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Page 2 @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
Page 3 @ssusina #MKTGNATION
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
Page 4 @ssusina #MKTGNATION
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!
Page 6 @ssusina #MKTGNATION
Mountains of Data Known
Engaged MQL
SAL WARNING Falling
Conversion Rates
Page 9 @ssusina #MKTGNATION
Moments of Clarity
• TOPO B2B Predic7ve Technology Report
• Forrester Report “New Technologies Emerge To Help Unearth insight From Mountains of B2B Data
Page 14 @ssusina #MKTGNATION
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
Page 15 @ssusina #MKTGNATION
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
Page 16 @ssusina #MKTGNATION
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
Page 18 @ssusina #MKTGNATION
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
No way to validate costs based on the incremental lead genera7on / cost per lead.
Evaluating Predictive Analytics
Two Month Trial Six Month Sales Cycle
Page 22 @ssusina #MKTGNATION
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
Page 23 @ssusina #MKTGNATION
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
Page 24 @ssusina #MKTGNATION
32 Opportunities Created
15
12
2 3
Prospec(ng Mee(ngs – Non-‐Inbound/Event
A B C D
Page 25 @ssusina #MKTGNATION
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!
Page 26 @ssusina #MKTGNATION
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!
Page 28 @ssusina #MKTGNATION
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.
Page 30 @ssusina #MKTGNATION
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
Page 31 @ssusina #MKTGNATION
2016 Sales Activity YTD
0
10
20
30
40
50
60
Closed Won Lost -‐ Compe7tor Lost -‐ No Decision
D
C
B
A
Page 32 @ssusina #MKTGNATION
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
Page 33 @ssusina #MKTGNATION
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
Page 34 @ssusina #MKTGNATION
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
Page 35 @ssusina #MKTGNATION
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
Page 36 @ssusina #MKTGNATION
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
Page 37 @ssusina #MKTGNATION
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
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