How to Use Big Data for the eCommerce Business? 20 Ways from 15 Experts
In the information-driven world, big data plays an essential role in revolutionizing the eCommerce industry. Online businesses can pull in the data from web server logs, geolocation services, abandoned shopping carts, transaction receipts, social media activity, sensors connected to the IoT (internet of things), etc. Often such a large amount of information can be overwhelming. But, with big data, eCommerce companies can have valuable and actionable insights that can help with acquiring and retaining customers, increasing profitability.
Also, when businesses smartly leverage data from the competitors operating inside their circle of competence, they can have a deeper understanding of the products, market, and clientele. Accordingly, companies can modify their existing products or create new ones that meet customer needs. They can also tailor their marketing to the customers' preferences and ensure that their brand provides the level of service they expect. With technology and human behavior evolving constantly and the competition increasing multifold, big data is nothing less than an asset for eCommerce companies to reach a global audience.
GoodFirms surveyed 102 eCommerce owners and experts worldwide to find a variety of ways to use big data in eCommerce.
What is Big Data?
Big data is an immense volume of structured, unstructured, and semi-structured data that can be mined for information. It is used by various industries, such as eCommerce, medical, energy, financial, manufacturing, transportation, and even governments.
Vipin Chahal, Founder of Return Policy Guide, says, "Big data is a field that gets ways to dissect, methodically remove data from complex information. It also helps manage informational indexes that are excessively huge to be managed by conventional information preparing application programming. Big data permits web-based business organizations to comprehend clients better through client conduct examination."
The Application of Big Data in eCommerce
eCommerce has seen 10 years of growth happening in just 90 days during the height of the COVID-19 pandemic. And as the popularity of online shopping keeps growing, a tremendous amount of data is generated, which further propels the market's growth. Contextual and programmatic advertising, one of the upcoming trends in this industry, uses these data sets to identify target customers.
For example, people Google search for a specific product, and then the next time they log into their social media accounts, they start seeing the ad of the same type of product from different businesses.
Customer's name, date of birth, address, transactions, amounts, prices, loyalty points, products, etc., constitute structured data for eCommerce. While social posts, tweets, likes, product reviews, images, etc., are semi-structured data.
Sam Underwood, VP of Strategy at Futurety, believes that big data has enormous benefits for eCommerce, primarily because it's easy to apply to the industry. He says, "By definition, eCommerce businesses generate a lot of data: clicks, impressions, bounce rates, add-to-cart rates, conversion rates, the list goes on and on. Using all of these data points often provides us with an airtight story to share with eCommerce leaders to help diagnose product wins/opportunities, ideal audiences, and much more."
Analytics Tools for Big Data in eCommerce
Traditional databases fail to extract, transform, and analyze the humongous raw data. By assessing, purifying, changing, and demonstrating information, data analytics help reach meaningful and authenticated conclusions. And so, big data analytics tools are extensively used by data scientists/analysts, statisticians, predictive modelers, and other analytics professionals to make sense of terabytes of data.
Google Analytics works for Arnas Vasiliauskas, Chief Innovation & Product Officer at CarVertical, to record the customers' entire journey. Right from researching a product even before they enter an eCommerce platform to purchasing.
He says, "Google searching is simply the most basic initial consumer instinct when they are considering their options or looking around other brand's websites for comparison before they make a decision. Google Analytics gives you a peek of the customer's thinking and decision-making process for businesses to land an in-depth understanding of their market's behavior."
How to Use Big Data to Create Personalized Experiences for Customers?
Data certainly rules the world. But, it is the Age of the Customer as well. So, to triumph the customer experience game, eCommerce companies need to capitalize on data.
1) To improve user interface
"Big data is more than just the data. It also includes an efficient means of data interpretation so that managers can make appropriate business decisions," says Nate Nead, CEO at DEV.co.
He says, "When it comes to improving UX/UI, big data can tell us various things like,
- What buttons are most often clicked,
- Which colors perform better,
- What time of day do my customer purchase,
- When are my promotional emails most helpful,
- What page designs convert best,
- What product SKUs(Stock-Keeping Units) are most popular and most likely to be purchased in tandem.
The answers to all these questions not only require access to the data, but the answers are often not what we think. As the saying goes, people assume, and people lie, but the data don't."
