predictive analytics - case study & trial results

Steve Susina Marketing Director, LYONSCG

@ssusina #MKTGNATION

Our Journey •  The Evolving Martech stack •  What is Predic7ve analy7cs/marke7ng/scoring •  Making A Decision •  Internal Business Case / Selling the Execs •  How we Evaluated

•  Apply to prior 7 months data (July 2015 – January 2016) •  Review Mee7ngs, Opportuni7es, Pipeline, Closed Business •  Analysis of Prospec7ng

•  Results •  Lessons Learned / Work to do


The Evolving MARTECH Stack MARKETING AUTOMATION

MARKETO SOCIAL MEDIA &

CURATION FEEDLY & BUFFER

DATA DATA.COM, ETAIL INSIGHTS,

LINKEDIN, HOOVERS, BUILTWITH

CONTENT WORKFLOW DIVVYHQ

WEBSITE/CONTENT MGT WORDPRESS

WEBINARS GO-TO-WEBINAR

CONTENT GENERATION GRAMMARLY SPEECHPAD

MEDIA RELATIONS PR WEB/CISION

ANALYTICS GOOGLE ANALYTICS, MARKETO QUILL BY NARRATIVE SCIENCE CRM

SALESFORCE.COM


It Starts . . . Marketing Nation 2015

•  Recognized the buzz about Predic7ve

•  Research & Educa7on •  Conclusion . . . We know our prospects . . . We have a defined ICP . . . We have a good lead scoring model . . . We Don’t Need This!


Mountains of Data Known

Engaged MQL

SAL


Mountains of Data Known

Engaged MQL

SAL WARNING Falling

Conversion Rates


Problem: Too Much and Too Little Data


Chet Holmes 3% Rule


Moments of Clarity

•  TOPO B2B Predic7ve Technology Report

•  Forrester Report “New Technologies Emerge To Help Unearth insight From Mountains of B2B Data

Using these tools . . .

. . . considering these . . .

. . . PA is next step on the con7nuum.

I should look at Predictive Analytics again!


Predictive Analytics


Interesting Market Dynamics •  Number of strong, venture-‐funded firms with seemingly similar models

•  Labce Engines •  6 Sense •  Min7go •  Infer •  Leadspace •  Everstring

•  FlipTop exited w/ LinkedIn acquisi7on in late 2015 •  Strong desire by industry players to build client base ahead of consolida7on, posi7on for addi7onal funding, acquisi7on

What IS Predictive Analytics? Statistical Model based on our

Closed-Won and Closed-Lost data Integrates with our Salesforce

and Marketo databases

Scoring model applied to our exis7ng data

New Lead Acquisi7on External Buying Triggers


ABOUT OUR TRIAL

•  Ini7ated Trial with Everstring 12/2015

•  Analysis of our exis7ng Closed-‐Won and Closed-‐Lost •  Crea7on of data model using buying triggers •  Built model to create predic7ve score of our exis7ng database and real-‐7me scoring on all newly created leads

•  Lead genera7on component


Our Database Model


Baseline performance of our data

No way to validate costs based on the incremental lead genera7on / cost per lead.

Evaluating Predictive Analytics

Two Month Trial Six Month Sales Cycle


Analysis of 167 SCHEDULED MEETINGS (Inbound and Prospected) from US ISRs July 2015 to February 2016

50

48

33

36

Prospec(ng Mee(ngs -‐ Overall

A B C D


Inbound vs. prospecting-driven meetings

40 41

16 19

10 7

17 17

0

10

20

30

40

50

60

As Bs Cs Ds

Inbound

Prospected


32 Opportunities Created

15

12

2 3

Prospec(ng Mee(ngs – Non-‐Inbound/Event

A B C D


Prospecting Activity 2468 new contacts with prospecting activity

533 608 768

559

0

200

400

600

800

1000

A B C D

55% of ISR Prospecting against C and D Rated Leads! More D-rated Leads prospected than A-Rated!


Most of our Opportunities from Prospecting are from A- and B-rated leads

0

20

40

60

80

100

120

140

Mee7ngs Opportuni7es

A

B

C

D

85% of Opportunities were based on A & B rated leads!


So, the only thing left to do . . .


Not Quite . . .

• Pride of ownership: “We know enough to call the right prospects!”

•  Fear of missing out – some of those Cs and Ds might s9ll convert!

•  There’s no way we can afford this.


Avoid FOMO via Fast Track For Inbound C & D


Overcome Expense Concerns: Use Math

•  If prospec(ng (me on Cs/Ds was shiBed to As/Bs, and rate of mee+ng & opportunity crea+on is consistent: •  28 incremental opportuni7es over the past 7 months •  48 incremental opportuni7es for a full 12 months

•  Assuming $350K average deal size, that’s $9.8 to $16.8 million addi7onal pipeline

•  Based on 33% close rate, $5.5 million in addi(onal sales


2016 Sales Activity YTD

0

10

20

30

40

50

60

Closed Won Lost -‐ Compe7tor Lost -‐ No Decision

D

C

B

A


Recommendations •  Approve full-‐year Everstring contract

•  Set new rules of engagement for ISRs: •  Reassign all Cs and Ds to Drip Programs •  ISR general prospec7ng to be restricted to As and Bs •  When building out lists, score account first, only pursue contacts if account is rated

A and B

•  Any inbound or event follow-‐up requests will be immediately changed MQL, regardless of score

•  Marke7ng to build engagement campaigns for Cs and Ds, qualify and pass at TBD minimum engagement threshold


Two Month Post-Implementation Prospecting

21.60% 24.60%

31.10%

22.60%

27.50% 28.00% 27.80%

16.70%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

A B C D


Two-Month Post-Implementation Meetings Set

34.00% 32.50%

13.70% 16.40%

48.70%

25.60%

5.10%

17.90%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

A B C D


Post-Recommendation Pipeline Generated

34.00% 32.50%

13.70% 16.40%

48.70%

25.60%

5.10%

17.90%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

A B C D

$1.25 million in opportunity pipeline

$0 in pipeline

$20,000 in pipeline

$0 in pipeline


Not losing opportunistic C and D Leads

$1,250,000

$20,000 $0 $0 $25,000 $0

$772,000

$250,000

$0

$200,000

$400,000

$600,000

$800,000

$1,000,000

$1,200,000

$1,400,000

A B C D


Conclusions •  Look for trial opportuni7es

•  A longer paid trial is bemer than a short free trail • Make sure you get your en7re database scored

•  You’ll need it to determine how your sales team is spending their prospec7ng 7me.

•  Take advantage of market condi7ons when nego7a7ng •  Separate Inbound from Outbound for your analysis

• Commit to fast-‐track high-‐quality inbound leads

predictive analytics - case study & trial results

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