It's always interesting to me when so many people take sides on an issue.

Recently, I wrote about how email is going to take a backseat to other forms of digital communication by 2020. Slack and Circuit are two good examples of what might replace it. Many people told me they can't wait for that to happen. They are really sick of processing email every single day and constantly poking through an endless email archive.

Yet, a few took issue with my prognosticating and even resorted to name-calling. It turns out that at least a few of the naysayers run an email marketing company. And, you know what? I think that technique of "spray and pray" is going to go the way of the dodo as well. So they have a whole new reason to fire up that Twitter account (irony alert) starting now.

Here's why blast marketing doesn't work.

Let's say you use MailChimp to queue up your wonderful newsletter about wearable tech news. Despite the misspelled words and bad grammar, you're confident you can reach at least a few dozen people with your words of wisdom. So you fire it up. Sadly, what often happens with this technique--especially if the recipients didn't actually request to be on your list--is that it just goes unnoticed. I'm not saying it falls into a spam folder or the dreaded Promotions tab in Gmail. I mean no one is paying attention to it anymore. It's not being read.

(By the way, I'm not dissing MailChimp--it's a great tool for reaching a targeted audience and has some amazing features. I'm talking about the blast email approach to the masses.)

Isn't there a better way of reaching people other than following the lead of farmers who spray insecticide all over a farm field in hopes of killing a few pesky bugs? Maybe that's the only option, but I've heard of a much more intelligent way to reach the masses.

One is from a recruitment software company called Gild. They recently upgraded their platform to use machine learning. When you search for a job, you tap into their private database of people who might be candidates for the job (this includes those actively looking for a job and those who are not but fit the job profile). Recruiters can see the difference between a job description for a "software engineer" or a "software designer" and how many people fit that description in a specific area. It's like matching up your "needle" with a smaller haystack.

When the recruiter starts emailing people, they know the recipient is much more likely to respond. The app can even cull data directly from LinkedIn. (Now, if the app connected directly to LinkedIn and bypassed email altogether, we'd be living in the year 2020. That's really a future vision for how this would all bypass email.) It's not an email blast. It's a strategy to find the right people for a job and actually hire someone.

Here's an example from my own field. When I first started out as a writer, I used to blast out emails to random editors. In a few cases, it would work OK. I know many people have figured out how to make it work, but I always felt a little weird about sending so many blasts to people I didn't even know or care about. For at least the last 10 years, I've avoided doing that. It's not like I use machine learning to automate the process, but I tend to contact people more directly, one by one, with a highly customized email that's just for that single person.

Machine learning will eventually replace mass email blasts entirely. This approach involves figuring out who you really need to reach, who matches a profile, and who will respond. They will even use techniques of predictive intelligence to let you know who is the right candidate for a pitch. Machine learning will essentially predict the success of your "campaign" before you even send it. Heck, you might as well just contact the ones who will respond directly and skip the blast.

I'm excited to see what other companies like Gild come up with for making machine learning much more effective. It will cut hours out of our day. The results will be stellar. Spray and pray will finally become something only a farmer could love.