The document discusses the challenges faced by sales executives, including limited customer engagement, reliance on legacy products, and lengthy deal cycles. It emphasizes the role of sales operations in transforming productivity through data-driven selling models and advanced analytics, leading to significant reductions in seller time and costs. Additionally, it highlights the need for companies to enhance their analytics capabilities to better predict customer behavior and drive sales growth.

1. Last Modified 10/7/2016 4:13 PM Eastern Standard Time
Printed 9/28/2016 12:04 PM Pacific Standard Time
WORKING DRAFT
Breakthrough
Sales
Productivity
Improvement through
Advanced Analytics
October 2016
San Francisco
Dreamforce

2. Sales executives face a common set of challenges that should be
addressed by Sales Operations leaders
Sellers don’t spend
enough time
with customers
The organization
keeps selling
legacy products,
not new
products
Sellers are
giving away
too much
margin
Deals take
longer than
they should
There are no
universal metrics
to review
?

3. Sales Operations can play a significant role in enabling
transformation and driving sales productivity improvement
Local teams with inconsistent selling models
based largely on past experience
Globally consistent selling approach with data-
driven selling models to optimize productivity
Sales
Operations
▪ Drives top-line growth – 30% reduction in seller time, 40% reduction in deal cycle time
▪ Reduces opex – 30% decrease in sales operations cost, 2-3% annual productivity growth

4. Opportunity for Sales Operations to harness the power of advanced
analytics capabilities to drive continuous improvement
SALES SUPPORT CAPABILITY
Analytics
capability Strategy & planning
Sales
enablement
Performance
management
Bid/Project
management
Intelligent
Automation
Data-driven
Insights
Predictive
Models
Optimize account
coverage and target-
setting
Personalized seller
dashboards
Segment deal flow
based on deal &
customer characteristics
Redefine hiring and
assessments based on
data-driven criteria
Identify granular
growth opportunities
Seller incentives based
on deal-level pricing
dynamics
Optimize deal dis-
counting based on his-
torical “cluster analysis”
Predict accounts at
risk of churn
Calibrate technical
sales performance
based on rep meta-
data
Use A/B testing to
predict optimal pricing
in digital channels
Improve value
propositions with “next
product to buy”

5. Expand the range of data beyond basic
customer data…
... and use advanced analytics to identify
predictive features in the data
Most companies today use analytics to predict customer churn, but
are only scratching the surface of their data
PREDICT CHURN
Basic customer data
Product
install base
Current
service
contract
Contract
renewal
date
Expanded data
Service contract history
Product purchase history
Pricing and payments
Web portal usage
Service ticket history
Call center / IVR records
Thresholds
Aggregations
Deviations
Trends

6. Machine learning is opening new avenues of granular insight and
prediction, greatly improving customer focus
PREDICT CHURN
10+% monthly churn
2-10% monthly churn
<2% monthly churn
Build decision tree using advanced machine learning platform to identify extremely high
churn microsegments…
… with actionable churn
reasons
Zip code Ziggy: Made service
contact last month, heavy TV
viewer, in a competitive market
Service issue Susan: Less
engaged in TV, declining BB
usage, called with a service
problem
Discount hunting Deidre:
Promotional pricing ended,
light TV viewer, called to ask for
a discount
Moving Matthew: Called to
make a move but does not have
a web account

7. 53
Slow growers
37
Fast Growers
Most B2B companies do not effectively leverage
advanced analytics in sales
By growth; N = 1,013 companies
Companies rating use of analytics in sales as “extremely effective” or “moderately effective”
% of companies
43
Overall
SOURCE: McKinsey & Company Sales Growth 2015 Survey

8. Why companies fail to capitalize on their analytics efforts
Building the
analytics
talent engine
Foundational
IT infra-
structure
Consistent
data models
Next-
generation
selling talent
Embedding
into sales
workflows

9. Analytics agenda for Sales Operations leaders
Rethink your talent profiles
both inside Sales Ops and the field
Re-imagine sales processes
through a digital and analytics lens
Simplify your IT roadmaps
with a bias for impact “out of the box”
Invest ahead in defining your data model
Aspire to “just-in-time”
insight delivery to sellers

10. Stay connected
Brian Selby
Expert Partner & Leader of McKinsey’s Sales Operations Practice
McKinsey on Marketing & Sales
Brian_Selby@mckinsey.com
Brian Selby