Big data is transforming the shopping experience. So, what is big data analytics, and why is it a big deal for e-commerce?
Mankind has always met life’s challenges with resourceful determination. We foraged in the Stone Age, farmed in the Agrarian Age, manufactured in the Industrial Age. Now, in the Information Age, we process data.
The exponential growth of digital information in recent years has opened up endless possibilities to store, consume, and analyze data. This deluge of information is known as ‘big data’—and it has huge implications for online retail.
What is Big Data?
Most businesses make use of just a small fraction of the data at their disposal.
‘Big data’ refers to the sum total of the structured and unstructured data.
Data with structure includes easily searchable content, like text and numbers. Data without structure is content that is not easy to search, like as audio, video, and social media posts.
The vast majority of big data remains locked within software, systems, and infrastructure. As such, its owners often unaware of the value of their untapped resource.
What are Big Data Analytics?
As computational power and storage capabilities have improved, many businesses are using ingenious methods to extract insight from their ever-growing mountain of big data.
Key to this process are data annotation, data labeling, and data tagging. These three terms describe the same process: the classification of each piece of data so that it can be analyzed and optimized to deliver value.
What kinds of information can you tag for big data analytics?
- Text documents
- Images and even parts of images
- Objects within videos, audio, emails, and phone calls
- Web browsing records
- Internet chats and text messages
- Location information
- Business data such as ad-buying statistics, social media posts, sensors.
And the list goes on. You can see why they call it big data.
Tagging every element of this information makes it recognizable to artificial intelligence (AI) algorithms. AI can then correlate the results, identify patterns, interpret trends, and calculate predictions.
As these forecasts are based on an extensive analysis of solid evidence, the level of accuracy is much higher than with predictions based on more speculative methods.
How is Big Data Shaping E-commerce?
Big data analytics helps retail companies make strategic decisions based on detailed insight into the behavior of existing and prospective customers.
Few retail sectors produce more data than e-commerce, so it is no surprise that e-commerce is one of the early beneficiaries of this new science.
Here, then, are five ways big data can be used to deliver a competitive advantage for an online retailer.
1. Demand Forecasting
Whatever is being sold online, from clothing to kitchen appliances, harmonizing supply and demand is a constant challenge.
Placing a bulk order with a manufacturer for, say, white T-shirts or bread-makers, only to discover that black long-sleeve tops and waffle-makers are the next must-have items, can be catastrophic in this highly competitive marketplace.
Using big data to detect trends before they become mainstream reduces the risk of missed sales and a warehouse full of unwanted stock.
2. Product Development
Big data analytics mitigates the risks associated with the long-term planning of new goods and services.
By capturing the plethora of information that reveal shifting customer preferences and upcoming innovations in the industry, businesses can develop a far-sighted outlook that allows them to deploy staff, engage suppliers and have the right offer ready for their customers at the right time.
3. Pricing Strategy
An e-commerce business that stocks thousands of items would struggle to maintain a manual pricing strategy designed to compare prices with those set by its competitors. With big data, however, pricing action is taken for a specific product in minutes.
A continual analysis of the many variables that can influence price—including wholesale price, customer willingness to pay, size of the market, and competitor pricing—enable a tailored and automated pricing strategy for each individual product.
4. Sales Generation
Unlike with traditional brick-and-mortar retail, online consumers skip back and forth between different stores, visit review sites, and explore product recommendations before filling their cart.
Then, they might check out and leave, abandon the cart altogether, or return to make the purchase through another device.
Big data captures and analyzes consumer behavior at every stage of the shopping journey, whether browsing turns into buying or not.
These data-rich profiles therefore allow retailers to generate a dynamic, personalized customer experience filled with products, messaging, and recommendations designed to maximize sales and seize opportunities for cross-selling and up-selling.
5. Customer Service
Beyond the products and shopping experience, it’s often customer service that shapes a shopper’s perception of an online retailer. Let’s face it, there are a few sharks out there, and customer service is how the good guys can stand out from the crowd, establish a good reputation, and build customer loyalty.
Big data analytics allows retailers to act on the real-time monitoring of response speeds, delivery times and customer feedback. As a result, customer service teams can increase their speed and effectiveness, and optimize the entire customer experience.
How to Start with Big Data in E-Commerce
As the company’s Nordics Region Managing Director, Lea Backhurst, says:
The ongoing expansion of big data analytics is changing the way we think of shopping. The concept of visiting an online marketplace to browse and buy is gradually being replaced by something more like having an online personal shopper who knows exactly what you want from your entire shopping experience, often before you do yourself.
So, to learn more about Summa Linguae’s e-commerce capabilities, contact us today.
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