It is that time – we are at the half way point of the year.  How are your 2017 sales’ goals coming along?

Are you on target (if so congrats)?  If not, what are you doing to reevaluate your game plan and adjust your current strategy?  If you fall in the latter group, don’t worry, it’s only half time and you still have a chance to achieve your goals by adopting recommendations in this article.

Consider these facts from the National Retail Federation:

  • The holiday season can represent as much as 30 percent of annual sales
  • Online holiday sales to increase between 7 and 10 percent to as much as $117 billion during the months of November and December
  • Each year about 40 percent of consumers begin their holiday shopping before Halloween

For most of you reading this article, you are probably a small to mid-size business owner sitting poolside or on the beach drinking a Mai Tai.

However, what you may not know is that many of your competitors are getting a head start and gearing up for the holiday season in July.  Now, I’m not advocating not relaxing or taking vacations especially in the summer, but just keep in mind, your procrastination could cause a slump in sales, or worse yet, the loss of your business.

Wikipedia defines procrastinations as: the avoidance of doing a task that needs to be accomplished.  It is the practice of doing more pleasurable things in place of less pleasurable ones, or carrying out less urgent tasks instead of more urgent ones, thus putting off impending tasks to a later time.

christmas-in-july-machine-learning-predictive-buyer-scoring

Fortunately hope is not lost and now the small-mid size business owners have tools at their disposal that they can implement now to have a successful holiday retail season.

Using Machine Learning and Predictive Buyer Scoring to significantly improve UX

In my third article, I go into detail about how to retarget anonymous visitors on your site to boost conversions via their Predictive Buyer Score.  Remarketing to higher predictive buyer scores is only one piece of the puzzle.

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As an e-commerce shop owner, you do not want to neglect and/or ignore those who fall into the lower half of the predictive buyer score range.  Remember, in that second article, you have 4250 anonymous visitors that scored below a 50%.

They were on your site for a reason; let’s not waste an opportunity to work on improving their Predictive Buyer Score so when they come back to your site, you can convert them from shoppers to buyers.

Here are some parameters that can be selected by Machine Learning algorithms that can positively or negatively affect a Predictive Buyer Score:

  • Page views per visit
  • Total time on all pages
  • Total time spent per session
  • Checkout abandonment rate
  • Total page load time
  • Number of clickable events
  • Domain Lookup Time
  • Redirection Time
  • Server Connection Time
  • Server Response Time
  • Document Interactive Time
  • Document Content Loaded Time

These are a small portion of the numerous inferences that can be used to determine someone’s Predictive Buyer Score using Machine Learning.  Now, as an owner of an e-commerce shop, you want to work on raising your lower 4250 Predictive Buyer Scores mentioned in the second article.

If you focus on improving negatively affected parameters, you could potentially move 20% of the lower ones into a higher category and thus increase your chances for conversion.

Taking a modern day athletic mindset to your e-commerce business

The athletes of yesterday typically just practiced their respective sport during the season and that was it.  Nowadays, the modern day athlete takes a more holistic approach and aside from practicing their sport, will work on strength, conditioning, flexibility etc. to gain an edge against their competitors year round.

The same holistic approach that athletes of today take should directly apply to the modern day e-commerce business owner.   The old days of just using the parameters of clicks and scrolls as well as traditional business intelligence like relying on outcomes to prove a hypothesis will simply not cut it anymore.

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I guarantee that if you apply this mindset that you will lose.  E-commerce business owners must take a well-rounded strategy, using multiple parameters as well as Machine Learning to identify hidden patterns when it comes to attracting and converting clients.

Great news – it’s really not that hard or expensive

While many are starting to believe the power of Machine Learning + Predictive Buyer Scoring to help take their e-commerce sales to the next level, they still may be hesitant due to cost or difficulty of implementation.

With advances in technology, the small-mid size business owner can have their own, personal Machine Learning + Predictive Buyer Scoring ‘data-scientist’ with a quick and simple install of JavaScript code on their site.

Remember, it was ten years ago when the iPhone was launched.  Look at how the capabilities of smartphones affect the way we do things today.  I’ll go out on a limb and say the same thing about Machine Learning and its effects on e-commerce, healthcare, finance, and many other industries.  It is starting to revolutionize what we do today and beyond!

How are you preparing for the holiday season? Is July too early to start? Tell us below or tweet us!

Want to know even more about Predictive Buyer Scoring and Machine Learning?

Never fear- there’s a webinar on the subject tomorrow! Spots are limited, so grab them before it fills up!