Innovative technologies have driven eCommerce since its inception, so it’s no wonder that the advancing technological possibility of evaluating large amounts of data is especially interesting to online retailers.
Such so-called big data analytics is intended to increase sales, create better shopping experiences and ensure higher customer satisfaction. These are no small goals. With the following big data analytics, you can take your online shop to the next level.
Get Detailed Insights into Your Customers with Big Data Analytics
Countless online shops have been experimenting with it for years: Personalization of the products and content shown is intended to ensure that, in the best case, you offer your customers only relevant products that correspond to their purchase behavior and personal preferences.
The best way to achieve this is to respond to current usage habits. For example, you can focus more on mobile eCommerce, as with progressive apps that offer the user experience of an app as a mobile website. In addition, automated product recommendations are a powerful tool to boost your conversions and therefore your sales figures.
This seems to be bad news for you as an online retailer, but the first answers to this problem are already in sight. Among the alternatives being discussed are technologies such as digital user tokens that give individual customers more control over how they use their own data and Google’s Privacy Sandbox. This concept of the internet giant proposes placing customers in certain segments in order to ensure greater individual privacy. So you’ll still be able to get to know your customers in detail based on valid data.
Better Than a Crystal Ball: Big Data Predictions
You’d like to know now what your customers will buy from you in the future? It may sound like magic, but it’s nothing more than a data-based prediction. Big data offers you the opportunity to carry out such future-oriented analytics and thus, among other things, to optimize supply chains and save storage costs or to identify future product trends early on.
To accomplish this, neural networks examine both historical data that provides information about certain demand developments as well as current data such as the latest sales, orders, unsold stock or deliveries in order to improve the accuracy of forecasts and learn from mistakes.
For many years, the prime example for the possibilities of such forecasts has been Amazon with its famous lean inventory management, which is supposed to reduce stock levels to the minimum necessary. This is only possible through excellent big data analytics.
Intelligent Price Optimization – Finding the Optimum Price with Big Data
Big data not only provides you with valuable knowledge about your customers or future demand but also helps you with pricing. How are product prices developing currently, globally or regionally? To what extent do your competitors differentiate themselves through prices?
Given dynamic price changes and a globalized market, it’s nearly impossible in eCommerce to gain a comprehensive overview of the current pricing policies of all competitors manually. However, price developments in the market quickly impact your sales: If you only have limited insight into current market developments, you may lose potential buyers or your margins may shrink due to unwanted discounts.
Intelligent big data analytics tools like blackbee Insights can help. They provide you with a comprehensive overview of relevant competitors, their product assortments and pricing strategies. For example, you can track how other suppliers behave in the price-intensive phases before and during large discount campaigns. With a solid database and accurate analyses, you can optimize your market performance with minimal effort.
You’d like to know more about how our big data analytics tool blackbee Insights supports you in defining a competitive pricing strategy? Our pricing experts are happy to assist you.