Integrate Indices
Data Quality — B2B Tech Industry
Key Takeaways
•
•
Poor data quality is a significant
marketing challenge, affecting
on average
40% of the leads
generated for SMB and enterprise
businesses alike as well as the
media companies that work
with them.
•
•
Duplicate Data, Invalid Values/
Ranges and Missing Fields are the
most prevalent data quality issues.
•
•
Failed Email Validation and Failed
Address Validation are
less-common errors, but more difficult
to remedy.
•
•
Media companies risk losing clients
and revenue streams if data quality
errors are left unchecked.
INTRODUCTION
Marketing decisions are only as good as the data that informs them. Clear decisions never result from confused information. And successful strategies aren’t based on erroneous data. The importance of clean and accurate data isn’t new — but neither is it typically obtained to the standards marketers require.In fact, the results of several recent Ascend2 surveys have identified data quality (specifically, lead quality) as a top concern among marketers. A December 2013 Lead Generation Benchmark Survey by Ascend2 and Research Partners found that “Improving the quality of leads generated” was the No. 1 priority among respondents (62%). These results were further corroborated by Ascend2’s November 2014 Data-Driven Marketing Survey, which identified “Lack of data quality/completeness” as, again, the No. 1 most challenging obstacle to data-driven marketing success (54% of respondents). How could this be? Well, according to SiriusDecisions, 25% of the average B2B marketer’s database is inaccurate and 60% of companies have an overall data health of “unreliable.” These are strong numbers and bold statements – so we decided to test them.
Integrate’s data governance software filters tens of thousands of conversions daily, blocking leads that fail verification tests, lack complete data, don’t comply with campaign parameters, or are incorrectly formatted. In light of the Ascend2 and SiriusDecisions studies, we sought to examine the data flowing through the Integrate platform to answer the following questions:
Why do marketers consider poor data quality to be the top obstacle to data-driven marketing? How is it the 25% of the B2B databases can be inaccurate? In other words, what proportion of lead data is poor quality and why?
Using Integrate’s data governance
software, we recently analyzed the quality of over 775K leads generated for B2B marketers in the technology industry during the last year – and we were astonished to find that on average 40% of generated leads were deemed to be of poor quality.
METHODOLOGY
Since these leads were generated for and by companies of differing sizes and operational goals, we analyzed in aggregate as well in three B2B technology industry categories: Small and medium businesses (SMB), enterprise businesses and media companies that generate leads for B2B marketing clients. To be included in the research, the organization must have generated a minimum number of leads relative to their category.
Furthermore, leads deemed to be poor-quality were categorized by one of six specific issues or “dispositions.” It should be noted that a single prospect can ultimately fall into several categories, since leads that are blocked by Integrate’s software are automatically sent back to the lead-producing source, at which time they are often corrected and resubmitted by the media partner only to be sent back for another quality disposition. This obviously softens the 40% poor-quality figure. However, it also goes to show how prevalent lead data issues are and the extent to which they can slow an operation if not properly and automatically governed.
TECHNOLOGY INDUSTRY SMB ENTERPRISE MEDIA COMPANY
Total Leads Generated
Across Category 69,170 236,977 472,438 Minimum Annual
Leads per Individual
Organization 6,000 24,000 36,000
The issue of data quality
continues to be one of
the biggest roadblocks
to effectively analyzing
the prospect and
customer journey. It also
dramatically increases
the costs of analytics
projects and negatively
impacts performance.
Sameer Khan
Senior Product Marketing Manager, IBM Customer Analytics
DISPOSITION DEFINITIONS
Missing Fields – Includes any lead which is missing one or more marketer-required prospect data points; for
example, the lead may contain a name and email address, but be missing job title information. Since lead prices are negotiated based on specific number and types
of data points, these leads do not meet the quality requirements of the purchasing organization.
Duplicate Data – The designation for leads that
contain specific prospect data that has already been by the same media source on the same campaign, and if left unchecked essentially causes the marketing organization to pay twice for the same lead.
Invalid Formatting – A lead may have all the
required information, but is not formatted according to marketer specifications, which hinders adequate program performance aggregation and analyzes. It also prevents the easy injection of leads into marketing automation and CRM systems.
Failed Email Validation – Indicates that the
prospect-provided email address was inactive when pinged by Integrate’s email validation software.
Failed Address Validation – Indicates that the
provided physical address was unable to be identified in USPS database.
Invalid Values/Ranges – The designation for leads
with either values or ranges of values that aren’t within accepted campaign parameters. For example, a lead may contain a New York business address when the campaign parameters specify that only leads from California are acceptable. Or, the prospect may work for a company of 50-100 employees when the campaign parameters specify that no prospects from companies of less than 500 employees
will be accepted.
Data is the oil of any
marketing engine, and in
order to create perpetual
demand generation, data
accuracy needs to be a top
priority. Marketers must
be ruthless and deliberate
about data quality and
standardization at
point of entry.
Jonathan Burg
Sr. Director, Marketing + Customer Acquisition, Apperian
4,329
Series 1 10,667 1,070 1,585 990 7,416
Missing
Fields Duplicate Data FormattingInvalid Failed Email Validation Failed Address Validation Values/RangesInvalid
0 2,000 4,000 6,000 8,000 10,000 12,000 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%
FINDINGS
Results from all three categories are strikingly similar, with poor-quality dispositions comprising 38% of SMB leads, 39% of enterprise leads, and 41% of media company produced leads.
