This document outlines five steps for improving marketing lead data to increase revenue: 1) Set a baseline by auditing existing data, identifying errors and opportunities for improvement. 2) Organize a cross-departmental team and projects with goals and metrics. 3) Make improvements starting with simple, low-cost projects focused on data capture, processing and sales-marketing coordination. 4) Measure results including costs, data quality changes and business impacts. 5) Repeat the process by identifying new opportunities and maintaining past improvements through ongoing measurements. Quality data programs prepare teams for additional cooperation and maximize the value of integrated marketing systems.

1. Marketing Lead Data:
Five Steps to Higher Revenue
July 20, 2011
David M. Raab
Raab Associates Inc.
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2. Data Problem? What Data Problem?
• top 3 marketing
problems:
technology, resources, pr
ocess (what about
data?)
• 2/3 of prospect data is
less than 75% accurate
(vs. 1/3 of customer data)
• data decays at 2% per
month
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3. Why Data Problems Matter
• your new marketing
automation system
will fail (the boss
won’t be happy)
• your email won’t get
delivered (or, even
worse, it will)
• opportunity costs
(wasted acquisitions,
lost revenue)
• salespeople won’t
follow up (55%
blame missing data;
next-highest reason
is 14%)
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4. Root Causes
• poor data capture • less sales involvement
• user-provided data • recycled operational
data
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5. Solving the Problem: 5 Step Program
1. Set a Baseline
2. Organize for Action
3. Make Improvements
4. Measure Results
5. Repeat as Needed
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6. 1. Set a Baseline
• research existing data
• select key elements
(one word: email)
• test for dupes and
errors
• consider the source
• document update
processes
• identify opportunities
for improvement
• define value and
prioritize
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7. 2. Organize for Action
• governance
• cross-department
team
• executive sponsor
• departmental data
stewards
• select projects
• set goals
• define metrics
• track and report
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8. 3. Make Improvements
initial projects: simple,
low cost, measurable
• input: data capture, user
registration, progressive
profiling
• import: processing rules,
source priority
• external data: validation,
enhancement, refresh
• process: sales and
marketing coordination,
training, feedback
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9. 4. Measure Results
• measure types • always compare to something
• effort (costs, processing volumes) • goal, history, industry benchmark
• results (changes, error
rates, usage, attitudes)
• make results accessible
• business value (lower costs, more
leads, higher revenues) • dashboard, variance reports
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10. 5. Repeat as Needed
• (it’s always needed)
• identify new
opportunities
• avoid back-sliding
(maintain previous
improvements)
• on-going baseline
measurements
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11. Tools Can Help
• data cleansing
• name/address
standardization, matchi
ng, reference data
• real time validation
• periodic refresh
• no silver bullet
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12. Conclusion
• quality pays
• integrated
systems magnify
the value
• quality programs
set the stage for
additional
cooperation
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13. Thank You.
David Raab
Raab Associates Inc.
draab@raabassociates.com
www.raabassociatesinc.om
Twitter: @draab
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