Automation vs. Artificial Intelligence – An Ecommerce Use Case
Last updated on December 28th, 2023 at 05:19 pm
ChatGPT’s rise has sparked worldwide interest in AI, particularly among online retailers. As of now, approximately 50% of retailers claim to use AI in some form. The AI market in retail is valued at 7.34 billion USD in 2023, highlighting its growing importance in the industry.
As expected this surge has led to a greater focus on AI-driven business solutions. However, it’s crucial to distinguish between AI and automation. Although they seem similar, key differences exist, especially in their application in retail.
This blog will explore these distinctions, highlighting how they impact the retail sector. But first, it’s essential to understand what automation in retail means to fully grasp the nuances of these technologies.
Automation platforms include a broad range of technologies and methods, streamlining various aspects of online retail operations. iPaaS being the category name for such platforms.
iPaaS (Integration Platform as a Service)
In online retail, iPaaS (Integrated platform as a service) is well known for providing automation solutions.
Consider Mike, an inventory manager at an online retail business. Every week or so, he must update stock levels across various platforms such as Shopify, Amazon and others, a task that involves checking and reconciling thousands of items. A small human error in this manual process can lead to significant inventory problems.
To overcome this challenge, all he needs is an iPaaS (Integration Platform as a Service) that syncs inventory across multiple sales channels simultaneously in a real time manner. This leaves no room for manual errors.
iPaaS in fact offers an end to end solution where you can automate the retail processes right through product listings, catalog, inventory, order, accounting and more.
All of these automated capabilities free up an online retailer to focus on critical aspects such as supplier relationships and marketing which are essential for scale and growth.
RPA: Robotic Process Automation
To be clear, these do not involve physical robots like the ones used by large retailers in their warehouses; they are software bots. Their role is to automate small, repetitive, rule-based tasks such as updating customer records or processing orders. They can work independently or under human supervision.
The process of RPA begins with data collection from multiple sources. For instance, extracting product IDs, quantities, customer shipping details, and payment information. Next, the data is transformed and categorized, prioritizing products based on delivery deadlines and inventory levels.
Finally, the bots deliver the output, updating the inventory system and completing the order processing cycle.
However, it’s important to note that these bots are pre-programmed for specific tasks and this means they operate well within a defined framework but can’t adapt to changes or new scenarios independently.
For example, if an online retail store introduces a new product line or changes its order processing software, RPA bots would require reprogramming to handle these changes.
AI serves as the decision engine in Intelligent Automation because it can operate independently or with minimal human input.
According to retail leaders, AI’s significant impact lies in customer intelligence (53%), inventory management (50%), and chatbots for customer service (49%). However, AI’s most prominent role in the retail industry is in driving sales and marketing.
Personalized Product Recommendation: Retailers now easily gather data on customer preferences, shopping history, and behaviors. By utilizing machine learning algorithms, this data is analyzed to tailor product suggestions more effectively.
This approach not only enhances the customer experience but also drives targeted marketing campaigns, leading to higher sales and customer satisfaction.
Demand Forecast: AI helps retailers accurately forecast demand by analyzing historical sales data, particularly for high-traffic events such as BFCM(Black Friday Cyber Monday).
By integrating currently important metrics like real-time customer analytics, retailers can precisely adjust their inventory, aligning supply with projected demand to maximize efficiency and minimize excess stock.
Customer Support: AI chatbots offer round-the-clock customer support, addressing issues like product replacements, returns, and specific order related problems instantly.
While they efficiently handle basic to medium-level queries, human intervention remains crucial for financial issues like initiating refunds.
The table below shows the key differences between Automation and Artificial Intelligence for an Ecommerce Store or business:
|PIM(Listings & Catalog)
|Automates product cataloging, updates product information, synchronizes listings across platforms.
|AI enhances product discovery by personalizing search results and recommendations based on user behavior and preferences.
|Streamlines inventory tracking, auto-updates stock levels, triggers reorder alerts.
|Uses predictive analysis models to forecast demand based on historical data, customer behavior and market trends. Thus, helps streamline inventory accordingly.
|Automatically routes orders to appropriate suppliers or warehouses based on predefined selection.
|No distinct or direct use case except for maybe extremely large scale ecommerce data warehouse operations.
|Shipping & Tracking
|Automates generation of shipping labels, tracking number assignment, and order status update.
|Based on historical data, AI helps predict shipping times more accurately.
|Automate invoice generation, payment tracking, and basic accounting tasks.
|Provides financial forecasts, detects anomalies in financial data for fraud prevention.
|Sales & Marketing
|Supports sales velocity through process efficiencies.
|Leverage customers’ data for personalized recommendation and optimizing product listing.
AI and Automation in Online Retail
AI is often recognized for its capabilities to generate new insights or data. This aspect is crucial in the online retail industry, where AI can analyze vast amount of consumer data for personalized marketing, pricing strategies and predicting buying trends.
On the other hand, automation primarily involves the seamless transfer of data between systems, minimizing the need for manual intervention. You can apply rules on that data to automate operations. Hence, this aspect is crucial to the operational or backend process optimization. It ensures that the entire ecommerce operation is streamlined and more efficient.
In dropshipping, where timely order fulfillment is crucial, automation maintains high accuracy and reduces the risk of errors in operational processes.
Together, AI and automation create a powerful synergy in online retail and dropshipping.
By combining AI’s predictive capabilities with automation’s efficiency, online retailers can optimize their operations, market better, and enhance the overall shopping experience for customers.
In conclusion, retailers should strategically integrate AI for adaptable, data-driven decision-making, while employing automation for efficient execution of operational processes and profitability.