84% of marketing organizations are implementing or expanding AI and Machine Learning in 2018. (Forbes)
This presentation will detail tactics and provide examples of how B2B manufacturing, services and technology companies are shortening their sales cycles by marrying their sales and marketing data with machine learning and AI.
Data building blocks: What and where are the right kinds of data to baseline sales and marketing effectiveness.
Data alignment for results: How to capture, measure and grow visibility, engagement and sales. Examples of how clients align physical and digital tactics to more efficiently convert customers through aligned sales and marketing experiences.
Data strategy: What is a data strategy, and how to expand the types of data you collect to feed advanced machine learning applications.
Machine learning and AI: How to apply data science to scale sales and marketing results. Examples of how clients used website and voice-of-customer data to increase engagement and conversion.
2. CONFIDENTIAL
Data You Have Today
How to apply digital and
competitive data to the
sales funnel to increase
relevant awareness and
lead quality.
Data You Can Get
Show how B2B
marketers have the
opportunity to know
and apply more about
their customers today
than ever before.
Data Strategy
Look at the data you have
and quantify the value.
Data Science
Examples of how to
create models for
machine learning and AI.
P R E S E N T E D B Y A M G & P A N D A T A
Agenda
3.
4. 68% of B2B buyers
research on their own,
online, before interacting
with a sales person
Forrester Business Technographics Global Priorities &
Journey Survey (2017)
13. Impressions
Channel Engagement
Social Shares
Social Comments
Clicks
Click-Through Rate
Landing Page Visits
Landing Page Clicks
Average Time on Site
Bounce Rate
Email Open Rate
Email Click-Through Rate
Paid Search Keywords
Internal Search Queries
Contact Form Starts
Contact Form Bails
Contact Form Submissions
What Data We Have Now
Key Data Points
28. Simple Process for Closing the Loop on ROI
List the post-
purchase steps
1 2 3 4
29. Simple Process for Closing the Loop on ROI
List the post-
purchase steps
1 2 3 4
Call out challenges
& blockers
30. Simple Process for Closing the Loop on ROI
List the post-
purchase steps
1 2 3 4
Call-out challenges
& blockers
Fix a challenge
with an
existing tool
31. Simple Process for Closing the Loop on ROI
List the post-
purchase steps
1 2 3 4
Call-out challenges
& blockers
Fix a challenge
with an
existing tool
Connect the data
to measure ROI
44. • Generate relevant
awareness for more qualified
leads that close faster
• Design post-purchase
experience that unlocks
continuous/real-time
end-buyer data
• Connect technology to scale
success and measure ROI
Key Takeaways
45. Agenda
Data Strategy
Look at the data you have
and quantify the value.
Data Science
Examples of how to
create models for
machine learning and AI.
47. Data Science Simplified
Address specific business challenges
Transform data to meaningful information
Inform decisions that impact the bottom line
Process
Technology
People
48. “Sales teams adopting AI are seeing
an increase in leads and
appointments of more than 50%
[and] cost reductions of 40%–60%”
– Harvard Business Review, Dec 2018
49. 77% of companies
report that business
adoption of AI initiatives
remains a major
challenge
YET…
– Forbes Technology Council, Mar 2019
50. Improve retention with intelligent
customer service
• 60,000+ comments from
customers over 2 years
• Disagreement on definitions
& llimited trust
• “Not my customers” excuse
PAINS
• Net Promoter Score
improved by 20% on
average across all channels
• Developed organizational
trust in using data to affect
change
OUTCOME
51. Qualify leads with
digital behavior
• Company sells through
distributors
• 600k visits / month
& < 1% identified
PAINS
• Identified intent to buy in 3
months with 80% accuracy
• Call to action integrated in
website to identify
anonymous users
OUTCOME
52. Identify more leads
with text analytics
• Time-consuming lead-
generation process
• Sales professionals reading
company descriptions to
qualify prospects
PAINS
• Pre-qualify companies
with +86% accuracy
• Saved hundreds of hours
a month
OUTCOME
54. 3 Ingredients for
Successful Data Strategy
• Buy-In
• Trust & Adoption
• Consistent Performance
• Data driven by business leader
• Data literacy & training
• Data governance
55. Projects driven
by ROI get buy-in
Coordinate cross-
functional working groups
Address cultural
boundaries to impact
Start small & prioritize
use-cases by ROI
56. Literacy creates
a foundation
for trust
Identify and invest in your
data champions
Articulate assumptions
& definitions
Document data sources
& quality
59. How you can
RISE to your data
What decisions?
Who will make them?
RESULT
Is there a quantifiable return?
Is it worth the investment?
IMPACT
How will the solution be tested?
How is success defined?
SUCCESS
Who will the solution affect?
What regulatory risks are posed?
ETHICS
