Surface Better Insight from Your Sales Pipeline Data

SURFACE BETTER INSIGHT FROMYOUR SALES PIPELINE DATACapture, Calculate and TrackHard-To-Find Key Leading Indicators

• Introduction• Leading, Lagging (and Lounging?) Indicators• Leading Indicators – What’s Missing?• The Importance of Data Staging• Case Study• How to Derive and Maintain the Missing Indicators• Marketing Data– Leading Indicators Have Leading Indicators

• Dashboard and Analysis Tips• Senturus Overview• Additional Resources

Agenda

Copyright 2016 Senturus, Inc. All Rights Reserved.

Introduction: Presenters


Greg HerreraPresident and Co-

FounderSenturus, Inc.

Jim FrazierVP of Client Solutions

Senturus, Inc.

Presentation Slide Deck on www.senturus.com

RESOURCE LIBRARYAn extensive, free library of past webinars,

demonstrations, whitepapers, presentations,helpful hints and more.


This slide deck is from the webinar: Surface Better Insight From Your Sales Pipeline Data

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SURFACE BETTER INSIGHT FROMYOUR SALES PIPELINE DATACapture, Calculate and TrackHard-To-Find Key Leading Indicators

Leading, Lagging and Lounging Indicators


• Lagging Indicators– Measure performance that has already happened– Financial Statements: Revenue, Gross Margin, Profit

• Leading Indicators– Indicate the direction of in-course performance

• Lounging Indicators– “… will be leaving the company to spend more time

with family”

• Leading indicators in the sales and marketing processes– Those influencing the direction of revenue, gross

profit and organizational hygiene• Session will be most valuable to organizations

having sales teams entering and tracking multi-stage sales cycles in an opportunity pipeline database such as Salesforce.com• Examples will use Tableau for data visualization• Techniques discussed can apply to other pipeline

tracking and visualization systems

Scope of Discussion


Leading Indicators – What’s Missing?

Indicator

Available straight from CRM system

Available from staging database

Available from Data Warehouse (Data Mart)

Win Rate Y Y YAverage Deal Size Y Y YTime to Close Y Y YPipeline Growth (PG) Missing Y Y PG from New Deals Missing Y Y PG from Existing Deals

Missing Y Y

PG from Stage Change

Missing Y Y

Conversion Rate for Each Stage

Missing Y Y

Interaction Growth Missing Y Y


4 ways to increase revenue1. Pipeline growth = Increase the number or value of

deals in the pipeline2. Win rate = Improve success rate3. Average deal size = Close bigger deals4. Time to close = Increase the velocity of the deals

moving through the pipeline – i.e., shorten the sales cycle

3 ways to achieve pipeline growth5. New deals = Enter the pipeline6. Value change = Existing pipeline deals change

size7. Stage change= Existing deals change stage

Leading Indicators of Revenue


Contextual Attributes – What’s Missing?

Attribute

Available Straight from CRM System

Available from Staging Database

Available from Data Warehouse (Data Mart)

New vs Existing Account

Manually Entered Possibly Y

New vs Existing LOB Missing Y YRecency of Interactions

SFDC Activity only

If integrated Y

Frequency of Interactions

SFDC Activity only

If integrated Y

Clusters of Interactions

Missing Y Y

Full Customer Attributes

Missing Missing Y

Full Product Attributes

Missing Missing Y

Product Mix Missing Missing YPredictive Analytics Foundation

Missing Missing YCopyright 2016 Senturus, Inc. All Rights Reserved.