2) To build accurate customer profile
Ken Fortney from GRIN.co says, "Data and analytics track user behavior online. Suppose the brand is also using social media platforms that have been optimized for social commerce (Facebook Marketplace, Instagram Shopping, and Pinterest). In that case, they can merge data from multiple platforms to build an accurate customer profile and recommend a product, product bundles, etc. pre-purchase instead of the traditional post-purchase recommendations."
3) To personalize product recommendations
According to Bruce Hogan, CEO of SoftwarePundit, two of the most common uses of big data are personalizing product recommendations and personalizing communication.
He says, "Companies with thousands or millions of customers can run machine learning algorithms that process existing purchase data to determine which products each customer is most likely to purchase next. The algorithm can produce a score for each item and person combination.
These scores can then prioritize the products featured in various channels – the two most powerful being onsite and in emails. An example would be showing recommended products on landing pages, or during before and after checkout."
Bruce suggests using alternative algorithms to cluster customers to identify groups of customers with similar purchasing habits. He says, "Using these clusters, the company can do more detailed analyses to identify the products that individual customers are most likely to purchase. Here's an example:
- Cluster customers into groups based on their purchasing behavior
- Identify the most popular products among customers in each cluster.
- In each cluster, recommend the most popular products to customers who have yet to purchase them."
4) To personalize customer communication
Bruce Hogan suggests that companies can run analytics at a high level to determine which channels individual customers are most responsive to.
He says, "More granularly, companies can identify the dates and times that individual customers are most engaged. In addition to sending communications through channels at ideal times, big data allows eCommerce companies to personalize the actual content of communications. For example, companies can test and optimize email subject lines, calls to action, and recommended blog content."
5) To personalize marketing for third-party websites
Sean Lee, CMO of Amify, informs how to personalize marketing when selling through third-party sites as these sites tend to own the customer experience. And brands get little data to help them better understand their customers or retarget to them.
He says, "For brands that participate in Amazon Seller Central versus Amazon Vendor Central, they do get primary data (first name, last name, and ship to address) that they can use strategically to personalize the experience. When you take this data and compare it to your own website data, you can understand how customers engage with your brand across various channels. Once a brand understands this, they have more insight to leverage when determining their advertising strategy.
Additionally, a brand can mine the customer review data on Amazon for their own products and competing ones. It brings a wealth of insight that can be leveraged in personalized marketing. This data can be used to anticipate, identify, and fix product formulation issues. These strategies might require the triangulation of data from owned and third-party channels, but it should be part of every brand marketing strategy."
How to use Big Data to Optimize Price and Increase Sales?
With an ongoing process of pricing optimization, businesses can increase their profits. However, the decision about optimizing the price must be guided by the real data rather than shooting in the dark.
Nate Nead advises knowing how to extract what type of data. He says, "Before investing efforts in price optimization -
- Do you know what the optimal price of your popular items are?
- What are your most popular SKUs(stock-keeping unit)?
- How elastic or inelastic is the demand for them?
- Have you tested the different site and landing page designs or funnels to see what pages and/or products might convert better than others?
These are all questions to ask of the data that can help grow sales. However, you first have to ask,
- What data do I need to pull to get the answer to the question?
- How do I get and properly interpret this data if I've not already set up a system to extract it from my existing eCommerce store."
6) To optimize overall operating methods
Simon Dwight, Founder & CEO of SDK Marketing, says, "Big data resources enable advancements in business strategy and planning, from customer experiences to marketing and supply chains. These advancements bring key changes to budgeting as they lower operating costs. In this way, eCommerce organizations can invest in third-party logistics and take advantage of economies of scale. This allows for lower equipment cost and optimization of overall operating methods."
7) To optimize price for explicit clients
Vipin Chahal says, "Internet business organizations are beginning to utilize large information examinations to pinpoint the most attractive cost for explicit clients to get expanded deals from online buys. Buyers with long-standing faithfulness to an organization may get early admittance to deals, and clients may follow through on sequential costs relying upon where they live and work.
Big data assists e-retailers in tweaking their proposals and coupons to fit client wants. High traffic results from this customized client experience, returning the higher benefits. Large information about purchasers can likewise help online business organizations run exact showcasing efforts, give fitting coupons, and reminding individuals that they actually have something sitting in their truck."
8) To conduct time-based A/B testing
Sean Lee informs that brands usually optimize online pricing on their own websites with A/B testing and understanding which price point drives more volume and top-line revenue.
