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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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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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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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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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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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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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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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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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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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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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

([email protected])

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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Sales Forecasting Capabilities

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