Why Retailers are Adopting AI-Powered Ecommerce Platforms

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Over the past few years, Amazon’s relentless march has been every brand’s nightmare. In terms of resources and technical prowess, few businesses, let alone retailers can match the online juggernaut’s scale and ambition. 

Countless analysts, industry experts, and Thought Leaders have pitched in with their opinions on the factors that have contributed to Amazon’s success in the online retail space. The list includes everything from customer obsessiveness, a great loyalty membership program, amazing customer support, outstanding user experience and so on. But Amazon’s biggest advantage perhaps lies in its ability to incorporate and layer cutting-edge technologies like AI, ML and computer vision into every aspect of their processes.

AI’s Role in Accelerating the Evolution of Ecommerce

Over the last 20 years, ecommerce has evolved from being a novelty to a mammoth industry that is expected to exceed $650 billion by 2020. During this relatively short time span, the industry has seen several innovations in terms of payments, logistics and user experience. However, expect the next phase of ecommerce to be larger in scale and wildly ambitious in scope: think drones, fully automated warehouses, hyper-personalized customer engagement, automated ordering from IoT-enabled devices and tighter integration with offline retail. Artificial Intelligence, Machine Learning, and Computer Vision are expected to form the core powertrain that will drive most of these innovations. 

The Rise of AI-Powered Ecommerce Platforms

To stay future-ready, brands are rapidly adopting AI-powered enterprise ecommerce software like Capillary Anywhere Commerce Plus to drive higher engagement and sales while improving customer satisfaction. So how does an Artificially intelligent ecommerce platform differ from a traditional one? While the core architecture remains similar, the former differs when it comes to feature-sets and execution of certain tasks. For instance, a traditional ecommerce platform will require manual intervention to understand the fastest, easiest and most cost-effective to fulfill a certain order. A smart ecommerce platform will be able to calculate this automatically using thousands of data points like location of warehouse, inventory, logistic vendor costs, etc. And that’s just a small instance. An AI-powered ecommerce solution is capable of enhancing and optimizing all aspects of ecommerce starting from User Experience and until final delivery. We’ll go over some of these real-life use cases in the next section. 

Real-life Use Cases of Artificial Intelligence in Ecommerce

1. Personalization:

Across the globe, brands are driven to using massive discounts and promotions to boost sales. While this might increase the top line growth in the short term, it will start eating into profitability in the long run.

With AI-driven ecommerce solutions, brands can improve conversion and drive sales using highly personalized and contextual promotions and offers. 

Here are some of the ways personalization will be leveraged by brands: 

Personalized Promotions: Through a combination of customer behaviour analysis and predictive insights, the platform automatically generates promotions, predict the best price, and discounts for a specific customer.

Smart Merchandising: Several brands have started leveraging AI systems to design products based on data, enable conversation-based shopping and forecast sales. For instance, shoppers can simply type in their style preferences and the system will find the closest match or even generate a completely new-design in real-time.

User Experience: Intelligent ecommerce platforms are capable of personalizing all aspects of the UX, starting from which banner is shown on the home page, to customized CTAs, landing pages, product recommendations, etc. For instance, a vegetarian customer looking to order a pizza online will be shown a veg-only menu and product recommendations.

2. Automated Inventory Management:

AI-powered ecommerce platforms can automate all aspects of inventory management, including sales forecasting using advanced algorithms and historical data. Anytime a product is sold across any given channel, the centralized inventory management system within AI-driven ecommerce platforms automatically updates the SKUs which will help brands to avoid stockouts and negative customer sentiments. The sales forecasting functionality of smart ecommerce software is especially helpful for brands dealing with perishable goods like groceries, transport/event tickets by predicting the estimated sales and thereby the recommended inventory.

3. Intelligent Fraud Detection:

Rapid technological advancements have spurred several ecommerce store innovations like virtual cash, omnichannel loyalty programs, and digital downloads. Unfortunately, faster computing and cheaper internet access have also paved the way for sophisticated cybercrimes and digital fraud. New age hackers are using Machine Learning, distributed networks and internal knowledge to quickly detect vulnerabilities within a network and cause damage worth billions in a span of a few minutes. To counter this, businesses are adopting the same cutting-edge technologies which can analyze decades of data, calculate risk scores and nullify attacks within milliseconds. This integrated and real-time approach to fraud prevention is likely to be the norm in the future.

4. Customer Support:

Businesses spend a whopping $1.3 trillion on customer support every year.  Artificial Intelligence can automate most of the repetitive tasks previously assigned to customer care agents in two ways: through chatbots and voice-based virtual assistants. This can help companies to reroute precious human resources towards creative tasks that will have a larger impact on the overall business. Intelligent assistants are already capable of tackling almost all basic tasks like fetching order status, finding a specific product, scheduling a pickup/return, initiating cancellations/refunds, etc. Going forward, expect these virtual agents to handle complex tasks like personalized recommendations, sentiment analysis and improve conversions by suggesting alternative products.

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