He says, "Store pricing, however, is often set months in advance, and is not changeable by brands, which means brands can get pricing wrong, causing the retailer to leave money on the table, especially if demand is high. Brands can also conduct time-based testing of prices on their own site along with some eCommerce channels like Amazon Seller Central and then measure the conversation rate.
The combination of A/B and time-based testing allows brands to see where the lift is occurring and whether the increase in volume makes sense when discounting prices. This strategy, used across channels, can help sellers zero in on the best price without sacrificing margin. It can also ensure proper inventory levels."
How to Use Big Data to Predict Trends and Forecast Demand?
Accurate trend prediction and demand forecasting improve businesses' financial decision making and provide savvy and demanding customers with products when they want them. Again, it is big data analysis that comes to the rescue in giving accuracy to this process.
Simon Elkjær, Chief Marketing Officer at avXperten says, "As customers are constantly evolving, they might have different wants, needs, and the same strategies might not work for them anymore. It's up to eCommerce companies to keep up and even be one step ahead in predicting these needs, and they can do all that through analyzing data."
9) To uncover hidden patterns
Helene Berkowitz, Founder and CEO of ReceetMe, says, "eCommerce leaders can apply AI solutions to uncover patterns buried deep in data that wouldn't typically be seen by a human analyst. It allows them to identify new trends in customer behaviors and their purchasing decisions. This is critical since traditional data models have become irrelevant in the Covid era."
This new data also helps companies meet new demands to optimize inventory, logistics, manufacturing, sales, and marketing, leading to higher sales and continued growth.
10) To get category-specific insights
Sean Lee believes that big data is a requisite in marketing for predicting trends and forecasting demand. He says, "This is particularly true for Amazon Sellers where robust data abounds through third-party tools. For instance, a brand can use third-party search term data and category data to understand emerging players in their space better. They can assess the overall size of a particular category and determine if this is the best category for their specific products at that moment in time. They can also use this data to identify trends and ensure they can meet demand.
For instance, on a site like Amazon, it's all about winning the buy box in your category. A brand may try to go broad in a large category like "shampoo and conditioner" with a new shampoo product, for instance, but big data might tell them they would rank in the middle of the pack in that category... However, they might see that they could win with the "organic shampoo" market and shift gears in their marketing accordingly.
Sean Lee shares an example of how brands can quickly identify and fix a problem with a product not performing well through big data.
"Several years ago, I co-founded and launched a brand for P&G called Zevo, which was a new insect control product. We launched online first, and by leveraging the review data on our Shopify website, we were able to identify an issue with the scent of the product, which was not loved by customers. Using that large data set allowed us to quickly identify the problem and change the formulation before we went into large scale production for the brick and mortar retail market.
Had we not learned this so fast, we would have learned the hard way after producing and shipping all of that product. Run as many studies as you want, but nothing is better than transactional feedback from customers since it is so authentic."
11) To design and manage inventory
"Assisting on a client's necessities isn't only a present-state issue. Internet business relies upon loading the right stock for what's to come," says Vipin Chahal.
He adds, "Huge information can assist organizations with getting ready arising patterns, slow or possibly roaring pieces of the year, or plan showcasing efforts around enormous occasions. By assessing information from earlier years, e-retailers can design stock appropriately, stock up to foresee top periods, smooth out by and large business tasks, and gauge interest."
Simon Dwight agrees with Vipin. He says, "The longer an item stays in a warehouse, the more they will have to pay more to store it. Big Data tools help forecast the demand for particular groups of items or products in detail. These solutions help you determine which items to stock in which of your warehouses and provide you with an approximate number of sales per item."
12) To identify the next best sellers
"Every retailer wants to know the next best-selling products, and big data is useful for it. Trend forecasting algorithms comb data from social media posts and web browsing habits to identify what's causing a buzz," says CJ Xia, VP of Marketing & Sales at Boster Biological Technology.
He adds, "Ad-buying data is analyzed to see what marketing departments are pushing. Sentiment analysis determines the context in which a product is discussed online. Big data can be used to predict the next top-selling products in a specific category accurately. With big data, eCommerce retailers can make accurate demand forecasts and ensure you're never at risk of overstocking."
How to use Big Data to Secure Online Payments?
eCommerce customers prefer diversified payment options owing to the individual's social, financial, and convenience factors. Business owners also need to protect their customers against cyber theft to gain their trust. Once again empowered by big data, eCommerce companies with the knowledge that helps them provide diverse and secure online payments.
