The eCommerce opportunity is going through the roof, with its global market size touted to touch $8 trillion by 2026.
That said, the opportunities unlock a slew of challenges.
From availability, flexibility, and personalization to sustainability and cohesive experiences, eCommerce today faces an expanding array of customer expectations for almost anything and everything.
Jeremy Woodlee, the managing director of Accenture Song, believes that – With consumer behaviour shifting fundamentally, a linear and product-centric purchase path is now replaced by a non-linear and customer-centric purchase path.
Image Source: Accenture
As eCommerce businesses plan to keep customers at the heart of the business, they must meet these expectations while increasing organisational efficiencies and driving profit.
To do this, brands have long sought to leverage Data Science and advanced analytics. Data enables them to generate insights that underpin all the advanced capabilities to stay one step ahead of their customers’ needs.
This article intends to give you a glimpse into five amazing ways Data Science can help eCommerce businesses produce revenue-shifting outcomes.
But before that, a quick gaze over Data Science as a term.
What Is Data Science?
Data Science is the study of vast volumes of data to discover hidden patterns and extract meaningful insights that you can use to fuel intelligent and transformative decisions for your business.
“Data Science takes a multidisciplinary approach by combining tools and technology from the fields of machine learning, artificial intelligence, cloud computing, IoT, quantum computing, mathematics, and statistics.”
Top 5 Ways Data Science Helps Skyrocket Ecommerce Profitability
- Predict the Needs of Customers
eCommerce brands must deliver relevant offers that depict consumers’ demands. Such accurate prediction is almost invariably the result of a 360-degree understanding of your customer’s shopping behaviours.
A recommendation engine with Data Science algorithms is the solution that finds the connection between customers’ preferences and products. It helps businesses anticipate the customer’s needs before they know it themselves. It would then create highly targeted sales and promotional messages that effectively nudge them into buying.
Personalised promotions also enable brands to upsell and cross-sell.
To build such an analytics-driven marketing strategy, brands use insights from individual customers’ transactional and purchase data in a non-intrusive manner.
Not only does it help brands to find the best deals for their customers, but it also boosts conversion rates significantly.
The best-known application of Data Science in forecasting customer needs is Flipkart’s “Recommended for you” or Amazon’s “Customers who bought this item also bought” features.
- Determine the Optimal Prices
Shopify’s Future of Commerce Report 2022 reveals that 74% of customers view price as the most influential factor in their purchase decision.
No matter how perfect your product is, your eCommerce sales strategy will fall flat if you do not optimise your prices. The price optimization process ensures that the products sold on your eCommerce store are at optimal levels.
Dynamic pricing is the best way to get there. Data Science deploy pricing algorithms to set dynamic pricing for different target groups. With dynamic pricing, business owners can adjust product prices depending on real-time variables such as price changes by competitors, demand volume, stock volume, market trends, frequency of product visits, day and time of purchase, and many more.
It enables you to continually track prices on the market and propose the best pricing to stay competitive, maximise profit, and boost sales.
“Data fosters a granular understanding of best price points not at category level but at the product level so that you can generate optimal revenue.”
- Keep Inventory Accurate
Inventory management is a part of eCommerce’s lifeblood. But most brands hit strong headwinds while optimising stock for better profitability. They have to analyse the market trends, predict sales patterns, replenish stock, and ensure regular supply to keep their online store running like a well-oiled machine.
Although the key here is easier to grasp, it is really hard to execute. Oftentimes, brands find them struggling with the problems of understocking and overstocking.
If you lack the products in demand, you will likely spend extra on courier-mail services for the last-minute hustle to deliver products or even damage your reputation. On the other hand, goods lying around in excess incur additional costs on insurance, handling, storage, etc.
Data analytics solutions are key to gathering and analysing past and real-time data on customer behaviour, buying patterns, seasonal trends, product performance, and stock levels. The insights that businesses get help them make data-backed decisions needed to plan inventory efficiently.
Image: Advantages of Inventory Optimisation Image Source: Appinventiv
- Detect and Prevent Fraud
Global payment fraud is projected to cost 25% more than in 2020 and reach a whopping $40.62 billion in 2027. Amidst the financial frauds growing at breakneck speed, thriving into a secure eCommerce store can feel especially overwhelming.
This scenario inevitably has a monetary impact on eMerchants and their customers. But the negative customer experience it generates is far more detrimental to a brand’s credibility. To solve this issue, eCommerce businesses are now relying on built-in fraud detection systems assisted by Data Science and machine learning algorithms.
With the help of behavioural analysis and real-time detection, data analytics paints a better picture of suspicious activities. It helps detect patterns, identify unusual transactions, and block fraudulent activities before it occurs.
Image: Data Science for fraud detection Image Source: Medium.com
- Deliver Delightful Customer Experiences
What separates winners is their ability to deliver hyper-personalised customer experience. And the key element of strengthening customer experience is using Data Science to dive deep into customer journeys and design contextual offers and experiences for every customer.
Brands can also use data strategies to build customer journey maps by capturing interactions and metadata from individual customers. From there, they can build comprehensive customer journey models to understand how individuals move through different journey stages.
With all this data in their hands, brands are in a better position to elevate different touchpoints to deliver highly relevant content and improve customer experience.
“Data Science coupled with AI-powered tools lets you craft real-time responses required to offer timely and accurate customer service.”
Final Thoughts
To sum up, the dynamics between eCommerce and Data Science are very promising. If you are an eCommerce brand, it’s a good time to harness the power of Data Science to increase revenue using measures such as tailor-made marketing and advertising, product recommendations, optimum product mix, pricing strategy, demand forecasting, and many more.
