Integrate Indices. Data Quality B2B Tech Industry Integrate, Inc.

Transcription

1 Integrate Indices Data Quality B2B Tech Industry Integrate, Inc.

2 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 (6). 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 (5 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 datadriven 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. 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, Values/ Ranges and Missing Fields are the most prevalent data quality issues. Failed and Failed Address are lesscommon errors, but more difficult to remedy. Media companies risk losing clients and revenue streams if data quality errors are left unchecked Integrate, Inc.

3 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% poorquality 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 Minimum Annual Leads per Individual Organization 69, , ,438 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 Integrate, Inc.

4 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 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. 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 Indicates that the prospectprovided address was inactive when pinged by Integrate s validation software. Failed Address Indicates that the provided physical address was unable to be identified in USPS database. 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 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 Integrate, Inc.

5 FINDINGS Results from all three categories are strikingly similar, with poor-quality dispositions comprising 3 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 Values/Ranges and Missing Fields. Formatting, Failed and Failed Address make a less notable impact, but still significant when combined 5% of SMB dispositions, of enterprise dispositions and 7% of media company dispositions. SMB Lead Dispositions Total Leads Generated 12, ,000 16% 1 6 GOOD 8,000 6,000 4, % 2,000 3 POOR 0 Series 1 Missing Fields 4,329 Duplicate Data 10,667 Formatting 1,070 Failed 1,585 Failed Address 990 Values/Ranges 7,416 0% Series 2 6% 15% 1% 11% Integrate, Inc.

6 Enterprise Lead Dispositions Total Leads Generated 35,000 16% 30, , % GOOD 20,000 15,000 6% 10,000 5,000 39% POOR 0 # of Leads Missing Fields 15,678 Duplicate Data 32,572 Formatting 2,472 Failed 11,148 Failed Address 8,976 Values/Ranges 22,200 0% % of Leads 7% 1 1% 5% 9% 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 needs to be the year everyone wakes up and gives data the attention it truly deserves, rather than making it another failed resolution. Justin Gray CEO, LeadMD Integrate, Inc.

7 Media Company Lead Dispositions Total Leads Generated 59% GOOD 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10, % 1 1 6% 41% POOR 0 # of Leads Missing Fields 39,117 Duplicate Data 73,989 Formatting 5,852 Failed 17,550 Failed Address 9,885 Values/Ranges 48,394 0% % of Leads 16% 1% In aggregate, Duplicate Data comprised 15% of leads across all three categories. Values/Ranges at and Missing Fields at indicate very significant quality issues as well. Formatting, Failed and Failed Address made up the remaining 7% Integrate, Inc.

8 Lead Dispositions in Aggregate Total Leads Generated 140,000 16% 120, , % GOOD 80,000 60,000 6% 40,000 20,000 40% POOR 0 Series 1 Missing Fields 59,124 Duplicate Data 117,228 Formatting 9,394 Failed 30,283 Failed Address 19,851 Values/Ranges 78,010 0% Series 2 15% 1% 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. Taylor Sr. Manager, Americas Demand Gen, A10 Networks Integrate, Inc.

9 CONCLUSIONS 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, 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 and Failed Address less prevalent issues but more difficult to remedy. Combined, failed 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 313,890 lead errors. 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 President, Heinz Marketing Inc Integrate, Inc.

10 Master demand marketing. Integrate is a marketing technology provider of Demand Orchestration Software, enabling marketers to automate top-of-funnel demand marketing efforts. The software works with marketing automation and CRM systems, as well as ABM and predictive software, to build holistic, predictable demand marketing engines. The end results are more efficient marketing organizations; cleaner, faster prospect data; and scalable contributions to pipeline and revenue. Visit or to learn why innovative companies like Dell, Rackspace, Salesforce and Inttact choose Integrate. Take control of your marketing data. For more information, contact us at: requests@integrate.com PREVIEW INTEGRATE