How to Scale Your eCommerce Business With In-memory Computing
eCommerce has been on the rise through the years, and demand for online shopping facilities continues to increase, especially for the younger generation. Statistics show that 67% of millennials and 56% of Gen Xers have expressed a preference for online shopping compared to shopping in brick-and-mortar stores. Convenience plays a major role in eCommerce's boom in popularity - 43% of online shoppers have indicated that they shopped online while in bed, 23% did so while in the office, and 20% from the bathroom or while in the car. Interestingly, men have been spending 28% more than women when it comes to shopping online.
Online retailers are now trying to adapt to the influx of online shoppers and find ways to effectively manage increasing traffic to their websites. Because of this, there has also been a continuous rise in the adoption of in-memory computing, which allows for faster processing and more efficient analytics on big data. It eliminates bottlenecks in the data analytics process by handling mixed workloads within the same architecture-without the need to separate transactional databases from analytics databases. In-memory computing has practical applications in a number of industries that deal with big data, including financial services, retail, and insurance.
Below are a few tips on how to push your eCommerce business forward by leveraging in-memory computing.
Online retailers can leverage location data to gain a strategic advantage against competitors by analyzing the places that your customers frequent. This can help provide a deeper understanding of their behavior and how it affects current market trends. Gathering this data will allow you to tailor user experience and make strategic customer acquisitions. You can also use geofences to access your competitors' customer data to see if these customers are loyal to the brand and, if so, why.
Location data can also be used as a demographic identifier to help identify and segment your customer base. This data will then help provide more targeted contextual ad content and promotions based not only on location but also on intent. This ensures that you deliver the right marketing campaigns to the right people at the right time. A 2019 report shows that location-based marketing resulted in higher sales for 9 in 10 marketers, followed by growth in their customer base (86%) and higher customer engagement (84%).
Personalized Customer Experiences
In-memory computing can help deliver real-time insights that will help provide tailored experiences to your customers. The power of this architecture lies in its ability to access and process data at increased speeds, which allows for the capture of real-time event data that can then be used to create personalized experiences. Real-time data ensures a seamless, convenient customer experience that would have been impossible without the necessary information and the technology to process the data at scale.
A common way to personalize the customer experience is by providing recommendations. Based on what a customer does or the pages the customer visits, real-time data analytics will provide insights that will help craft an appropriate response or action. Anticipating customers' next steps will keep them engaged and browsing or shopping on your website. Real-time data improves satisfaction and conversion rates, and ultimately, revenue because you're able to provide personalized offers at the right time.
Intelligent Supply Chain
The power to harness big data gives you the power to predict future inventory so you won't have issues with understocking or overstocking. AI-based systems allow for automation of some processes, saving time and labor costs in the long run.
There are available IoT-enabled devices that can gather data points from every aspect of the supply chain process, giving you access to data that can help drive improvements to your current process and discover inefficiencies that could use some enhancements. Ultimately, this data can also help optimize other aspects of your business, including field services and customer service. They can also be used to track stock, orders, and shipments so everything can be managed within a single system.
Dynamic pricing is, by no means, a new strategy; it's been used by marketers for years, though not systematically. in simple terms, it's the method of adjusting prices constantly, sometimes in a matter of minutes, to address real-time supply and demand patterns. This is a double-edged sword because, while it allows for greater control of pricing strategy, it can also give customers the perception that your online store can't be trusted due to the fluctuating prices.
On the other hand, it can also help get you more customers through promotional offers and seasonal discounts. These can be hard to sustain with a flat pricing model because there's little to no wiggle room to adjust your profit margins. Additionally, data analytics can give you information on the pricing strategy of your competitors so you can adjust your prices and campaigns accordingly.
Current technologies in machine learning and deep learning allow businesses to predict pricing trends and evaluate real-time data. Businesses can leverage this to generate personalized offers on the spot and modify customer purchase considerations in real-time.
The data-driven approach is gradually becoming the norm in business, especially in the eCommerce industry. As competition becomes more intense, online retailers must constantly overcome the challenge of being profitable while keeping prices competitive and providing a seamless experience for customers. Fortunately, in-memory computing and AI have come to the rescue, empowering businesses to gather and analyze data at speeds never before imagined.
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