As big as it is today, e-commerce has had a quaint run.
From anxiety over online identity theft and credit card fraud, the hassle of returning unwanted goods, to the high cost of shipping associated with online purchase.
But e-commerce has had time to grow and develop over the years through hard-learned lessons and new inventions.
One factor that has played a key role in this growth is the rise of Data Science.
Data engineers and scientists have made important impacts across various industries over the years.
“Solutions to the challenges of our future increasingly link back to data and data science” according to Stanford Engineering Dean, Persis Drell.
A study by Forbes Insights revealed that several industries are depending on department specific data and analytics to get strategies off the ground, the majority of which end up being successful.
So one thing is clear:
Data and analytics have a huge stake across many sectors.
But for e-commerce, data is a must.
Because data is like air for e-commerce.
Let’s get one thing straight:
Whether good data or bad ones; e-commerce need data to function.
In the hands of the right engineer, accurate data can easily be a powerful tool that will capture plenty leads and eventually drive revenue to new heights.
And many of the e-commerce giants must know this because they are harnessing the power of data to generate value for their business.
Talk about a company that has embraced cutting-edge technology to provide quality services; Amazon is the obvious model example of an e-commerce company that is personalizing every interaction by taking advantage of the insane amount of customer data it has access to.
The company has deployed these data to optimize pricing, advertisement, supply chain, and even diminish fraud.
Amazon has successfully built an empire that provide goods faster and cheaper to their customers and the company’s consistently positive reports show that they are on the right track.
And it’s not just Amazon.
Take Nordstrom, their data engineers came up with a system of monitoring customer’s habit and behavior using Wi-Fi.
With the data obtained, the fashion giants have successfully taken a trip into their customer’s shopping trend, thereby allowing them to optimize personalized data and improve customer service overall.
So how exactly are data engineers and scientists helping these e-commerce biggies to drive more sales and experience success in their business?
Let’s find out.
1. Improved Search
If data engineers have succeeded in anywhere, it is in the area of search. Designers then, are required to implement site design based on their input.
As easy as it sounds, giving users what they want and need when they search used to be tricky for e-commerce businesses.
Previously, if users made use of the search bar to find a product on an e-commerce platform, they did so by entering their preferred word for that product, with the hopes that the e-commerce operator has inserted that same word at the backend of the site to match their query.
But data engineers have made this scenario a lot more flexible.
For example, if you search for a product on now eBay with a specific keyword, the system searches the database for that keyword, as well as synonyms in nouns, and similar phrases used for that type of product.
This, coupled with the the website score tracker the company integrates to its system has made search 10x better.
2. Personalized Recommendations
Data scientists and engineers are helping e-commerce biggies to know more about their customers.
They take the data they have gathered from your previous activities to predict what you’d like to buy and make recommendations for you.
The goal is clear:
The more they know about you, the better they can predict what you’d like.
Companies like Netflix and Amazon make use of a system called ‘collaborative filtering’.
They paint a picture of a user based on shopping patterns and behaviors and recommend other products to that user based on what people with the same profile have purchased.
3. Price Optimization
Customers are always out to get the best deal of a product as often as possible and data scientists monitor price management closely.
Most of these e-commerce giants utilize data review to discover opportunities to drive down prices to a certain point during certain seasons.
For example, Amazon prices a best selling product down during a specific holiday to encourage increased spending that period.
Its price may not be the lowest amongst vendors that offer the same product that period, but dropping the price of such popular item at that time of the year might promote the perception that it sells the product at the cheapest price.
4. Anticipatory Shipping
According to EKN research, 80% of e-commerce companies lag behind Amazon in data maturity, and anticipatory shipping is one area that thrusts Amazon ahead.
Analysts have described this as the closest e-commerce can come to a crystal ball, and we can see why.
What does anticipatory shipping mean?
It means exactly what it sounds like. It is a system that uses an algorithm to ship products before customers have even placed an order.
Amazon uses this algorithm-based system to ship products to different geographical destinations without specifying the final delivery address as at the time of shipping.
Doing it not only benefits the customer, but it also helps the company save cost.
The executive director of the Supply Chain and Logistics Institute, Donald Ratliff, reveals that this system provides the company with an opportunity to reduce costs by 10% to 40%.
So imagine a company as big as Amazon cutting cost by up to 40 percent.
Final Words –
Data is power.
In today’s world, data scientists are using data to uncover user behavior, to help these companies prove or disprove new trends.
This shows that data is one important catalyst that will propel the e-commerce industry further into the realm of technology than was initially projected.
In turn, it will result in a source of new ideas and amazing innovation for these businesses, and will ultimately boost profitability.
And it goes without saying that misinterpreted data can be catastrophic and drive a business into the ground, whereas, accurate data is the key to understanding and targeting the right customers.