predictive" Analytics?; April 2015
Six Ways to Guarantee You're a Social Selling
Failure;April 2015Making Sales Enablement Work: Nine Must-Haves
for the Modern Sales Ops Leader;March 2015
Beyond the Commission: Will You Stay Ahead of
the SPM Maturity Curve?; November 2014
Sheldon Cooper, Sales Whisperer: Applying the
Science of Data to the Art of Selling;September
2014
Would You Buy from a 20th-Century Sales Rep?;
August 2014
CRM + Sales Leadership: Building a Platformfor More "A" Players;February 2015
Author: Peter Ostrow, VP/Research Group Director; Customer Management, Sales Effectiveness
About Aberdeen Group
Since 1988, Aberdeen Group has published research that helps businesses worldwide improve their performance.
Our analysts derive fact-based, vendor-agnostic insights from a proprietary analytical framework, which identifies
Best-in-Class organizations from primary research conducted with industry practitioners. The resulting research
content is used by hundreds of thousands of business professionals to drive smarter decision-making and improve
business strategy. Aberdeen Group is headquartered in Boston, MA.
This document is the result of primary research performed by Aberdeen Group and represents the best analysis
available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted
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