Stop Throwing Away Targeted Leads!

You met Matt in our Executive Q&A, he has now contributed a piece of his own on throwing away targeted leads.

Marketers are masters at creating demand and generating leads. They leverage all kinds of techniques, including search engine optimization (SEO), search engine marketing (SEM), pay-per-click ads (PPC), social media, and email.

In fact, email is the marketer’s workhorse – it is flexible, engaging, and provides tremendous return on investment (ROI). According to the recent article, Email remains the best digital channel for ROI, by Econsultancy, “email marketing was ranked as the best channel in terms of return on investment”. The article continues, “companies are attributing 23% of their total sales to the email marketing channel, compared to 18% in 2013.” Clearly companies value email marketing and are working hard to improve results.

What if I told you email marketers are throwing away leads every day – leads that could result in significant increases in leads and pipeline?

It’s true, and many of us know we’re doing it. (For you rookies, like me a couple of years ago, here comes the secret source of targeted leads.) Demand generation teams know that email campaigns produce lots of response email – the majority of which are automated responses. Examples include “out of office” and “no longer with the company” emails. These response emails are filled with leads and other business intelligence.

Gain Leads through Out-of-Office (OOO)

Do the Math. The math tells us that U.S. workers are away from the office 5.8% of the year. With 5,000 contacts and a $5K ASP, that’s $200K+ in potential Opportunity lost.

Mining out-of-office (OOO) emails can significantly improve your lead volume and, more importantly, your revenue! Let’s take a look at some numbers to get a feel for the scale of this improvement.

According to the Bureau of Labor Statistics, U.S. workers had an average job tenure of 4.6 years in 2012. Combining this with information from the Society for Human Resource Management 2004 SHRM Benefits Survey, the average U.S. worker with 4 – 5 years of tenure receives between 18 and 21 paid days off each year. Conservatively, let’s say the average U.S. worker receives 15 such days – one week of vacation and the standard ten federal holidays.

The math tells us that U.S. workers are away from the office 5.8% of the year. Let’s keep the math simple and use 5%. Another important statistic is the number of leads that can be found in each out-of-office email. LeadGnome customers receive a lead from an out-of-office email 58% of the time. In other words more than one lead for every two out-of-office email.

Let’s say you send out 10,000 emails per month – two campaigns, each sent to 5,000 contacts (or maybe you have nurturing programs). If 5% generate out-of-office auto-response emails, then you receive 500 out-of-office emails per month. Assuming one lead for every two OOO emails, then you receive 250 additional leads each month. This is 3,000 additional leads per year! Let’s go a step further and say that your average sales price is $5,000. With an industry average top-of-funnel (ToF) to close rate of between 0.5% and 1.5%, you have the potential to close an additional $225,000. All this from data you have access to, but are not effectively leveraging.

Mining Information Manually

So the next question is: What would it take to mine this information manually? Let’s take a look at the process:

  1. First, someone has to sift through all of the email in your campaign response inbox to find an out-of-office email. This is time consuming and inefficient (many out-of-office emails are easily missed).
    1. Unfortunately, your campaign response inbox is filled with all kinds of emails – out-of-office, no longer with company, actual replies from customers wanting to engage, opt-out requests, bounces, etc. – it’s a noisy place!
    2. And since not all out-of-office use this term in the Subject of the email, it is very difficult to find them by first inspection.
  2. Next, the out-of-office email needs to be read and the relevant information captured. To do this, a person usually copies the data (or cuts ‘n pastes) into a spreadsheet. This is slow, tedious, and error prone.
    1. Lead data appears in out-of-office emails to varying degrees. Here’s what I’ve typically seen – I’ll use very rare, rare, often, very often to rank the appearance of each data element.
    2. Name – very often
    3. Email address – often
    4. Company – rare
    5. Phone number – very rare
    6. Title – very rare
  3. Finally, someone needs to upload this information into your email service provider, marketing automation solution, or customer relationship management system.

Because email address is a minimum requirement for a lead record (e.g., newsletter registration forms typically require just an email address), but are not always provided in out-of-office emails, such potential leads must be discarded. This and other shortcomings in the process tend to limit the efficiency and effectiveness of manually mining out-of-office emails. Even though there are limitations to what can be accomplished by this manual process, the results can add up quickly.


I encourage you to take advantage of out-of-office emails – they will deliver significant value to your company if you are able to mine them. Note that both frequency of email campaigns and the target audience size of each campaign directly affect the return you can expect from mining out-of-office emails.

Of course, you will need to dedicate team members to execute this process. However, many marketing teams are fairly lean, so this may not be possible. If this is true for your team, then you might consider the LeadGnome automated service. My next blog post will focus on calculating the return on investment and cost benefits from using the LeadGnome service versus human resources. In the meantime, if you’d like to learn more about the LeadGnome service, please feel free to contact me at

The post Stop Throwing Away Targeted Leads! appeared first on RevEngine Marketing.

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