Machine Learning Improves E-Commerce Site Search

machine-learning-search

Ever wonder why most people are fascinated with psychics and fortune tellers? They can foresee the future even though there is no scientific evidence. We think that if we know what’s coming, we can prepare for better or worse.

Businesses are no different. The capability to predict trends will help them optimize the supply chain, marketing, product sourcing, and customer acquisition.

Fortunately, forecasting is within reach, not through a crystal ball but from analyzing the massive data through machine learning. But what is machine learning?

American computer pioneer, Arthur Lee Samuel came up with the phrase “machine learning” in 1959 to describe the ability of computers to self-learn without being programmed by humans. Mr. Samuel invented the first machine learning program, The Samuel Checkers-Playing Program and proved that artificial intelligence (AI) was possible.

Is Machine Learning Taking Over?

Fast forward to today where machines are taking over. Not quite as ominous as Skynet in the Terminator movies, but the latest machine learning computers provide reliable predictions on many levels, in real time, without human intervention.

Machine learning uses complex data analysis to improve performance and predictions without further programming.

It relies on massive data sources to identify patterns of predictability making it ideal for e-commerce. The extensive data of e-commerce businesses allow the computers to learn more about shoppers, their history, habits and make accurate behavioral predictions.

Machine learning is here to stay. E-commerce already uses powerful applications of machine learning technology. Businesses benefit from pricing optimization, customer support in the form of “chatbots,” fraud protection, supply and demand forecasting, and customer segmentation.

However, our focus on this article will be on machine learning site search because every shopper's journey begins with a search. Poor search results mean shoppers can’t find what they want and will go elsewhere.

The machines are taking over when it comes to on-site searching, but that’s a good thing as you’ll see.

Web Development CTA

 

Importance of Relevant Site Search

When customers begin a search on your site, they are in the late stages of the buying cycle with high intent to making a purchase. Ensuring that a customer finds exactly what they came for is essential to providing quality customer experience and closing the sale.

Machine learning is instrumental in improving the relevance of each new search for every customer. 30% of visitors use site search and according to Souq.com, “visitors using search contributed 13.8% of the revenues” on e-commerce sites. In other words, quality search results mean better sales.

The improvement to searching is not just limited to the exact match. Machine learning can display for a variety of related products, increasing chances for additional sales.

How Machine Learning Improves E-commerce Site-Searches

Older, traditional site searches are called “recommender” or “product recommendation” searches. They have little imagination and deliver only results focused on the keyword. Some search apps can’t understand misspelled words returning no results at all. The customer must try again or go elsewhere.

In an SLI study, 73% of customers left a site after 2 minutes if they hadn’t yet found what they were searching for. Mobile users are even less patient.

Product searches enhanced with machine learning returned a wider choice of results to each query. They map products and interconnect them in new ways. For example, a search for “cat food” returns cat food wet, dry, mat, bowls, container, dispenser, lid and canned. Adding one of those extra keywords will yield more related choices. The program improves the search results based on the preferences clicked by customers.

E-commerce searches with machine learning combine the keyword plus data such as click rates, conversion rates, customer ratings, inventory, and margins. The search relevancy increases with each search multiplied by thousands of searches per day.

Not only can machine learning identify complex patterns throughout a catalog, but it can recognize behavioral models as well. Also known as predictive analytics, it is the science of knowing what is going on in a customer’s life motivating them to buy.

For example, if a shopper purchases an item of newborn baby clothes, that information could be set up to trigger coupons or promotions for all baby accessories targeted to that shopper.

With machine learning the customer has a wider variety of results, paving the way for larger purchases and impulse buying.

Other Ways E-commerce Has Harnessed Machine Learning

Machine learning has improved systems and information throughout the e-commerce environment. Chatbots have taken over human customer service roles by interacting with customers verbally. They can provide answers to FAQs and improve responses through self-teaching.

On-site merchandising has benefited by computer predictions based on customer buying and search habits. Merchandisers can increase the relevance of recommended and related products, staying ahead of the trend instead of being caught behind.

E-commerce uses the data from machine learning for predictive marketing. They send push notices targeting specific customers for promotions. 80% of customers actually enjoy receiving these product recommendation notifications, and according to an eMarketer survey, these highly targeted recommendations can have an open rate of 90%.

Machine Learning Improves Conversions

Machine learning provides the best in search relevance and usability for visitors

BigCommerce recommends that companies who focus on “leveraging the experience of shoppers currently on your site” has shown an increase of:

  • store revenue by 300%,
  • conversions by 150%
  • AOV (average order value) by 50%.

Fast growing eCommerce merchants can’t afford to offer online shoppers a mediocre site search. If your current eCommerce site search solution isn’t cutting it, your bottom line is probably at a mere 40% of its true potential.

InstantSearch+

With machine learning, it gets better over time. The algorithms and data improve automatically with every search. The benefits for e-commerce businesses are two-fold.

First, it drives sales by giving customers a better shopping experience personalized to the shopper’s needs and history, creating more upsell opportunities.

Customers get a more comprehensive yet targeted range of buying options. It’s just like directing someone to the fruit section of a grocery store if they are looking for fresh apples. They will typically buy more once they see the choices.

Secondly, machine learning provides accurate forecasting data helping owners make better business decisions.

ML programs give greater control and insight over:

  • Forecasting product demand and trends
  • Customer segmentation
  • Accurate estimates for packing and shipping costs
  • Recognizing or avoiding inventory issues
  • Improving Marketing campaign targeting and effectiveness

It adds a new dimension of intelligence that both customers and management will appreciate. The machines may be taking over, but it’s for the better. Machine learning fulfills your business and customer’s needs in ways that you haven’t even imagined.

Image credit: Amazon / amazon.com

 

Web Development CTA

You've come down this far for a reason.