The analytical capabilities big data brings to eCommerce is instrumental. In 2019, 100% of the e-commerce enterprises in Thailand utilised big data analytics for creating a new product. With big data analytics, companies can create new products that provide increased consumer value and at the same time, connect with their desires.
Using predictive analytics, companies can forecast the post-launch performance of a product and determine the best marketing strategies and optimal production and distribution chains for rapid growth.
In other words, big data in Ecommerce is revolutionising how companies identify new means to increase their sales and sustain in an increasingly competitive landscape. From aggregating and analysing customer data such as browsing history, shopping preferences, and social media activity businesses are becoming more aware of their customers’ needs.
Benefits of Using Big Data in Ecommerce
1. Cost Reductions
With big data analytics, companies can save costs by optimizing their supply chain and reverse logistics (product returns) process, reduce costs in marketing, and even analyse the purchase-return data to determine products that are more likely to be returned.
2. Better Customer Experience and Satisfaction
With 80% of customers preferring personalised shopping experiences, big data can help businesses enhance the satisfaction rates by taking into account multiple factors such as purchase history, preferences, browsing habits, demographics, and polarity.
3. Better Marketing Strategies
Big data analytics makes it easier to see which marketing strategies performed better and their impact on overall sales. Likewise, with techniques such as sentiment analysis, marketers can better understand the emotional feedback of customers and tweak their campaigns accordingly.
4. Ability to Predict Trends
By accumulating data from multiple sources such as social media networks, customer reviews and feedback, and weather conditions, big data analytics can help companies predict trends, even before they hit the mainstream.
5. Price Optimization
Since pricing is a critical factor for driving purchase decisions, companies need to always have a competitive advantage by offering the best price. Big data analytics will optimize this process by considering multiple parameters such as competitors’ pricings, profitability, weather conditions and uniqueness of the product to determine the best price.
With fraudulent claims and payment frauds ruling the digital world, e-commerce businesses are vulnerable to losing money. Big data analytics can help organizations monitor atypical spending behaviour and predict even before an individual can cause damage.
How Does Ecommerce Use Big Data?
1. Personalized Experience
9 out of 10 marketers imply that customers crave for personalization, and 4 in 10 use machine learning for that. Personalization alone increases conversion rates by 63%. The most prominent role of big data in e-commerce is the power of personalization it offers to organizations.
Almost every online store uses personalization to drive sales, especially eCommerce giants like Amazon, Walmart, and Target. With the extensive use of predictive analytics for personalization and recommendation engines, Target is so advanced that some customers get creeped out with the suggestions they receive.
Interestingly, there’s an incident in which Target figured out a teen girl was pregnant, even before her father got to know. To avoid that level of creepiness, Target started to mix irrelevant ads with personalized ads to make the whole look a little random.
Target assigns a Guest ID number to every customer. Every information used for personalization including, name, email address, credit card details, demographics, and purchase history is tied to this ID. Target is also known to buy information about customers from other sources.
2. Customer Analysis
With a diverse group of customers to cater to, businesses will have to define multiple target groups before running each marketing campaign. Thanks to big data, customer segmentation is easier than ever as the more you know about a customer, more precisely you can group them.
Similarly, not all customers are created equal. A minority of your customers will be responsible for the majority of your profits – just like the Pareto Principle. Segmentation is also crucial to identify customers who bring in the most value (customer lifetime value modelling) – thereby allowing you to channel your efforts accordingly.
Another great instance of using big data in eCommerce is sentiment analysis. Sentiment analysis helps businesses understand the emotional feedback of customers towards products and services – thereby allowing companies to tweak their deliverables. Customer data from sources such as social media networks (Twitter, Facebook, or Instagram) is used for this.
Take the case of BikeBerry, for example. Being an online bicycle and accessories retailer, BikeBerry was always concerned about customer retention and engagement. For that reason, they “thoughtlessly” engaged in discount-driven retention campaigns via emails.
More precisely, BikeBerry followed a one-discount-fits-all approach – which was harming their budget. They wanted to maximise the utilisation of their customer retention budget by sending offers to only those customers who need a “push” (in the form of coupons) to make a purchase.
This also meant that BikeBerry was also sending coupons to those customers who would have purchased without the coupons. To optimize their retention campaigns, BikeBerry took the help of big data analytics and tools to differentiate the customers who are more likely to purchase without incentives and the ones who relied on them.
And the results were almost instantaneous. Their email marketing campaigns saw a sudden increase of 133% in sales, along with a 200% increase in user activity. The rate of returning customers doubled, and they started to spend at least 30% more than before.
Their big data tools work by aggregating and analysing multiple data sets, including purchase history, behavioural data, browsing patterns, demographics, email opening rates and time. BikeBerry now runs a tight ship by sending timed emails only when a customer is most likely to open them.
3. Customer Experience
Customer experience and service can make or break an e-commerce business. 90% of Americans see customer service as a deciding factor to remain loyal to a business. If customers aren’t satisfied with your service, 13% of them will tell other people how unhappy they are. Likewise, 72% of customers, if they had a positive experience, will share it with other people.
With big data in e-commerce, companies can deliver multi-channel customer support with decreased response time and increased problem-solving efficiency.
North Face, an American activewear product company is a prime example of using big data in Ecommerce for enhanced customer experience. The company teamed up with IBM to utilise the potential of Watson. Watson combines AI, ML and big data analytics to deliver the right experience to customers.
Customers can directly engage with Watson, just like speaking with a human salesperson and ask questions or input preferences to receive customised recommendations. The answers a customer provides in the initial phase will be stored and used to shape future conversations with Watson.
