The Issues with Buying B2B Contacts

When it comes to acquiring contacts in bulk for money the options are really endless. There are the reputable market leaders such as Zoominfo,, Insideview, and then there are what I would consider the “fly by night” operations with lessor known names. We all receive emails selling us lists of our competitors client’s from companies we can’t recognize. Meeting constantly growing demand generation needs makes purchasing these lists ever tempting, even if the act is one that belies desperation.

Renting lists is equal parts frustrating as volume based thresholds keep vendors in the shady area of grey with respect to names they put forward to meet their numbers. They play the numbers game in the truest sense. This is where you get a list back and half the leads are from contacts in 3rd world countries.

In the overall scheme of modern digital marketing email marketing is still one of the cost effective means of driving interest, hence we are left to our own vices to sift through the masses of data available to us and build massive contact databases, at least while no GDPR type measures move stateside.

While it’s no secret that the best option is to continuously build an opted-in database of emails and nurture them to fruition. The reality is this a painstakingly slow process that is not in line with B2B revenue target timeline.

The Challenges of Purchasing Lists in Sales & Marketing

Both marketing & sales departments rely on this data and both teams encounter distinct issues with its usage.

Marketing teams struggle to grow their databases through list acquisition because historically this has been act on volume not on quality. Over the years this mentality had worked to some extent, but more recently this strategy has become increasingly difficult to execute. Poor quality data now affects our online reputation in ways that are difficult to quantify but yet so pervasive they have immediate and lasting effects on our campaign results. Poor quality lists lead to unopened emails, SPAM complaints and inevitably reduce us to the back alleys of lower tier servers and poor deliverability rates.

Email pinging checkers like Neverbounce & Zerobounce as well as a host of others address some of the issues. They are cost effective but limited in their ability to validate contacts on two fronts. Using these services one notices that a significant number of contacts come back as “catch-alls” effectively meaning maybe the contact is accurate. In many cases 30-40% of contacts come back with this designation. This is because in their own admission these services are only pinging for hard bounces and not actually emailing each record for validity. Inherently this differs from the way your marketing team will actually use the data so therein lies a serious flaw.

Just because an email does not bounce does not mean that the email is going to the intended person. An obvious example of this are employees that change roles or leave an organization, often these emails are kept active but it does not mean anyone is actually seeing them. If email best practices calls for relevant content to relevant contacts, this process is underwhelming.

Sales Teams Struggle

While on the marketing side email data that isn’t 100% accurate is more forgiving on the sales side the costs are less forgiving and equally taxing. Sales people are more fickle with purchased data as they are on the ground having to reach people and their time is considered valuable. Deficient data keeps reps doing the medial task of checking sources against Linkedin, guess emailing syntaxes, and Google searches. Good reps view this as necessary evil but become increasingly frustrated because they want to spend time talking to people. This issue is further amplified by organizations that have adopted dialers and other sales acceleration tools. The success of these tools is largely correlated to the quality of the lists being plugged in.

Lack of Accountability for B2B Data Providers

The problem with current list sellers regardless of reputation is the lack of accountability. There is really no good way to test and validate the quality of a data source in bulk. Vendors prey on this fact and while they are more than happy to provide a sample of data prior to purchase, terms often become bleak upon delivery. Samples provided are usually “cherry picked” records that are intended to make us hum and haw about how this great list will solve our demand generation needs for years.

List purveyors generally go to great lengths to ensure their money is received prior to handing over the data. Guarantees and refund policies are meant to appease concerned parties that they will have some recourse of action should the list data be poor. Of course those that have been through this process know this is just an exchange of one faulty record for another less faulty record.

Our tests showed that even the most reputable data providers that touted 98% accuracy of their records that simply meant that emails would not bounce back and that phone numbers to head offices were indeed. The reality is about 20% of their records were not in fact cases where people, emails and phone numbers actually lined up. In our study less reputable list providers hovered anywhere from 30-60% accuracy.

The Problem with Automated Lead Enrichment

The market is now littered with solutions that help organizations enrich data in their CRM installs or their marketing automation solutions. Essentially these solutions integrate with your technology stack and help to fill in and correct gaps in your contact data. It sounds great, sales teams complain about poor data in Salesforce and these solutions can address the issue in bulk and quickly.

The problem is that to enrich data you need good data! Whenever I prompt vendors about this they walk me through complex algorithms and processes that feel purposely meant to confuse and bewilder me into thinking some grandiose process is playing out on the back-end to give me stronger data then I have ever had before. Sadly this is far from the truth as no one has presented me as yet with a valid “alpha” source of data that can be trusted. Until that happens we are forced to wonder exactly what are we enriching our data with?


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