Give Your ECommerce Operation a Data Science Boost
Many retail stores remain closed due to COVID-19 restrictions, forcing countless outlets to move their operation online, many for the first time. With eCommerce retail becoming an increasingly competitive space, data science is playing a key role in giving retailers a competitive advantage, particularly for those businesses hoping to make a longer-term investment in their online presence.
Data-driven decision making uses facts, metrics and data to inform strategic business decisions that align with a company’s goals, objectives and initiatives. It subsequently enables companies to create new business opportunities, generate more revenue, predict future trends, optimise current operational efforts and produce actionable insights.
There are many ways in which data science can revolutionise eCommerce businesses and, in this article, we take a look at some of the most important.
Shopping Cart Abandonment
The shopping cart abandonment rate is an important metric for eCommerce sites to track because a high abandonment rate could signal a poor user experience or broken sales funnel. A sales funnel should run seamlessly from marketing to product selection to checkout, bringing potential customers to a purchase through a series of marketing actions such as automated emails, videos, articles and landing pages. Today, the average cart abandonment rate in online retail is 69.57%, which is $18bn lost every year.¹
There are many possible causes of cart abandonment, making it a complex problem to tackle. Beyond simply improving and optimising the shopping experience through A/B testing, a key strategy for dealing with cart abandonment is shopping cart recovery.
The following methods can be used to entice shoppers to recover items in their cart.
- Abandoned cart emails or text messages – If the user entered their email address or phone number during the checkout process before leaving the website, then there is the opportunity to send them an abandonment message. This usually takes the form of an offer or discount code to entice the user to return to the site and complete the purchase.
- Abandoned Cart Retargeting – Ad retargeting is another powerful tactic in cart recovery. With retargeting, retailers place an ad pixel on their checkout page and then can remarket to those users on platforms such as social media and Google. The advantage of retargeting is that it works even in the absence of personal information such an email address.
Sentiment analysis tools help eCommerce retailers derive valuable insights mined from unstructured customer comments on feedback forms and social media platforms about a given product or brand. A customer experience strategy that does not integrate sentiment analysis as a core functionality will not capture the overall customer journey in a holistic manner.
Using sophisticated text mining techniques, eCommerce businesses can identify and resolve issues in products or services and enhance the overall user experience. Natural language processing techniques can identify words bearing a negative or positive attitude towards the brand and this feedback helps retailers to improve their products and services in direct response to consumer needs.
Customer loyalty cards, while rewarding shoppers with discounts and deals, are an effective way for retailers to collect data on a large scale.
Customer loyalty cards extend beyond the obvious function of purchase tracking by establishing potential links between online and in store customer behaviour. This helps retailers to understand and shape purchase decisions by targeting advertising and organising products to encourage more sales.
Enabling personalised product recommendation is one way in which data science is transforming eCommerce businesses. Predictive forecasting uses different data sources to make predictions of a customer’s budget and preferences, including the history of previous sales, economic indicators, customer searches and demographic data. Predictive intelligence technology is used serve what online shoppers need even before they look for a product.
A predictive model can be trained using a historical dataset which classifies customers according to their possession of various characteristics, and the degree to which these characteristics tend to indicate certain product purchases. We would then customise our product suggestions to new customers based on the combination of price and product characteristics the model suggests will be most likely to lead to a purchase. As a further extension of this idea, we can also create metrics such as customer lifetime value (CLV), or incorporate a marketing mix model to understand how exactly we should target each customer.
Selling a product at the optimal price for each customer can be done with the help of machine learning algorithms. The algorithm analyses a number of parameters from the data at a highly granular level, such as flexibility of prices, location of the customer, the buying attitude of an individual customer and competitor pricing. The resulting price point is optimised to benefit all parties. This is a powerful and important tool for retailers to market their product using customer-specific and location-specific parameters.
Upselling and Cross-Selling
Ecommerce is a particularly rich environment for upselling and cross-selling. Retailers can make offers and recommendations that are truly personalised through insights gained from data science. In doing so, retailers not only increase revenue and profit, but also strengthen customer relationships.
With the help of customer data and product performance analytics, retailers can see what products a person is buying and track the different products they frequently purchase to learn how to optimise their marketing for each customer based on their previous purchases. For example, if a customer frequently buys apple juice and bottled water separately, it may be advantageous for the retailer to market these products together as a bundle, to increase the purchase frequency.
In a supply chain, the warehousing function is critical to link the material flows between the supplier and customer. It is important for retailers to stock the right goods, in the right quantities and the right locations to meet customer demand for products. To achieve this, the stock and supply chain must be analysed thoroughly.
Powerful machine learning algorithms can analyse the data between supply and demand in great detail to detect patterns and correlations among purchases. This data is then analysed and informs a strategy to increase sales, confirm timely delivery and manage the inventory stock. This can be used to predict ahead of time whether periods of very low or no demand for a product are indicators of mistakes in the data, for example miss-stored or misclassified items, or genuine low demand.
Warehouse management software can also dictate how and where stock should be stored to optimise picking routes. Ultimately, by applying intelligence to big data, these systems can recommend stock movements within the warehouse so the flow of goods is constantly optimised.
Reducing Churn Rate
For subscription-based digital products, machine learning models can be used to predict whether a customer may churn. Such models are usually discriminative classifiers, using deep neural networks, tree-based methods or logistic regression. Generative models or recurrent neural networks can also be used. Both kinds of models can provide a probabilistic assessment of whether a customer is likely to take an action and is appropriate for targeting.
The digital world is in a constant state of flux, and to keep up with the competition and move with the ever-changing landscape, retailers must leverage data to make more informed and powerful data-driven business decisions. Data-driven decision-making can help retailers to improve and personalise user experience, predict purchases, optimise inventory management and, ultimately, drive profits. Data-driven insights can enable retailers to increase their agility, compete more effectively and gain a serious competitive advantage over other eCommerce businesses.
About the author: As a specialist in production-grade, enterprise-level data science, Finn Wheatley is Whitehat Analytics’ director of data science. Finn has more than a decade of experience working with and engaging staff at all levels in data-intensive environments, in both the public sector and industry. Finn holds an MSc in Computer Science and has deep experience working across multiple sectors, with expertise in retail and consumer data analytics, public sector (including counter-fraud analytics) and financial services.