Why to use predictive modeling for Email Marketing

Today most e-mail marketers have started using predictive modeling to better target customers for particular products/services.. It is because predictive modeling in marketing activities can deliver data-driven predictions that you can use to influence the future.

Traditional View of Marketing

In e-mail marketing, the cost incurred for every thousand potential customers who view the advertisement (CPM) is $7-8 or less. Many marketers believe that it is not worth building a predictive marketing model as the savings from a predictive model is less or negligible considering it takes time and money to hire predictive modelers for model building. Rather, predictive modeling should be used in Direct Mail where the CPM is $500- 600 or more.
Mail to all the customers. Someone is going to buy.
Modern View of Marketing
Mail to only those customers who are interested and more likely to buy your product.
Spam affects Email Marketing Campaign! 
Spam is commercial email, junk mail or bulk mail that has not been requested by the recipient. A relevant email to a top customer can get lost in the spam if you mail him too frequent and he can also label you a "spammer".

Permission-based emails is a latest weapon to avoid being labeled a spammer!
Permission-based emails are requested, anticipated, personal, relevant and verified by user.

Reasons to use Predictive Modeling for Email Marketing

1. The cost of acquiring a permission based subscriber e-mail address is high (between $10 and $40).

2. A relevant email to a valued customer can get lost in the spam if you mail too frequent. Many users unsubscribe or delete e-mails without reading them and can also label you a spammer and unsubscribe for future email updates.

3. Unsubscribe rates are often 2-3% or more per month. If the value of each subscriber is $15- $25 and the number of subscribers are more than 500k, the annual loss from unsubscribers can reach up to millions of dollars.

4. It is not required to build predictive model every time you send an email campaign. You can build a model once and perform scoring from the next time. Note - It is important to check the stability of the model once in a quarter by comparing the current scoring with the predicted probability from training data set.
About Author:

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 7 years of experience in data science and predictive modeling. During his tenure, he has worked with global clients in various domains like banking, Telecom, HR and Health Insurance.

While I love having friends who agree, I only learn from those who don't.

Let's Get Connected: Email | LinkedIn

Get Free Email Updates :
*Please confirm your email address by clicking on the link sent to your Email*
Related Posts:
19 Responses to "Why to use predictive modeling for Email Marketing"
  1. Nice to be visiting your blog again, it has been months for me. Well this article that i've been waited for so long. I need this article to complete my assignment in the college, and it has same topic with your article. Thanks, great share.
    social media marketing westchester New York

  2. This comment has been removed by a blog administrator.

  3. Much obliged to you so much Love your site.. Hotmail email login

  4. leather jackets can really make you look good, they also make you feel warm and comfortable** clique aqui

  5. Your work is great and I welcome you and bouncing for some more useful posts. Much obliged to you for sharing incredible data to us. hotmail login

  6. Thank you so much for sharing this worth able content with us. The concept taken here will be useful for my future programs and i will surely implement them in my study. Keep blogging article like this.
    Zuan education

  7. I'm certainly very happy to read this blog site posts which carries plenty of helpful data, thanks for providing such information.
    profit engine bonus

  8. Awesome article! Predictive analytics modeling is to engage your customers and increase revenue. Predictive models can help segment email list and personalize your email messages based on customer.


  9. Pretty blog, so many ideas in a single site, thanks for the informative article, keep updating more article.
    Digital marketing course in chennai

  10. I’m glad to locate so much of informative data in your blog. https://salesfunnelcreation.wordpress.com

  11. i would love to have some debt consolidation and have financial freedom in the years to come:: acheter base de donnee email

  12. Oh my goodness! an excellent article dude. Thank you Nonetheless I will be experiencing trouble with ur rss . Don’t know why Struggling to sign up to it. Perhaps there is any person finding identical rss difficulty? Anyone who knows kindly respond. Thnkx www.hotmail.com

  13. Thank you so much for the post you do. I like your post and all you share with us is up to date and quite informative, i would like to bookmark the page so i can come here again to read you, as you have done a wonderful job. msn hotmail login

  14. Email marketing is very important. But, you need to have high-quality email lists, which means they are cleaned of bounce emails. You can clean your lists with the help of an email verifier.

  15. Yes i am totally agreed with this article and i just want say that this article is very nice and very informative article.I will make sure to be reading your blog more. You made a good point but I can't help but wonder, what about the other side? !!!!!!THANKS!!!!!! sgfservices

  16. Be that as it may, First Things First... You need to get it out of your head that you must have a considerable measure of cash with a specific end goal to legitimately advertise your organization. http://eugene-and-louise-bakery.be/

  17. Thanks a lot for taking a few minutes to line all of this out for people like us. This kind of article ended up being extremely helpful to me. Mailbird

  18. Most will simply use the default software program that comes with the purchase of their computer. Email made


Next → ← Prev