The conversion rate (probability) for each stage should be calculated from empirical data contained in historical pipeline snapshots and historical wins

• Marketing automation systems (Pardot, Marketo) track interactions with behavior scores

• Integrates to CRM systems, such as Salesforce, that update contact’s records with behavior score -- more interactions lead to more new deals entering the pipeline

Leading Indicators Have Leading Indicators


Stage ProbabilityEarly 10%Middle 20%Strong 50%Favored 70%Closing 95%Closed - Won 100%Closed - Lost 0%


Sales Pipelines are Current Snapshots

Opportunity Amt Stage Prob Close Date

Age

Bike Universe $50k Early 20% 2016-12-25

23

The Cracker Box $40k Middle 40% 2017-02-25

35

Racing Bike Outlet $35k Favored 60% 2016-11-01

20

Northwind Traders $65k Verbal 70% 2016-11-25

84

Rugged Bikes $60k Closing 90% 2016-12-31

407

Grand Cycle Store $55k Closed Won

100% 2016-10-13

365

Revolution Cycles $40k Closed Lost

0% 2016-07-08

293

As of now, the pipeline says this:

• In Salesforce and most other CRM systems, sales pipeline tracked in an opportunity table, which maintains only the current values

• Win rate, average deal size and time to close are calculations of closed deals (date and value of the transaction is known)

• The date and value of pipeline growth transactions is not known from this table

14Senturus, Inc. – Proprietary and Confidential

Contrast: Pipeline Balances Monthly Snapshot

01/01

/12

05/01

/12

09/01

/12

01/01

/13

05/01

/13

09/01

/13

01/01

/14

05/01

/14

09/01

/14

01/01

/15

05/01

/15

09/01

/15

01/01

/16

05/01

/16

09/01

/160

1,000,0002,000,0003,000,0004,000,0005,000,0006,000,0007,000,000

Suspect Qualification EarlyMiddle Strong Closing

Contrast: Pipeline Growth from Same Pipeline!



Sales Pipelines are Current Snapshots

Opportunity Amt Stage Prob Close Date

Age


23

The Cracker Box $40k Middle 40% 2017-02-25

35

Racing Bike Outlet $35k Favored 60% 2016-11-01

20

Northwind Traders $65k Verbal 70% 2016-11-25

84

Rugged Bikes $60k Closing 90% 2016-12-31

407

Grand Cycle Store $55k Closed Won

100% 2016-10-13

365

Revolution Cycles $40k Closed Lost

0% 2016-12-08

293

As of now, the pipeline says this:

• In Salesforce and most other CRM systems, the sales pipeline is tracked in an opportunity table, which maintains only the current values

• Win rate, average deal size and time to close are calculations of closed deals (date and value of transaction is known

• The date and value of pipeline growth transactions is not known from this table


1-Derive Transactions from Snapshot Differences

As of Opportunity Amt Stage Prob

Close Date

2016-10-12


Pipeline growth transaction is derived by finding the values that changed from one balance snapshot to the next

Yesterday’s Snapshot:

As of Opportunity Amt Stage Prob

Close Date

2016-10-13


Today’s Snapshot:

Growth Date Opportunity Growth

Type

Old Val

New Val

Growth Amt

2016-10-13

Bike Universe Value Change

$20k $50k $30k

Derived Pipeline Growth Transaction:


2-Derive Transactions from SFDC Opp History

Create Date Opportunity Amt Stage Prob

Close Date

2016-10-12 8am

Bike Universe

$20k Early 20% 2016-12-25

2016-10-12 4pm

Bike Universe

$30k Early 20% 2016-12-25

2016-10-13 9am

Bike Universe

$50k Early 20% 2016-12-25

Note: Salesforce opportunity history table captures changes to only six pipeline fields, it doesn’t capture changes to custom fields.

Growth DateOpportunity

Growth Type

Old Val

New Val

Growth Amt

2016-10-12 8am

Bike Universe

New Deal NULL $20k $20k

2016-10-12 4pm

Bike Universe

Value Change

$20k $30k $10k

2016-10-13 9am

Bike Universe

Value Change

$30k $50k $20k

Derived Pipeline Growth Transactions:


3-Derive Transactions from SFDC Opportunity Field History

Note: Salesforce opportunity field history table can capture changes to any field, including custom fields, but it doesn’t capture the field value for new records

Create Date Opportunity

FieldOld Val

New Val

Growth Amt

2016-10-12 4pm

Bike Universe

Amount $20k $30k $10k

2016-10-13 9am

Bike Universe

Amount $30k $50k $20k

Growth DateOpportunity

Growth Type

Old Val

New Val

Growth Amt

2016-10-12 8am

Bike Universe

New Deal NULL $20k $20k

2016-10-12 4pm

Bike Universe

Value Change

$20k $30k $10k

2016-10-13 9am

Bike Universe

Value Change

$30k $50k $20k

Derived Pipeline Growth Transactions:

