Cloud9 treasure-hunting-in-your-salesforece-data

© 2012 Cloud9 Analytics, Inc. All Rights Reserved.


Intelligent Sales Forecasting

Treasure Hunting in Your Salesforce Data Swayne Hill Co-Founder & Senior VP, Cloud9


We Don’t Need more Dashboards,

We Just Need Answers


About Me

•  Senior VP Sales & Field Operations, Cloud9 •  18 Years in Business Intelligence •  Built and Managed Sales Teams for Cognos

- $50M to $1B •  Co-Founded Cloud9, 2007

Blog: DataDrivenSalesManagement.com Twitter: @DataDrivenSales


Treasure Hunting in Your Own Back Yard There’s Gold in Your Sales Data, You Just Need a Way to Find it


Recent Salesforce Reporting Enhancements

ü Bucketing Allows customized grouping of report records (e.g., deals that are Small, Medium, and Large as defined by opportunity value).

ü Cross Filtering Allows reports to be filtered by a field from a related object (e.g., contacts from accounts that don’t have a close opportunity—the opportunities are the “related object” Opportunities don’t appear in the report but we use a field from Opportunities as a filter).

ü Joined Report Format Allows two or more reports grouped by a common field (e.g., Opportunities report and Cases report grouped by Account Type—Customer, Prospect, etc.)

ü Higher limits on Analytic Snapshots… Scheduled Reports and Dashboards, and Dynamic Dashboards


…But Resist the Urge to Build Yet More Dashboards


How do I know if it’s good or bad

Good? …or Bad?

The Real Problem: How do we know…


Two of My Favorite Business Intelligence Tools



Three Techniques 1. Correlation 2. Modeling 3. Threading


Correlation

1 2

3

4 •  By Correlating Stage Days to Win Rate, we learned that spending MORE time in Stage2 was actually a good thing


Correlation: ‘Stalled’ Risk Filter

•  Action – ‘Stalled Risk’ risk filter, driven from new benchmarks •  Result – Early warning for sales managers on Stalled Deals,

better coaching, increased Win Rate by 30%


Modeling (on/off Model)

1.  Separate ‘Probability’ into two distinct components – ‘Forecast Probability’ (standard Salesforce Probability) and ‘Win Probability’ (how close is the deal to our sweet spot)

2.  Create a Model for weighting the qualification questions to assign the ‘Win Probability’

3.  Use the Model to create benchmarks for driving Risk Filters, support manage-by-exception


Modeling: In Practice

148

145

100

34

18

14

88

96

85

60

21

9

0 20 40 60 80 100 120 140 160

01-Qualify Prospect

02-Secure Sponsorship

03-Secure DG Preference

04-Negotiate Business Terms

05-Negotiate Legal Terms

06-Pending Approval

Average Days to Win

Have not met with VP Sales Have met with VP Sales

40%

60%

80%

100%

Oct Nov Dec Jan Feb Mar

Forecast Attainment

Forecast Attainment

0%

20%

40%

60%

80%


Deals Slippage

# Of Opps Slipped From Forecast


Modeling: ‘Met With VP Sales’ Risk Filter

•  Action – ‘Met With Sales VP’ risk filter •  Result – Early warning for sales managers on rep engagement at

a key decision-maker level, better coaching, improved forecast accuracy by 50%


Threading: Sales Pipeline Velocity

Deals

Maximum number of deals set to close in

the period

• Lead Flow & Conversion Rates

• Uncovered Territories, Rep Turnover

• Undocumented Deals • …

Effected by...

Win Rate

Total Closed/Won deals in the period relative to Deals

• Loss rates • Stage-based conversions

• Stalled deals by stage • Deferral rate, slippage • …

Deals Size

Average value of Closed/Won deals in

the period

• Product mix • Discount levels, tendencies

• Rep portfolio (SMB/Enterprise)

• …

Selling Time

Average duration for Closed/Won deals in

the period

• Stage duration, stage movement

• Expired deals, data quality

• Repeated deferrals • …

# of Deals * Win Rate * Average Deal Size

Average Selling Time (Days)

Pipeline Velocity =


Threading: In Practice

• Chart the Variance (current month – benchmark, e.g. rolling 6-mo average)

• Watch for changes/trends in Drivers for clues to Root Cause


Threading: In Practice

•  Stage duration, stage movement

•  Expired deals, data quality •  Repeated deferrals •  Late stage “new” deals showing

up •  Close dates improperly set •  Rep training, tenure

41% 35%

49% 52%

70% 69%

0%

10%

20%

30%

40%

50%

60%

70%

80%


% of Rev Subject to 3+ Close Date Slips

We looked at… Found: Carcass Carriers!


Threading: ‘# of Pushed Close Dates’ Risk Filter

•  Action – ‘# of Pushes’ risk filter, driven from new benchmarks •  Result – Early warning for sales managers, better coaching,

reduced deal slippage by 3X and re-established Velocity


1. Correlation 2. Modeling 3. Threading

You Too Will Find Gold in Your Sales Data


Cloud9’s Intelligent Sales Forecasting

Sales Forecasting

Business Intelligence

Intelligent Sales

Forecasting

Collect Forecast

Manage Change

Isolate Risk

Coach & Course-Correct

Deliver on the Forecast

Unify Sales Forecasting and

Pipeline Management to

improve accountability

Embedded Business

Intelligence provides Guidance and Risk visibility

for improving Sales Team productivity


Driving Customer Value

2010 2011

We increased revenue by $1.6M/year by delivering Cloud9 to the management team. Dave Fitzgerald, EVP - Brainshark

Salesforce.com is perfect for managing accounts and opportunities. We use Cloud9 to manage our people. Dan Solito, VP Global Sales Operations - Avaya

We increased year-over-year win rates by 17% after deploying Cloud9. Scott Johnson, VP – Stanley, Black & Decker

2012



Intelligent Sales Forecasting

Treasure Hunting in Your Salesforce Data Swayne Hill Co-Founder & Senior VP, Cloud9