Big data analytics has impacted almost every industry in some way or the other. But it has the most impact on industries which are consumer oriented. One such industry I am going to discuss is E-Commerce. Over the past decade, the evolution of both technology and the internet has had a direct correlation with E-Commerce. It has changed the way we socialize, the way we work and the way we shop. With an increasing number of users joining internet every year, instant access to internet, an increasing number of shoppers, their changing buying behaviour and ever-changing technology, E-Commerce has witnessed extensive growth in recent years.
E-commerce has evolved significantly in their decision making over years. In early days, simple basket analysis was used to make recommendations, today we have customer specific predictive algorithms being executed. Earlier this process took days to execute which is now executed in seconds making E-commerce players more effective. E-commerce with the help of analytics today seamlessly churns millions of activities of billions of customers by parallelization of processes and we no more see tabular format data stored in CSV formats as we did earlier.
Most e-commerce retailers worry about disruption – but disruption, in fact, be a good thing? Big Data has disrupted business irrespective of geography, company size or industry area. “Not so long ago, companies saw competitors and new regulations as the biggest potential disruptors of their businesses. While both those factors still have the power to disrupt, CEOs see the growing influence of customers and the ways in which goods and services are now produced – particularly through digitization and new technologies – as increasingly disruptive. “
Big data analytics has impacted and benefited E-commerce in many ways like:
Personalization, recommendation and customized offers: With more data being collected from customers based on browsing habits and past purchase behaviour, e-commerce retailers can process this information and using recommender systems, they can push out personalized content, advertisements, promotions, discount coupons and provide recommendations about relevant products based on their preferences thereby providing customers a rich and personalized experience which ultimately improves e-commerce retailer’s ability to increase sales through cross-sell and up-sell. For instance, Amazon “Recommended for you” category provides recommendations based on your purchasing data. When you add a DVD to your shopping cart, similar movie purchased by other customers are also recommended for you to purchase.
Optimize pricing and dynamic pricing: Big data enables e-commerce retailers to set the best price for products by tracking demand, transactions, cost of goods, product pricing history, competitor’s activity, available inventory and other variables. They can map the rise and fall of demand and match pricing accordingly and action can be taken on insights within minutes to maximize their profit margins. Other ways to use big data analytics to optimize pricing include using data to timing markdowns to maximize margins, best price promotions, and pricing differently at different times to different customers to maximize revenue. For e.g., E-commerce retailers can decide whether a $10 off or 20% discount on price would best work for customers.
Supply chain management: Big data analytics play a major role in streamlining demand-supply chain. Data collected from parameters like demographics, festivals, location enables E-Commerce players to accurately predict demand for products and forecast any potential disruptions to the process thereby helping them to manage their inventory and quickly act on preventative measures. For instance, Amazon links with manufacturers and tracks their inventory, decides best delivery schedule, route and product groupings to fulfill customer’s orders quickly.
Other ways in which E-Commerce industry is leveraging big data analytics to maximize their sales and profit include improving in-store efficiency and performance, creating personalized stores, minimizing fraud by proactively detecting it, introducing products customers may like, improving customer engagement, increasing customer retention, and improving customer services by collecting and analyzing data on each customer’s attitude, purchasing patterns, preferences and other factors helping e-commerce players serve them better.
While there are huge benefits in adopting Big data technology, there are also massive challenges. Some of the hurdles that E-Commerce retailers face on the path to adoption are:
- Volume: As the name suggests, Big Data integration involves the collection of a large amount of relevant data from a lot of different sources. E-commerce retailers get statistics from a variety of sources such as transactional data, operational data, click stream data, audio data, video data, customer profiles, loyalty programs, customer’s buying behaviour based on their searching pattern, transactions performed, location, social media or service usage and more. The challenge is not only about gathering correct and relevant data, but also analyzing and leveraging them appropriately.
- Velocity: Online shoppers are producing data at a very high speed. Big Data never stops and to leverage it correctly, it needs to be rapidly processed for quick business strategy and decision making. It’s the speed and immediate application that gives E-commerce retailers the power. E.g. If data is processed immediately, E-commerce players can predict out-of-stock situations before they happen and can prevent it quickly by communicating with suppliers. The concern is to handle data as it comes at unprecedented speed by rapid analysis and timely actions.
- Variety: Big data comes in different formats from traditional databases to unstructured email, videos, text documents etc. E-commerce business needs to learn to interpret this wide variety of data correctly to make the right business decisions. Challenge is to link, match, correlate, and interpret data that comes from different sources.
- Veracity: This refers to the accuracy associated with the type of data that is collected. For example, when a visitor visits an e-commerce website and signs up, the portal is unaware of the customer except for the information he/she entered. The customer is genuine or not is questionable. This might create huge revenue losses for a company when a customer makes a Cash-On-Delivery (COD) purchase and the information entered like phone number, the address is invalid or fake. This poses challenges as some data remains inaccurate because of human lack of honesty or technology failure or some economic factors.
Other common challenges faced by E-commerce business are finding the right products to sell, product returns and refund, attracting and retaining customers, convert shoppers into paying customers, generating targeted traffic, lack of proper integration of various systems like Order management system, customer support system, dispatch system, order tracking system etc., choosing the right technology and partners etc. There are also ethical, legal and security challenges faced by E-commerce business such as preventing card frauds, data misuse, protecting from phishing attacks and money thefts, consumer data privacy and more.
To overcome these challenges, E-commerce retailers must harvest the power of big data analytics to figure out their audience to lure them with offers, craft the right message for the right audience in order to convert them into leads with hopes of turning them into customers, implement tactics to get the most out of their customer base, find ways to cut inventory costs, improve marketing efficiency, reduce overhead, reduce shipping costs and control order returns, to choose right marketing partner and much more.
Avinash Kaushik, Digital Marketing Evangelist at Google and author of Web Analytics 2.0, says: “Most businesses are data rich, but information poor”.
The issue that E-Commerce retailers run into now, however, is that technology has expanded to the point where we have “too much” data. So, organizing, studying and understanding this data has become even more complicated because we’re inundated with endless facts, numbers, percentages and perceptions which has given rise to demand of people with data science skills who can process this data and derive insights from them enabling e-commerce retailers to achieve their goals more quickly and efficiently. There are a lot of organizational roles associated with data science skills in the E-Commerce industry for e.g. Data Analyst, Business Analyst, Data Scientist, Data Engineer, Senior Data Scientist etc. Data Scientist is someone who cleans the collected data and dives deep in that huge amount of data, identify trends and derive meaningful insights from them. These roles are not just specific to the E-Commerce industry but in all other industries also who have invested in Big Data Analytics to develop and execute strategies for improving profits.
The world is moving online. With increasing number of online websites and platforms to shop from, the recommendations and convenience those portals provide, online shopping is just going to keep growing. I personally love shopping online and I am amazed the way these platforms influence me and make me buy things which I was not even looking for in first place. Predicting customer’s behavior and changing their behavior is something about E-Commerce that excites me, so I choose to join this industry and wish to enhance customer’s shopping experiences by analyzing and predicting their needs and wants.