Data Science in E-commerce
With the world immersed in data from disparate sources, every time you click your mouse to purchase something, the information trail (data ) is captured and stored which is used in future by retailers to attract you to make more purchases. For example, if you are a customer looking to buy a new phone, mobile websites or apps have information of what products you viewed, Google has information about what products you searched for and GSMArena (a popular smartphone reviews website) knows what mobile phone reviews you read. You also happened to share these reviews via tweets or Facebook updates. All the millions of Tweets, Facebook likes, Instagram and Pinterest Photos can be organized in a manner to help e-commerce businesses discover what customers want and when they want it. Collecting, storing, sorting and analysing data to draw meaningful and productive insights is an integral part of data science and this comparatively new kind of job in the field of data science is fulfilled by experts known as “Data Scientists”.
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“The past does not repeat itself, but it rhymes.”- said Mark Twain
Even though future events have distinct circumstances or conditions, they characteristically follow similar patterns. The “Big Data Revolution” has brought technological advancements in data storage, cloud computing and data science which helps businesses identify these similar patterns. Today, data science algorithms can predict everything from flu outbreaks to mortality to crimes.
Consider a retailer that sells electronic gadgets. Let’s suppose that generally they have been doing great business due to the quality of their product and on-time deliveries. As the global trend shifts and competition grows, there is a need for ecological products. This slowly shifts company’s perfect customers to their competitors - which probably will go unnoticed by the company if they manually examine the market. Such small shifts can be identified by data scientists who write algorithms to continuously monitor the bygone sales cycles of the company by cross referencing the sales with external sources like news articles, social media updates - discussing these trends that help find correlations with the inclination to buy the products. Data science helps retailers discover new ways to understand how to retain their “core” customers rather than merely acquiring new customers.
According to EMC statistics report, the amount of digital data will exceed 44 zettabytes by end of 2020 that is close to 5,200 GB for every woman, man and child on earth. The amount of digital data produced is expected to double every year. As the saying goes “Data is the new gold”! Competition among e-commerce businesses is faster and fiercer. Customer habits change with the blink of an eye and every e-commerce business wants to win over that extra edge when it comes to fulfilling customer demands. Common sense, intuition and gut feelings are useful but definitely not enough to make predictions. Data science algorithms help businesses understand products, services, processes and customers effectively.
Data Science is not only for web companies-
- L’Oreal, the popular cosmetic company employs data scientists to find out the effect of various cosmetic agents on different skin textures and compositions.
- Rolls Royce employs data scientists to analyse data from airplane engines for scheduling maintenance.
- Feedzai uses data science algorithms to detect fraud.
- Fruition Sciences, an online decision tool for wine maker’s uses data science algorithms to accurately determine how much to water grapes and when to water grapes to produce better quality wine.
Data Science in ecommerce helps businesses provide a richer understanding of the customers by capturing and integrating the information on the web behaviour of the customers, the events that occurred in their lives, what led to the purchase of a product or service, how customers interact with different channels, etc.
Some data trends observed in the ecommerce industry are-
- 60% of people research and engage with brands on various channels like mobile, social media, in-store, websites, etc.
- People who search for a product using different channels spend 1/3rd more than people who don’t.
- 43% of retail sales in US are inclined towards the web.
- A survey by eCommera found that only 23% of UK retailers can make sense of data to take informed decisions.
- 50% of retailers in UK consider the shortage of business intelligence tools as the cause to harness the power of data science whilst only 16% are confident about their analytics solutions.
These trends show the rising boom for ecommerce industry and data science holds the promise of enhancing the shopping behaviour of customers that can provide ecommerce businesses with an improved marketing mix and enhanced profitability.
Data Science Use Cases in Ecommerce
1) Product Recommendations for Customers
“The future is going to be so personalised, you’ll know the customer as well as they know themselves” said Tom Ebling, President and CEO, Demandware
Promotions and Recommendations are highly effective when they are based on customer behaviour.Customers these days are dependent on recommendations whether it is for products to purchase, news on recent launches, restaurants to visit or services to avail. Most of the ecommerce websites like Walmart, Amazon, eBay, Target have a data science team that considers the type, weight, features and various other factors to implement some kind of a recommendation engine under the hood .The recommendation engines implemented through data science have two major motives-
- Cross Sell-You are purchasing an iPhone 6 so you possibly might be interested in one of iPhone cases to protect it.
