BusinessOnline CEO, Thad Kahlow, Lithium Technologies VP of Marketing, Dayle Hall, and LinkedIn Global Marketing Director, Kelly Kyer discuss the challenges B2B marketers face when it comes to data in today's ever-more-competitive world. Enjoy expert opinions about how to deliver meaningful results and why meaningful metrics must include both people and data — in addition to:
-- Why data is the great clarifier
-- Three secrets to demystifying data
-- How data should inform content alignment across the buying journey
2. Kelly Kyer
Global Director, Technology Vertical
Marketing
LinkedIn
Thad Kahlow
CEO
BusinessOnline
Dayle Hall
SVP, Head of Marketing
Lithium Technologies
THE CONVERSATIONALISTS
3. • Vanity metrics are surface-level metrics. They’re often large measures, like
number of downloads. Use them to initiate partnerships and gain a following.
• Clarity metrics are operational metrics, like the number of minutes a day your
product actually gets used or how long it took for a user to get service. These are
the hidden gears that drive growth. Use them to solidify your competitive
advantage.
VANITY & CLARITY
Both matter, but are used for very different things.
4. Only 10% of the marketers we spoke with feel
they are getting data-driven marketing right.
7. The process for better results:
• Adopt a performance driven mentality
• Integrate as much of your marketing
technology as possible
• Gain insights on the performance of
your programs
• Use that data to drive change
• Repeat
PROCESSES
TOOLS &
TALENT
8. The tactics to get you there:
• Set goals for every stage and level of the funnel
• Work back from your bookings goal each quarter
• Use conversion rates from previous quarters to set targets
• Track prospect to qualified opportunity
• Use findings to make decisions on funding for the coming quarter
• Conduct weekly pipeline reviews with sales and marketing leadership
• Employ multiple tools to help visualize and report out on the data
10. DATA MINING: The business process
2: TRANSFORM
Transform by cleansing & reshaping:
remove nulls, set data types, new columns
aggregate, join to like data sources
1: EXTRACT
Messy data
from disparate
sources
3: LOAD
Enriched,
intelligent data
sets ready for
analysis back
to warehouse
(ETL) where
does it live?
11. Web Analytics & Channel Management
Marketing Tech
Channel/Media Data
Connected, Correlated & Cleansed Data
The Connected Customer Data Martech Stack
12. PROCESSES
TOOLS &
TALENT
The tools for success:
• Leverage analytics resources to
measure traffic
• Enrich account specific information with
3rd party data
• Feed you’re the data into your marketing
automation system to optimize your
campaigns
• Tie your campaign data to your CRM
and active pipeline opportunities to run conversion
analysis
• Use visualization tools for more complete reporting
and sharing the results
14. PROCESSES
TOOLS &
TALENT
The skills to deliver:
• Digital Marketing
• Web content
• Merchandising
• Email nurture
• Business analytics
• Marketing operations
• Attribution modeling
Marketing Leadership needs to work with
business analytics experts to help craft the story.
15. What role does your gut play when evaluating
processes, tools and talent?
16. PROCESSES
TOOLS &
TALENT
Key take aways:
• Adopt a performance driven mentality
• Set goals for every stage and level of the
funnel
• Hire the right skills and expertise to meet
your objectives
• Marry the right people, with the right
processes to make the most of the Tech you
have (you don’t necessarily need more tech)
• Your gut is not obsolete, it still plays a role
in content and campaign creation
• Think and plan big, but act small and iterate
18. To get the full report with insights
and findings from our research with
senior B2B marketers download:
Metrics Matter: A B2B Guide to
Finding Real Marketing ROI
http://bit.ly/metrics_matter
Thad Kahlow
CEO, Business Online
@tkahlow
@BOLOptimized
Kelly Kyer
Global Director,
Technology Vertical Marketing,
LinkedIn Marketing Solutions
@kellykyer
Dayle Hall
Vice President, Brand & Digital Marketing
@marinadazza
@LithiumTech
Editor's Notes
ETL –
Step one – Extract messy data from disparate sources (messy; ex log files, not ready for consumption)
Step two – Transform by cleansing & reshaping, i.e. (where domain knowledge comes into play, where add value that stakeholder appreciate)
remove nulls, set data types, create new columns
aggregate, join to like data sources
Step three – Load enriched, intelligent data sets ready for analysis back to warehouse (ETL) where does it go?