An entire industry was built around California’s gold rush.
Devices were created to make the mining process easier, encouraging those who were worried about the physical labor to visit California and take a stab at the gold mining trade anyway.
If you were down on your luck and needed a new way to make money, why wouldn’t you go to California?
The same can be said for the ecommerce industry in 2015, going into 2016.
A mining frenzy is imminent, but it’s not exactly for a natural resource like gold.
Instead, retailers are mining for something that can be arguably just as valuable: competitor and customer data.
Data mining is the future of retail, and I’ll explain why. But first, let’s go over the basics:
What is data mining?
Data mining is the act of scanning the web for data points and collecting them to use in your business.
This data can be anything, from competitor prices to customer cart abandonment rates.
As long as you’re monitoring and collecting, it’s considered data mining. It can sound daunting, but it’s actually become quite simplified over the past couple of years.
Since you aren’t going to be able to use a pickaxe or a pan on your computer, how will you be able to mine the best data?
Well there are two many ways of doing it: manually, and automatically.
Manually mining for data is essentially reviewing data on competitor sites over time, collecting it, and analyzing it to make decisions.
Automating the entire process will provide your business with more accuracy and save some of your employees major headaches.
The reality of the ecommerce landscape is that prices change throughout the day, and if you’re scanning dozens of websites you’re destined to miss some changes.
It’s because of this unpredictable nature that data mining is becoming a necessity for the future of retail.
Mining customer data
Your customer data can teach you how to improve your store’s merchandising.
Data like heat maps, cart abandonment rates, and more can help you optimize and improve your store’s conversion rates.
If your cart abandonment rate is high, you can use that knowledge to improve the layout of your checkout screen.
Or, you can use it as a way to measure the impact of shipping costs on the checkout decision.
Roughly 45% of shoppers are more likely to shop at a store that offers personalized recommendations.
You can make these recommendations with a data mining technique known as basket analysis.
This measures a customer’s cart contents so the next time they visit you can offer them similar items that they might be interested in.
Recommendations are becoming the norm in ecommerce, and you don’t want to get left behind.
If a returning customer is visiting your store and abandons their cart, you can use their previously mined data to email them discounts and win them back.
Retargeting ads have been nearly twice as effective as other marketing attempts, and their success is showing no signs of slowing down.
But data mining goes beyond customer data, and is also applicable when it comes to competitor data.
Mining competitor data
Your competitors, especially Amazon, have already started mining for your data.
This includes inventory assortment, prices, stock levels, customer reviews, and more.
Data mining is becoming more and more common among retailers of all sizes, especially more sophisticated price intelligence platforms.
More than just analyzing your competitors’ prices, these platforms can pull assortment, reviews, and more. This helps you optimize your inventory assortment, and helps you carry items that will offer you a competitive advantage.
The frequency of mining is important as well. As often as 15 minutes is a good idea because competitors are constantly changing their prices.
Amazon has even changed the price of the King James Bible numerous times throughout the years. This is all possible because of data mining.
The future of retail
Online retail’s future lies within data mining because it allows brands to provide consumers with unique, personalized shopping experiences.
On top of that, data mining helps retailers compete against other sellers of any size, levelling the retail playing field and improving the pricing strategies of those who mine.
The success data mining offers indicates that we’ve only seen the tip of the iceberg for this tactic.