How Data Science and Machine Learning is transforming the E-commerce industry in 2019
By Arijit Banerjee
According to Barilliance, personalized product recommendation account for almost 31% of the revenues in the global E-commerce industry. The conversion rate for shoppers who do not go through recommendations stands at a measly 1.02%, while that percentage increases to a massive 288% after the first interaction. A separate study made by Salesforce found that online shoppers are 4.5 times more likely to add items to the shopping cart and complete a purchase after clicking on any product recommendation.
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Personalized product recommendations on popular E-commerce websites like Amazon and Netflix is just one the many ways in which data science technologies like Machine learning (ML) is transforming customer experience from simply good to exceptional.
Besides enabling personalization, data science technologies can benefit E-commerce retailers and solutions in multiple ways, as detailed in this article.
More on Personalization
So, why is product personalization so critical for online retailers? A 2017 study conducted by Segment reveals that only around 22% of online shoppers are satisfied with the level of personalized shopping experience that they receive with E-commerce brands. A market study conducted by technology company, Infosys concludes that 31% of the surveyed customers are wishing for a more personalized shopping experience. Despite the ongoing debate about protecting user privacy on the Internet, a Salesforce research found that 52% of online shoppers are willing to share their personal data in return for more personalized product recommendations.
Product recommendations based on “what customers ultimately buy” or the “best-selling product” along with sending customer e-mails with personal product recommendations are also improving conversions particularly among first-time customers.
Predictive forecasting and intelligence
Enabled by artificial intelligence (or AI), predictive forecasting is a technique that can disrupt E-commerce sales forecasting on the basis of Big data and seasonal indicators. For example, AI technology can use the current weather forecast data to predict the short-term demand and sales trends.
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To make its predictions, predictive forecasting uses a variety of data sources including:
1. History of previous sales
2. Economic indicators
3. Customer searches
4. Demographic data
Along with predictive forecasting, AI-powered predictive intelligence technology is being used to predict and deliver what online customers need even before they look for a product. Among the many customer success stories for Salesforce, predictive intelligence enabled online furniture retailer, Room & Board to increase its return on investment by a whopping 2900% simply by predicting and recommending additional purchases to its customers. B2B analytics companies like Lattice Engines and Mintigo combine customer data with individual activities on social media and websites to accurately identify sales prospects for their customers.
Customer Behaviour and Shopping Patterns
Apart from the business benefits of personalization, Big data analytics can be beneficial in determining customer behaviour and shopping patterns. For example, which are the retail brands that are most in demand among online shoppers? When do customers shop more for the type of products that you offer? When do online shoppers make high-value purchases?
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Based on these insights, E-commerce retailers can predict the market demand for products (or services) and devise more appropriate marketing strategies to tap into this demand.
Online shopping patterns are also useful in determining the right inventory level for a line of products. Online retailers can optimize their stock levels by predicting if the products in demand are going to be overstocked or understocked. Based on the insights provided by Big data analytics, you can manage your E-commerce operations such as supply chain, inventory, marketing channels, and product pricings more efficiently.
Customer-related KPIs and metrics
Among the important metrics (or KPIs) for E-commerce business, Customer Lifetime Value (or CLV) determines the overall value of revenue that each customer will bring during their association with the company.
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CLV benefits E-commerce retailers in the multiple ways, including:
1. Determine the right marketing strategies.
2. Determine the average cost of acquiring customers or Customer Acquisition Cost.
3. Set business objectives for future growth, expenses, revenue, and net profit.
4. Personalize customer purchases through up-selling and cross-selling.
5. Optimize business spending on marketing campaigns and online advertisements.
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As an E-commerce retailer, you know the challenges of acquiring a new customer. At the same time, after customer acquisition, customer retention is an important objective for online retailers. This is because loyal and repeat purchase customers generate around 40% of the company’s revenue. Customer retention is also key to increasing your CLV.
A customer churn model is effective for retailers to identify the customer who are more likely to switch to their competitor’s products and to take measures to retain these customers. Based on metrics such as number (and percentage) of lost customer and value (and percentage) of lost recurring business, the customer churn model can help E-commerce shops to:
1. Identify potential churn customers and devise retention campaigns.
2. Maintain and increase the CLV.
3. Minimize customer churn.
Be it through identity thefts, phishing, or account thefts, online frauds grew at a rate of 30% in the year 2017 making it almost twice the percentage growth in retail sales. Apart from these types of thefts, shipping and billing-related frauds are also on the rise.
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Besides providing good products and exceptional customer experience, online retailers must ensure customers of the safety aspect of online transactions performed on their website. Online fraud can cause loss of revenue and also create a negative perception about the business among online shoppers leading them to avoid making online purchases with the concerned retailer.
A combination of data science and machine learning can be used to detect suspicious behaviour through the following indicators:
1. Different shipping and billing address
2. Large value orders
3. Use of multiple modes of payment for the same shipping address
4. International orders
According to this November 2016 study conducted by Deloitte, 72% of companies can effectively use Big data analytics to improve customer experience. This article reports that 72% of companies believe that Speech analytics can be an effective tool in improving customer experience and delivering business benefits.
So, how can data science help in improving customer service? While traditional forms of customer service comprised of product (or service) feedback from customers or reaching out to customers through phone or e-mail, the rise of data analytics has provided online retailers with valuable insights that is helping them provide better services.
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Enabled by natural language processing (or NLP), Sentiment analysis is an effective tool that can derive valuable insights from the large number of online customer reviews and ratings about a given product or brand. Data analytics tools such as the Word Cloud and N-grams can be used to make sense of user reviews by looking for selected words or word associations that convey what users think about the product or brand.
Data analytics can help E-commerce retailers to identify and resolve issues in products or services, thus enhancing the overall customer experience.
The benefits of using data science technologies including AI, machine learning, and natural language processing are immense and are driving the phenomenal growth of the global E-commerce industry. This article outlines 6 of the crucial areas where data science is making an impact. Be it a small or a global E-commerce retailer, investing in data science technologies can enable you to understand customer needs, improve customer service, design better products or services, and prevent online fraud, among other benefits.
And that completes our thoughts about the use of data science in E-commerce! We hope this article has been informative for your business. Do you agree with the multiple benefits of data sciences for E-commerce players as outlined in this article? We would love to hear your feedback in the comments section provided below. In the meantime, you can also check out our certification courses in Data sciences and Big data analytics.