This also means that a customer’s conversation will shape the suggestions Watson makes and the recommendation engine of the website. Watson also takes into account the purchase history, browsing activities, and customer reviews from social media networks to enhance digital shopping experiences.
4. Secure Online Payments
Especially with the introduction of m-commerce (use of handheld devices like smartphones and tablets to perform online purchases and transactions), enterprises have to provide support for multiple payment methods – which invariably increases the number of associated threats. Analysing vast volumes of data can help businesses identify anomalies and detect frauds.
Predictive analytics can also allow e-commerce companies to identify potential threats and initiate preventive strategies for the same. A notable example of this is how PayPal (which processed $US712 billion in 2019 alone) uses big data analytics for fraud detection.
An illustration of rule-based fraud detection. Image Credit: digital.hbs.edu
Paypal’s big data engine collects more than 20 terabytes of log data every day. With machine learning algorithms, the system can compare patterns and identify fraudulent transactions.
Paypal also uses logistic regression (a statistical model that estimates the probability of an event to occur based on previous data), and advanced techniques like gradient boosted trees (a type of machine learning boosting, which relies on the intuition that the previous models, when combined with the next best model, minimises overall prediction errors) to make its machine learning algorithms more accurate.
5. Price Optimization
When it comes to customer purchase decisions, pricing is one of the most prominent factors. Along with better customer service and product quality, lower pricings offered by competitors can be one another reason why customers may feel tempted to churn and break loyalty.
Price optimizations can be overwhelming for humans to perform as the data associated is enormous. Several factors, such as competitor analysis, weather conditions, product availability, and cost to profit ratio, must be taken into account. Fortunately, big data analytics makes the entire process a piece of cake.
An excellent example of this is Agoda, an online booking agency. One of the primary factors that contributed to Agoda’s popularity is its competitive price guarantee. This means, if you book a hotel room via Agoda and get a cheaper offer on some other (genuine) website, Agoda will either match the rate or beat it.
But, returning the money once paid, can be tiresome. There’s a lot of paperwork, processing fees, and extra hassle associated with returns. To prevent all that, Agoda extensively uses big data to predict the lowest right price at the right time.
For that, Agoda takes into account multiple factors such as competing prices (both online and offline), weather trends, calendar events, news-related events, labour issues, features of the hotel and the room and many other factors.
6. Demand Forecasting
Demand forecasting with the help of predictive analytics is a notable big data application in the e-commerce industry. Implementing statistical models and machine learning algorithms on historical data, big data systems can predict demands and help businesses fine-tune their supply chain processes.
For example, take the case of Amazon. When Jeff Bezos registered the orders, delivered the packages to the post office and tracked the inventory – all by himself – demand forecasting may have been relatively simpler. Fast forward 25 years and Amazon has more than 200 million unique visitors per month.
Along with demand forecasting, Amazon also uses big data analytics for price optimization, anticipatory shipping model and its recommendation engine.
In 2019, Amazon created the Galaxy data lake, which can store and manage massive amounts of unstructured and structured data with speed and accuracy. Such a data lake is crucial for Amazon as delayed data can lead to millions of dollars in lost revenue.
These data lakes are powerful foundations for artificial intelligence (AI) and machine learning (ML), which further shapes Amazon’s demand forecasting models. Amazon takes into account multiple factors, including social media trends, economic viewpoint, website traffic logs, historical sales, seasonal demands, technological advancements, and price of goods to forecast demands.
7. Supply Chain Management
It’s safe to say that without big data analytics, supply chain management would have been an arduous responsibility. Big data makes inventory management efficient, even with the ever-increasing number of products. Big data also plays a pivotal role in fleet management and eliminating instances such as overstocking.
Walmart is a prime example of companies using big data in eCommerce and processes 2.5 petabytes of data (unstructured) every hour.
Walmart handles millions of customers per day and it is interesting to note that the company has more employees than the number of customers many other online retailers have.
Walmart extensively uses big data analytics for supply chain management and obviously, for delivering personalised shopping experience and recommendations.
With its eCommerce website functioning in 10 countries and offline stores in 27 countries, at any given time, Walmart manages an average of US$32 billion in inventory – that’s double the GDP of a country like Georgia.
With these kinds of figures, having an efficient supply chain management system is critical for smooth functioning.
For that, Walmart Labs (a subsidiary of Walmart) uses big data analytics across multiple supply chain activities, including sourcing, shipment preparation, transportation, last-mile scheduling/routing and pick up.
For example, to show the estimated delivery date, known as delivery promising, Walmart considers multiple factors such as:
- Distance between the fulfilment centre and the customer.
- Inventory levels of the item.
- Available shipping methods and costs associated with each.
- The capacity of shipping mode.
Similarly, big data analytics allow Walmart to significantly reduce costs and time associated with shipment preparation.
By considering the distance between each item (ordered by a customer) in the warehouse, Walmart’s picking optimization strategy will suggest the picker the right route to follow. Analytics is also helpful for packing optimization – choosing the correct box size for shipment.
By implementing big data analytics into their supply chain, Walmart witnessed numerous sustainable competitive advantages such as reduced inventory carrying costs, lower product costs, competitive pricing, and more importantly, increased customer satisfaction.
In 2020, there are at least two billion online shoppers – meaning, the data produced will be humongous in volume. Be it online or offline; businesses can use big data analytics to predict the needs and preferences of their customers, optimize the supply chain process for cost reduction and most importantly, improve sales by staying ahead of trends and demands.