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Automated Catalog Listing
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A single platform to onboard sellers as well as manage their product information, orders and inventory
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Automatic Replenishment System
Ensure on-time deliveries and fewer stock-outs through automating replenishment plans and orders
Open to Buy
Take control of your inventory investments by monitoring financial budget for purchase orders
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Business Data Model
Easily define business-critical master data entities
Easily manage multiple hierarchies and its data across channels
Business Rule Engine
Configure various set business rules for the master data attributes easily
Manage the master data requirement through data governance processes and policies
Ensure the overall standard and accuracy of data through
Vendor / Seller, Product / Material Management
A single platform to support various ways to register the product/material in the hub
Data is everything, and insights from the interpretation of data stand to change the face of businesses today. However, companies, by and large, have been slow to adapt and incorporate the benefits of data. While, many have found success in generating, collecting and storing troves of data, few have been able to analyze it and derive relevant insights for their businesses.
The matter takes precedence, especially in the context of e-commerce, wherein sizeable amounts of customers’ information gets stored in the CRM systems, in-app analytics and conversations with chatbots among other tools. So, while companies have formatted fairly structured ways to collect data, little has happened in the spheres of interpreting and analyzing such data.
Moreover, given the nature of the business, direct access to consumer-data gives e-commerce the ability to most effectively apply its learnings from data management and analysis. So, when data is unstructured and requires detailed sifting, the task can bear down heavily on costs for the company. However, if companies are able to clear this hurdle via technology, the benefits they stand to gain are huge.
How can data accelerate growth? Here are a few ways.
One of the greatest benefits that can come from data analysis is improved demand forecasting, which will help e-commerce retailers manage their inventory and active merchandise more effectively. By creating consumer profiles based on individual preferences, spends and purchase timing, retailers will be able to create more value for their users, as well as rationalize their own expenses.
Some critics find it pointless to collect raw, unstructured data and store them in the form of data lakes, till the time that it is utilized. By their very nature, Data Lakes gathers data without creating hierarchies or any restrictions. So, for some data scientists, this presents two key opportunities:
To reduce costs involved with data management and complexities, a handful of companies have gone to data lakes. For example, Vicomi, an emotional intelligence firm for online services, faced an uphill task when it came to producing adequate insights. Though, once it switched to a data lake architecture powered by Upsolver, there was a significant reduction in costs with development time for new analysis models, predicting new trends and reaching out to more customers.
For dynamic companies that are constantly reviewing their consumer insights and their own business model to adapt to changes, data lakes offer flexibility. As they constitute diverse marketing campaigns, data lakes can help customize, and target their marketing efforts by producing new ways to filter that data.
Data lakes take a more comprehensive approach by allowing customization of analytical models, instead of restricting the data into pre-defined models. This has driven more and more companies to choose data lakes as the answer to their business needs.
According to a survey done by Tractica, artificial intelligence in e-commerce is growing at a healthy pace, and worldwide revenues are expected to touch $36.8 billion by 2025. One of the best examples for this growth is global e-commerce mammoth Amazon, whose annual revenue is measured at $100 billion.
Dynamism in the strategy and product-market fit has brought e-commerce to the top of its game. What started out as an online bookstore, was quick to adapt and cater to every growing need of the consumer, from fashion to electronics to now even grocery.
Here it goes beyond simply analyzing data. Detecting the underlying problems, gaps and demands in the market involves the need for a superior algorithm. In order to use artificial intelligence and to derive sense from the data lake, there needs to be a better approach that brings advanced systems to the fore. Existing processes need to be combined with machine learning and systems that can provide feedback that can be applied on a business level. This can be achieved through artificial intelligence e-commerce and machine learning.
An analysis of the user’s buying patterns, the platform, on its end, is able to make predictions about what he/she will buy next. And these prediction work on up-selling and cross-selling i.e. when a user looks at a product, he/she will see a product recommendation, in the form of – ‘customers who bought this item also bought’ (in the case of Amazon) or combination deals, wherein a discount is applicable upon the purchase of a related item.
This happens through artificial intelligence and machine learning. Hence, the more the transactions, the more personalized the suggestions become, as the platform gathers more relevant information about the user. This benefits the buyer as well as the seller, where the former’s transaction is made more convenient and the latter is able to make an extra sale.
In a case study, it was found that data mining of the buying habits of customers allowed yet another retail giant to predict that a woman was pregnant. By formulating a set of criteria, the store managed to derive a set of women, who would soon be expecting a child and optimize their marketing and advertising efforts to encourage the purchase of relevant items. While some may perceive this to be a step too far in the invasion if customer privacy, some think of it as effective marketing.
But artificial intelligence in e-commerce is not elite in its ways and has even found application at small businesses’ level.
While getting into the depths of data insights, it is important to note that it is not just about predicting problems but also offering a platform to devise solutions and create more value for the customer. That being said, the opportunities therefrom become limitless for the application of artificial intelligence and machine learning in the ocean of e-commerce.
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