• Marketing automation systems (e.g. Marketo) integrate with CRM Systems (Salesforce) and push Behavior Score balances several times per day• Derivation technique 1 can work with any

combination of marketing and CRM systems to derive Behavior Score transactions• Derivation technique 3 can be configured in

Salesforce– Turn on history tracking on the Behavior Score

field in both the Lead table and the Contact table

Use Similar Techniques for Interaction Trends


Behavior Clusters as Leading Indicators of Purchase


To view the FREE video recording of the presentation and download this deck, go to: http://www.senturus.com/resources/surface-better-insight-from-your-sales-pipeline-data

/

The Senturus comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website:

http://www.senturus.com/resources/

Hear the Recording








Relative Time, Pipeline Seasonality, Rolling Averages

DASHBOARD AND ANALYSIS TIPS

• Good dashboard practice says to use auto-refresh relative time periods instead of having to specify date ranges

• Leading indicators enable in-course corrections, so frequent (daily) updates are valuable

• Many sales organizations experience quarter-based seasonality in their pipeline growth– Month 1 and month 2 of the quarter: Sales reps focus on

pipeline growth– Month 3 of the quarter: Sales reps focus on closing

• For these organizations, look at pipeline growth across 91-day periods (quarter of a year)– This technique enables daily dashboard updates and

accommodates seasonality

Dashboard and Analysis Tips


When calculating the average pipeline growth over a time period, you must accommodate the case where not every day in the period has a pipeline growth record. In this example, we’re looking at a seven day period and five of those days have no records.

Considerations when Averaging Pipe Growth (1)


Growth Date

Growth

Amount

10/2/16 $3510/5/16 $35

Daily Avg for period 10/1 to 10/7

$35

Wrong

Dates with nulls still do not work



Growth Date

Growth

Amount

10/1/1610/2/16 $3510/3/1610/4/1610/5/16 $3510/6/1610/7/16


$35

Still Wrong

• Dates with Nulls treated as ZEROES do work.



Growth Date

Growth

Amount

10/1/16 $010/2/16 $3510/3/16 $010/4/16 $010/5/16 $3510/6/16 $010/7/16 $0


$10

BINGO!Tableau handles this with a standard function and a single click

Using Tableau to handle relative time, seasonality and averages over time periods that have missing values

DEMO

Bringing It All TogetherSUMMARY

To view the FREE video recording of the presentation and download this deck, go to:

http://www.senturus.com/resources/surface-better-insight-from-your-sales-pipeline-data

/

The Senturus comprehensive library of recorded webinars, demos, white papers, presentations, and case studies is available on our website:


Hear the Recording









Contact Jim Frazier• [email protected]• 650 949 2921

Sign up for the Senturus Leading Indicator Subscription Service• We’ll manage the transformations, contortions and

updates for you• We’ll deliver the missing indicators to you• $999 per month (promotional pricing valid through

12/31/2016)

Interested in Learning More?


• Ideally pull your CRM pipeline data into the data warehouse or sales data mart• Next best interim situation is to pull it into a

staging database• In these databases you will be able to do the

transformations and contortions that surface the missing leading indicators• Pipeline growth transactions provide much better

visibility than pipeline balance snapshots– Need to know not just where you are, but how you

got there• Valuable marketing interactions which lead the

leading indicators can be surfaced by similar techniques

Takeaways


Business Analytics ConsultantsWHO WE ARE

Bridging the Gap Between Data & Decision Making

DECISIONS & ACTIONS

Business Needs

Analysis Ready Data

Analysis Ready Data

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Warehousing • Self-Service Business Analytics • Big Data & Advanced Analytics• Planning & Forecasting Systems

Business Analytics Architects

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Surface Better Insight from Your Sales Pipeline Data

Data & Analytics

Transcript of Surface Better Insight from Your Sales Pipeline Data