Paul Kovalenko, CEO of Langate, says, "In 2020, every $1 of fraud costs $3.36 to the US retailer, so eCommerce businesses strive to minimize fraud losses, and it becomes possible with Big Data. Big Data analyzes all transactions in real-time, reviewing them for correspondence to the fraud patterns, detecting new fraud patterns, and minimizing false positives."
13) To identify fraudulent transactions
CJ Xia says, "As big data can analyze huge sets of information, eCommerce retailers use these capabilities to detect online frauds and ensure safe payment on their websites. Companies use big data resources to enable machine learning algorithms. These algorithms analyze billions of transactions to identify potentially fraudulent transactions."
14) To identify trending payment methods
"Having a single centralized platform for payment has a lot of risks. All the information in one place is more prone to hacking," says Patrick Smith, CTO and Editor-in-chief at Firesticktricks.
He adds, "You can use a customized orders view to see what payment methods are in use and track them. You can also view by channels to see which payment methods are trending on a specific platform. For example, a payment method used more frequently on Facebook should be available on your eCommerce business's Facebook page."
15) To strategize EMI payments
Vipin Chahal points out that companies need to make clients realize that their installments are secure to give a pinnacle shopping experience.
He says, "Big data examination can perceive atypical spending conduct and advise clients as it occurs. Numerous online business locales offer a few installment strategies. Huge information examination can determine which installment strategies turn out best for clients and quantify the adequacy of new installment alternatives like ‘bill me later’. Some web-based business locales have actualized a simple checkout experience to diminish the odds of an unwanted shopping basket."
16) To monitor payment address
Avinash Chandra, Founder and CEO of BrandLoom, says, "Crunching big data helps in monitoring payment addresses and attempted scams. An attempted scam can be observed for frequent IP jumping or different addresses rendering back to the same IP. Similarly, big data can be used to identify other scams from the data gathered."
Nate Nead believes tracking big data is more than just identifying what customers do. He says, "It also means tracking nefarious visitors, including machines, that attempt to access data from your site with ill intent. Tracking, filtering, and potentially blocking IPs that attempt to hack an eCommerce site are another form of big data, especially for larger eCommerce stores who may have a greater potential threat from hackers."
17) To discover irregularities in payments
Sean Lee says, "Big data can be beneficial in identifying irregularities in payments. On your own web store, you may not have the scale to recognize anomalies in payments or potential risk, but working with a third-party payments provider, be that PayPal, Google Pay, or Amazon Pay, etc., can give brand access to the scale of data and insights that can reduce risk significantly."
Some More Benefits of Big Data in eCommerce
18) To shape operations according to buyer personas
"Big data is beneficial when making buyer personas. It helps you shape your operations according to your customers' preferences," says Patrick Smith.
He adds, "This includes understanding the time they prefer for shopping and what product is their priority. For example, you can use the peak shopping times to promote your ad campaigns for any sales or otherwise. Walmart used Big Data to identify that people who buy diapers often buy beer as well. You can use such data to create better promotional offers."
19) To discover new micro-segments
Helene Berkowitz informs that by tapping into big data, eCommerce companies can discover new micro-segments of customers and create new sales channels for them. She says, "For example, more time at home is driving sales of home improvement and DIY products. An eCommerce retailer can use Big Data to spot increased sales of specialty paint and fabrics used in homemade wallpaper and furniture upholstery. They could use these insights to offer a new line of DIY wallpaper or create online upholstery classes."
20) To make informed Ad spendings
David Reichmann, Owner and Founder of Rawrycat Masks, says, "Big data is essential for any size eCommerce business. Whether through demographic tools like Facebook's Audience tools or SEO tools like Google Keyword Planner, this data unlocks essential behavior and demographic information to help businesses make informed ad spend decisions."
Big data impacts every aspect of the highly competitive eCommerce landscape. When properly analyzed, this rising sea of information brings a wealth of knowledge to an online store, boosting its revenue.
An exceptional customer experience is a significant differentiator for eCommerce retailers that creates brand loyalty and reduces customer churn. With an analysis of big data, eCommerce businesses can improve user interface, merge data from multiple platforms to build accurate customer profiles, and personalize product recommendations and customer communication content.
Companies can also properly optimize price, predict trends, forecast demands, and provide diversified & secured payment options. Global eCommerce giants such as Amazon, eBay also use big data to know more about their customers and streamline the process of persuading them to buy. More and more eCommerce companies are now using big data analytics to get the maximum actionable insights from the unstoppable raw data flow.
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