Source: analyticsinsight.net
It’s pretty safe to say that as the world of e-commerce grows and expands with the constantly changing behaviors of consumers, so does the data that is stored.
Have you ever noticed how you may do a Google search of a particular type of product and then the next time you log into your social media account, you start to see that same type of product in ads from different businesses? Conspiracy theorists like to say that “we’re being watched,” and in a sense, we kind of are being watched, but not in a “G-14 classified” kind of way.
This Deja Vu type of shopping experience is no coincidence but more so one of the results of big data in e-commerce marketing. And big data isn’t something that’s just pulled from your web browser histories either. Big data is collected from things like
- Abandoned shopping carts
- Road cameras
- Geolocation services
- Social media activity
- Transaction receipts
Typically, big data is divided into three categories: transactional, social, and machine data.
Transactional Data is exactly what it says it is. Any types of online transactions you’ve made, big data analyzes the behaviors you have before making that transaction, leaving companies to use that information to improve your buying experience.
Social Data stems from your online social activity. “Likes,” “Loves,” “Comments,” and “Tweets” are actions that provide e-commerce companies with insight into certain interests and behaviors of their ideal customers. Even video uploads and “number of views” deeply influence e-commerce marketing analytics.
Machine Data is information that is collected from various types of equipment and sensors that keep track of various patterns and behaviors of users.
E-commerce businesses are leveraging off of big data analytics as a way to have a better understanding of their customers and their shopping behaviors. The data that they collect and analyze allows them to “custom-fit” all their marketing efforts to the specific preferences of their customers, train staff on tailored customer service tactics to meet their customers’ needs, and the creation of products their customers actually want to buy.
To be an e-commerce business owner trying to keep up with the ever-changing trends and behaviors of their customers is a job all in itself. That reason alone is why so many e-commerce business owners turn to sites like Shopify where they can go to the marketplace to hire industry experts that have expertise specifically for e-commerce marketing.
So, to answer the question of how is big data impacting e-commerce marketing in 2020? The answer would be that it’s impacting e-commerce marketing in a major way. According to businesswire.com, global big data analytics in retail was reported at $3.45 billion in 2018… By the end of 2024, the market is expected to reach 10.94 billion.
Because of the boom in e-commerce and the increase in online shopping, retailers are using big data as effective efforts to stay competitive and relevant in their market.
Let’s dive deeper into just how big data is fostering a major impact on e-commerce marketing.
Big Data’s Impact on E-Commerce Marketing in 2020
A More Personalized Shopping Experience
E-commerce businesses are leaps and bounds above brick-and-mortar stores when it comes to creating a more personalized shopping experience for customers. Sure, having a greeter at the front of a store is nice, but all too often, they can be a bit of a nuisance at times.
E-commerce businesses have a plethora of predictive analytic tools to give them a better idea of current and future behaviors of customers. Everything from tracking clicks per page to tracking the length of time between a visitor’s journey from your home page and checkout, big data is providing several different sources of data.
Data scientists are doing even more research now to find new sources of data to provide customers with a more personalized shopping experience. New on the horizon, text analytics is one of the newest sources of data being utilized in lots of businesses.
Everything from analyzing the texts in comments on business websites to reviews on third-party sites, this is a source of data that can take information that customers are saying and apply it as an improvement measure to give customers what they really want.
Increase in Cross-Border Sales
People all over the world love to shop, and big data has made it possible for e-commerce businesses to be more accommodating to neighboring countries. Based on the products you’re selling, big data can tell you where your products are competitive or unique, and you can start selling in that market.
Just make sure that you localize the products you’re selling for those geographic locations. That means having language options, making sure your prices and sizes are converted, and that you’ve set the shipping costs properly.
Impeccable Customer Service
Happy customers are your key source in turning customers into loyal customers, and the way to keep customers happy is through impeccable customer service. Big data helps e-commerce businesses provide great customer service in detecting your brand’s perception on social media and customer satisfaction levels (if you offer customers to rate you). In fact, big data can tell you exactly when a customers’ perception of your business shifted!
E-commerce businesses can take this information and use it to make appropriate changes to their site and even use it to train employees on how to interact with customers, whether it’s through chat box interactions or simply making the checkout process more convenient.
Personalized Marketing
Long gone are the days where you can get away with emails that say things like “Dear Valued Customer”… especially when people are loyal customers to a particular online store. We are in the era of sophisticated shoppers with high expectations, and when your customers are sophisticated, that means that your marketing efforts need to get sophisticated as well.
Big data has transformed digital marketing in ways to make the customer feel like a particular store really cares about their taste, interests, and preferences. For example, if you have a customer that only buys purses from your site, you may want to send her emails when new inventories or collections of purses arrive. Sending her an email about shoes might make her delete the email altogether.