- Up Sell- For instance, you are looking at a LED TV, here is the next version of the TV which is even more awesome and is available just for a few dollars more.
Data science algorithms learn the various attributes and correlations among the products; learn the tastes of customers to predict the needs of customers. Data science algorithms help in personalizing customer experience by changing the gallery pages for a specific customer or by changing the order of products in the search result of the mobile app or website.
Puneet Gupta, chief technology officer, Brillio (a US-based technology consultant and software developer) said -"With predictive analytics and the use of machine learning, e-commerce players can now derive a clear understanding of consumer behavioural patterns, spanning purchase history and performance of different products on the site."
The best example for this is Amazon’s Recommendation Engine that uses predictive modelling. Amazon’s recommendation engine discovers and mathematically represents those discovered relationships in historical data to make classifications or predictions about future events.
2) Gaining Customer Insights for customer retention, up selling and cross selling
With changing shopping habits, diminishing customer loyalty and high expectations-gathering customer insights has become extremely important for ecommerce businesses in order to survive.
Any Ecommerce website or mobile app has products to sell but the answers an ecommerce business needs to focus on is-
Who are the people buying their products?
Which location do they live?
What kind of products they are interested in?
How the business can serve them better?
What makes them buy?
The answers to all the above questions can be generally be provided by the data analysts in a group dedicated to customer insights within the product space. Data science algorithms can add value with more advanced analytics like classifiers, segmentation, unsupervised clustering, predictive modelling, and natural language processing together with topic modelling and keyword extraction.
Blue Yonder, a German Software company has developed a self-learning technology using data science tools and techniques that helps Otto (European Online Fashion Giant) - to self-learn about customers as they walk into the physical store or log in to the retailers Wi-Fi or connect with the mobile app or website. Customers are sent push notifications based on the location of stores, weather conditions and tons of other factors.
3) Defining Product Strategy for the optimum product mix
Ecommerce businesses have to deal with various questions like-
- What products should they sell?
- What price should be offered for the products and when?
Data science algorithms help ecommerce businesses define and optimize the product mix. Every ecommerce business has a product team that looks into the design process where data science algorithms can help the business with forecasting like-
- What are the loopholes in the product mix?
- What should they make?
- How many quantities should be ordered as initial batch from the factory outlet?
- When should they halt the supply of those products?
- When should they sell?
Data scientists help ecommerce businesses with more advanced predictive and prescriptive analytics whereas data analysts will merely look into the retrospective analysis like how much did the business profit by, what are the products that are worthless, etc.
4) Predicting the Supply Chain model for effective delivery
For ecommerce businesses to sell products, they need the right amount of products in the right place at the right time. In ecommerce or any retail business, some products might have a very short demand window (think of customised “Merry Christmas 2014” products on Jan 1, 2015) and if the business misses that window for a given product they might end up piling up useless stock inventory in their warehouses. Data science algorithms perform detailed analysis to develop advanced predictive models that help ecommerce businesses optimize customer satisfaction, reduce the risk factor and inform strategy.
5) Personalized Marketing Strategies
Data science plays a critical role in personalized marketing programs. Ecommerce businesses are always looking for novel ways to encourage existing customer to make more purchases or finding out strategies to attract more customers. Data Scientists can contribute to it through ad retargeting optimization, channel mix optimization, ad word buying optimization, etc. By designing data science algorithms for employing these various strategies, data scientists can help an ecommerce business reach dizzying heights which will earn worthy rewards for business.
Data science is at the core of ecommerce business and can also be used for Fraud Detection, Web Analytics, and HR.Can you think of any other data science applications in the ecommerce industry that are revolutionizing e-tailing? Let us know in comments below.