How to Put Your Remarketing Strategies on Steroids with Predictive Buyer Scoring


Not using Machine Learning + Predictive Buyer Scoring in your remarketing efforts is like bringing a knife to a gun fight.  
Most remarketing strategies in e-commerce today involve getting bombarded with pop-ups once you step foot on someone’s website.  This is even worse if you are a known visitor, getting pelted with emails about special offers on products that may not match your buying interests.  Have you ever watched a little kid’s soccer game?
Notice how ALL the kids (sometimes even the goalie) all gravitate towards the ball?  Let’s pretend that the soccer ball in this case is the web visitor and once the ball is live, all the kids start clamoring for it.  As cute and funny this maybe, we all know that this not a viable strategy to win the game.
Notice as the kids get older, progress through middle school, high school, college, and maybe even the pros that their soccer skills evolve.  Their playing style becomes more strategic, passes become more accurate and everyone on the field knows their defined role on the team.   This same principle can directly apply to your remarketing efforts.

How to use a Pro-Level Remarketing Strategies for your Small to Mid-Size Business

Unfortunately, many, many, e-commerce companies use the little kid’s soccer approach with their remarketing protocols instead of something more strategic.  They are content with launching pop-up after pop-up or sending email upon email once someone steps foot on their site.
Maybe they like implementing the little kids’ soccer strategy or possibly they just know any better?  Hopefully it’s the latter, and with advent of Machine Learning and Predictive Buyer Scoring, all e-commerce sites around the globe can now have a pro-level approach with their remarketing strategies.
Consider a website that gets:

  • 10k visits a month
  • 90% – 9k will be anonymous
  • 5% of the anonymous (450) will have purchased
  • 5k anonymous, non-buyers
  • 2% conversion rate

Referring back to my 2nd article, you can now accurately target individually, ALL 8.5k anonymous, non-buyers in REAL-TIME on their propensity to buy your product using Machine Learning + Predictive Buyer Scoring.  Going back to my first article, we can now determine the likely buyers from the ‘window shoppers’ of the coveted red or green shirt and thus act accordingly with relevant messaging where anyone that scores:

  • 90-100 receives 20% off
  • 70-89 receives 10% off
  • 50-69 receives 5%
  • Below 50 – truly just web browsing – no offers needed

What you have here is a targeted approach to pop-up messaging (let’s assume we have 2,125 anonymous visitors in each of the four categories) that will result in increased conversion rates.  Again, this is due to Machine Learning’s analysis of past buying patterns that determine the strength of one’s Predictive Buyer Score.  Let’s say we have a shirt price of $50, and we are able to convert:

  • 20% in the first group (2125×20%=425x$50 = $21.25K)
  • 10% in the second group (2125×10%=212x$50 = $10.6K)
  • 5% in the third group (2125×5%=106x$50 = $5.3K)
  • 0% as this group was truly ‘window shopping’

We now have $37.15K in additional t-shirt revenue with the help of Machine Learning + Predictive Buyer Scoring!!!
Now it doesn’t always have to be a coupon-if you have a product that is tailored to high end consumers, who aren’t sensitive to price, your site could offer product recommendations or reward points. Other examples of high predictive buyer score incentives include:

  • MMA clothing site could offer an exclusive invite to a McGregor-Mayweather watch party at a sports bar
  • Purchase a walker from a toy site for your niece, then six months later get a recommendation for purchase of a tricycle
  • Jeep site could offer free installation if you purchase one of their off-road tires

In essence, the Predictive Buyer Score powered by Machine Learning is a FICO like score on your web visitor’s propensity to purchase. As traditional brick and mortar retail establishments are dying, the world of E-commerce will continue to grow and become ultra-competitive.
Data is king and knowing what to do with it at the right time will separate the men from the boys and determine who will succeed and be in business one year from now. 

Facebook achieved web dominance by riding a business model of understanding users and feeding them tailored content and advertising.

Ask yourself, would you rather be well prepared with the gun or continue using your knife in your e-commerce sales efforts?
What’s your take on using predictive buyer scoring in your retargeting strategies? Tell us below or tweet us!