Moreover, disposition breakdowns are similar as well, with Duplicate Data being the No. 1 issues across all three categories, followed by Invalid Values/Ranges and Missing Fields. Invalid Formatting, Failed Email Validation and Failed Address Validation make a less notable impact, but still significant when combined – 5% of SMB dispositions, 10% of enterprise dispositions and 7% of media company dispositions.
Total Leads Generated
62%
GOOD
POOR
38%
15,678 7% # of Leads % of Leads 32,572 14% 2,472 1% 11,148 5% 8,976 4% 22,200 9% Missing
Fields Duplicate Data FormattingInvalid Failed Email Validation Failed Address Validation Values/RangesInvalid
0 10,000 5,000 15,000 20,000 25,000 30,000 35,000 0% 2% 4% 6% 8% 10% 12% 14% 16%
Total Leads Generated
61%
GOOD
POOR
39%
Enterprise Lead Dispositions
Garbage data seems to be the ‘cigarette smoke’ of the marketing community. Everyone knows
it exists, we know it will kill you but we keep doing the same bad practices and attempting to
solve this huge problem with small strokes. 2015 needs to be the year everyone wakes up and
gives data the attention it truly deserves, rather than making it another failed resolution.
Justin Gray39,117 8% # of Leads % of Leads 73,989 16% 5,852 1% 17,550 4% 9,885 2% 48,394 10% Missing
Fields Duplicate Data FormattingInvalid Failed Email Validation Failed Address Validation Values/RangesInvalid
0 30,000 20,000 10,000 40,000 50,000 60,000 70,000 80,000 0% 2% 4% 6% 8% 10% 12% 14% 18% 16%
In aggregate, Duplicate Data comprised 15% of leads across all three categories. Invalid Values/Ranges at 10% and Missing Fields at 8% indicate very significant quality issues as well. Invalid Formatting, Failed Email Validation and Failed Address Validation made up the remaining 7%.
Total Leads Generated
59%
GOOD
POOR
41%
59,124 8% Series 1 Series 2 117,228 15% 9,394 1% 30,283 4% 19,851 2% 78,010 10% Missing
Fields Duplicate Data FormattingInvalid Failed Email Validation Failed Address Validation Values/RangesInvalid
0 40,000 20,000 60,000 80,000 100,000 120,000 140,000 0% 2% 4% 6% 8% 10% 12% 14% 16%
Total Leads Generated
60%
GOOD
POOR
40%
Lead Dispositions in Aggregate
A 40% fail rate on prospect data quality should not be the industry norm. Bad data
has devastating effects for any marketing organization – wasted time and resources,
inaccurate program analysis and decisions, undermined tech investments. Most
importantly, it prevents marketers from providing an optimal customer experience.
Travis C. TaylorCONCLUSIONS
Poor data quality is truly a significant problem, affecting SMB and enterprise businesses alike as well as the media companies that work with them. The consequences for SMBs, however, is
often amplified due to fewer resources available to address quality issues.
Duplicate Data, Invalid Values/Ranges and Missing Fields biggest data quality issues. Combined, these
lead errors occurred 256,933 times in this study (33% of the 778,585 leads analyzed). When unchecked, these quality issues can waste media budget. It’s more often the case, however, that they are eventually corrected via multiple manual scrubbing processes and returns/negotiations with media partners. While marketers often don’t end up paying for these leads, these processes still require time and human resources, slowing campaign performance and leaving less time for program analysis and optimization. The result is fewer conversions through the marketing/ sales funnel, increased cost per customer and reduced revenue and profit margin.
Failed Email Validation and Failed Address Validation
less prevalent issues but more difficult to remedy.
Combined, failed email and address validation errors make up just over 6% of all leads analyzed. A smaller number, but not insignificant considering these issues can’t be identified through traditional lead scrubbing techniques. With average B2B lead prices at over $50, this quality issue could easily have translated to more than $2.5 million in wasted media spend.
Media companies risk losing clients and revenue streams due to quality issues. Manual processes
used to identify and correct the various quality dispositions are prone to human error, undermining the data’s veracity and value to clients. If the media companies analyzed in the study had not been using data governance software, they would’ve had to manually catch and correct a combined
Dirty data is the silent killer
of marketing campaigns.
It makes you look bad,
depresses the impact of great
content and offers, and can
put your brand, reputation
and domain at risk (or
worse). Ignore this report
and its implications for your
business at your peril.
Matt Heinz
To learn how Integrate’s data
validation software can improve
your data quality, contact us today.
866-478-0326
www.integrate.com
ABOUT INTEGRATE
Integrate is a marketing software provider on a mission to arm demand marketers with the tools, insights and integrations required to change the way they execute demand generation. Integrate’s software enables demand gen and marketing ops pros to manage the lifecycle of outbound demand generation programs and seamlessly connect resulting data with marketing automation systems – including Oracle Eloqua, Marketo and Pardot. The end results are more efficient marketing organizations; cleaner, faster prospect data; and increased marketing ROI. Visit www.integrate.com or follow @integrate to learn why innovative companies like TIBCO, DocuSign, Dell, Five9, Iron Mountain and CA Technologies, Inc. trust Integrate.
Take control of your marketing data.
LEARN HOWBad marketing data is the equivalent of putting the wrong kind
of gas into your race car. Organizations are building significant
marketing technology infrastructures today; yet, they continue
to neglect the very foundation that supports this infrastructure
— clean data. Data hygiene isn’t sexy, but it has to be part of the
process used to support your marketing infrastructure. Basically,