Marketers are recognizing more than ever the need to improve lead quality. In fact, we’ve seen survey after survey (Salesforce,[1] ANNUITUS,[2] Asscend2[3]) that clearly highlight deep frustration among marketers regarding the quality of their prospect data and how improving lead quality is a No.1 priority.
We haven’t, however, seen much analysis of the prospect data itself. Questions are left unanswered:
- To what extent is prospect data inaccurate?
- Where do the biggest quality issues lie?
- Is this an SMB concern or just an enterprise issue?
At Integrate, we recently set out address real world questions such as these, because recognizing the problem is the first step, but pinpointing the causes is key to finding a solution.
Get Integrate’s Data Quality Index here.
Leveraging Integrate’s platform, and specifically its data governance software, we analyzed more than 778,000 B2B tech industry leads and compiled the findings in Integrate’s first research report, titled “Integrate Indices: Data Quality – B2B Tech Industry.” The Index’s findings are disconcerting – and fully validate the call for better data.
On average, across enterprise and SMB tech industry businesses as well as the media companies that generate leads for them, 40% of leads generated were determined to be of poor quality. Specifically, 311K plus leads out of 778.5K contained an invalid email address, missing fields, duplicate data, incorrect formatting or invalid values.
The Index illustrates percentages for each issue category (disposition) according to company size/type to highlight common themes that plague the marketing industry. For example, the Index shows that 7% of all leads acquired by enterprise tech businesses contained missing fields, 11% of SMB leads had unacceptable values and 16% of leads generated by media companies for their marketer clients contained duplicate prospect data.
The ramifications of dirty data are profound
Marketers, especially demand gen practitioners, are understandably frustrated by the poor quality of their acquired prospect data. After all, inaccurate, incomplete and duplicate data – what we often refer to as dirty data – have many profoundly negative consequences:
- Skews conversion insights and reduces optimization effectiveness, which in turn prevents good leads from converting through the pipeline
- Requires manual lead cleansing that slows velocity of entire pipeline, allowing good prospect data to go bad
- Wastes sales’ time and resources that could be better spent on qualified, interested prospects
- Diminishes customer experience by preventing good prospects from getting the content they need when they need it
- Decreases return on marketing automation and CRM investment by wasting usage volume on ineffective data. In fact, 36% of marketers say “insufficient data quality” is the biggest obstacle to marketing automation success.[4]
The amount or time, energy and money that goes into correcting prospect data quality issues at various stages of the customer acquisition funnel can be crippling. As Justin Gray, CEO of LeadMD remarked after reading the report:
“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.”
[1] Salesforce, 2015 State of Marketing, January 2015
[2] Ascend2 Lead Generation Benchmark Survey, Dec. 2013
[3] ANNUITAS, 2014 B2B Demand Gen Study
[4] Ascend2 Marketing Automation Benchmark Survey, July 2014
