Business Marketing

Data Inaccuracies Harmful for Sales Cycles and Lead Generation

Today, 67 percent of businesses rely on CRM data to segment and target customers and 64.8 percent of sales reps’ time is spent on non-revenue generated activities. Therefore, B2B brands must prioritize clean data in order to streamline sales revenue and lead generation efforts.

Since the start of the Information Age, people have thought access to information would solve many of the world’s problems. In nearly every case, this optimism eventually gives way to confusion when the access to information doesn’t solve the problems people thought it would.  More information is good, but it also contains more bad information, and a small amount of bad information can drown out the good.  

In sales and marketing, misinformation is usually self-inflicted in the form of inaccurate data.  Companies have always worked with inaccurate data, but the problems created by it were always limited because of the manual nature of the sales and marketing funnels.  With the rise of marketing technology and automation combined with a strong push to automate and scale more personal interactions, the negative effects of bad data are being amplified.  

Before, you might get someone’s name wrong on a form fill.  Now you’re more likely to get their gender wrong, identify them as in the wrong industry, think they’ve been downloading your content and misidentify their political party affiliation.  So, you end up sending a unicorn themed mail piece to the wrong person, talking about the benefits of your CRM plugin designed for sandwich shops.  This person used to own a sandwich shop and is now in sales in the power washing industry.  They haven’t seen any of your company’s emails, but their security did check your marketing email links for potential viruses before sending marketing messages to the spam filter. This highlights how important up-to-date and accurate information is. The more information we have, the more opportunities there are for inaccurate information.   

Most of these examples are high level inaccuracies that show themselves when you do more segmented campaigns. Data inaccuracies can cause campaigns to be ineffective when the whole purpose was to utilize detailed data to deliver more relevant campaign content.  Also, inaccuracies with phone numbers, addresses, emails and contacts that are no longer at the given company cause even more waste in sales and marketing.  

There are many data providers now with massive volumes of company, contact and demographic data for both business and consumer marketing.  Even the best of these companies constantly struggle to keep their data clean.  A company’s sourcing and cleaning practices must be considered when looking for data. However, many of the best-known companies have fatally flawed practices. In some cases, they benefit directly from the misinformation.  

B2B companies think of LinkedIn as a premium source for business contact information.  But LinkedIn data is crowdsourced and with nothing to maintain the accuracy, we estimate up to one-third of U.S. profiles on LinkedIn are fake or abandoned.  It recently came out that hundreds of thousands of Google Maps listings are fake. This inaccurate data ends up making its way into companies CRMs wreaking havoc on the sales and marketing funnel.  

I’ve singled out LinkedIn and Google Maps because they are so well known.  All databases that allow the public to create or alter their data have issues with inaccuracy, especially when they have incentives for misinformation.   Take the case of Jigsaw, which was purchased by CRM behemoth Salesforce in 2010 for approximately $142 million in cash, plus bonus’, and rebranded as  Jigsaw used a crowd sourced data model allowing users to upload their sales and marketing data for credit they could use to download new records.  

As a result, Jigsaw quickly expanded the size of their business data. Yet, they did not have a strong mechanism to validate and clean their data, and they ran into quality issues.  Users were also uploading inaccurate data to start with, as well as editing existing records with inaccurate information.  It’s rumored that Salesforce is throwing out and working with D&B because the Jigsaw dataset became notoriously bad. 

Maintaining information accuracy is a difficult and never-ending task that has overwhelmed even the largest companies.  With easier access to large amounts of information, data companies have found that owning data is easy, but identifying the accurate versus inaccurate data in a large data pool is increasingly difficult. Even large companies are poorly equipped to tackle the issues that arise when managing large amounts of sales and marketing data. Additionally, sales and marketing departments complain about data accuracy, but they have no expertise with data and are not willing to get their hands dirty with it. They are right, that isn’t the job they signed up for.  Even data companies are typically better at selling data than they are at maintaining it. B2B companies do not need to have in- house expertise in data, but in order to sustain sales revenue they do need to make data accuracy a priority. 

In fact, MountainTop Data’s internal studies have shown 50 percent of a B2B marketing database goes bad every two years. The largest cause of this is people changing jobs, but it also includes company mergers, domain changes, promotions, new hires, company and contact relocation, and email and phone changes. 

Maintaining accurate CRM data for sales and marketing can also be looked at with the 1-10-100 rule.  This rule says it costs 10 times as much to clean data after you purchase it and 100 times as much to do nothing.  So, what is the cost of dirty data? Consider at least 50 percent of your sales and marketing budget wasted.

About the author


Sky Cassidy

Sky Cassidy is the CEO of MountainTop Data and host of the If You Market podcast. He grew up in rural Northern California and moved to Los Angeles after college.  After a decade in the sales and marketing trenches and dabbling in the Southern California startup scene he took over as CEO of MountainTop Data, a provider of list and data services for B2B marketing.