Big Data analytics is the new goldmine.
If we take a good look at the past year, we will see how technology has helped retailers overcome the challenge of e-commerce. Through data insights and tracing consumer behavior patterns, retailers have been able to effectively execute marketing campaigns to meet the ever-rising expectations of consumers. The core element of this digital transformation is big data analytics, which has placed a paramount role in the transformation and digitalization of the way retailers are now operating.
Which Analytics trends will progress the future of retail?
Retailers are now making use of data analytics at every touch point in their business.
From predictive sales to optimizing products on shelves, everything is based on data insights.
Now retailers are looking at evolving these strategies to keep pace with the ever-transient market and follow trends to enable them to progress into the coming years. Some trends to look out for
in the coming year are-
1. Personalized Customer experience with Prescriptive Analytics
Predictive analysis has been the median in retail to forecast demands and footfalls to personalized consumer experience through predictive analysis. But the main challenge for retailers who are competing with giants like Amazon lies in pricing. Now armed with prescriptive analysis, retailers can now take this challenge head-on by analyzing different types of data such as location intelligence, customer trends, product availability, peak hours and much more. This allows retailers to optimize profit margins by capitalizing on a different number of opportunities available.
2. Dynamic pricing through data analytics
E-commerce often resorts to dynamic pricing to provide the best deal for the customers. Until recently, prices were governed by the market forces of supply and demand. But with the AI entering the field, the sellers also have to take into account the consumer’s perspective, i.e. how much is a consumer willing to pay for a certain product or service.
The concept behind AI is machine learning; that means adjusting algorithms based on the latest data patterns. To state simply, the software creates algorithms to identify patterns from data and predict prices based on that info. In 2014, Amazon came up with a brilliant technology that revolutionized algorithms, for which is received the predictive stocking patient. It allowed the retailers to reduce their delivery time and cost by predicting which consumers were going to buy a certain product and at what time, even before they actually bought it.
3. Store operation optimization
Store optimization is a major challenge faced by many retailers today. Allocating different numbers of staff to address various shopping needs based on the trends, seasons, occasions etc. This is where data analytics comes in handy. Data analytics enable retailers to more effectively manage store operations by optimizing the staff based on various scenarios and trend data.
Zara exemplifies excellent store operation optimization
The best example of store operation optimization is Zara. While most of the fashion stores run on a bi-annual or seasonal basis, Zara runs its clothing line according to the latest trends. This only ensures that they have no remaining stock by the end of the season but also that they are not lacking in the fashion department. Zara has a team of designers, who constantly work on the feedback received from the sales team about product movement and consumption. This not only helps them design things that the consumer likes but also lets them be updated to the trends.
4. Omni Chanel Data Utilization
The retailers today are collecting a huge volume of consumer, sales and loyalty data. These come from various sources. As these sources increase- maintaining, managing and analyzing data has become a huge task on its own. Retailers are trying to manage this by employing data scientists, who analyze and manage this data. But with an increased focus on automation, it will soon be seen that retailers are deploying cutting-edge retail analytics software, which brings all the info together to get a holistic view of the company’s overall performance.
5. Sales growth via product assortment
When are we trying to find the conversion and sales in-store, then product assortment plays a primary role. It is widely known that retailers who failed to plan their product placement have faced devastating impact on their sales. Upon reviewing shopping patterns to understand correlated products that are purchased together, retailers have been able to increase their sales. With the help of in-store analytics, retailers can integrate store customer behavior data to purchase history to uncover shopping patterns. Looking into future trends, it is predicted that data analytics will enable retailers to become more aware and proactive with product assortment.
6. Rewarding consumers through data analytics
While e-commerce is gaining more and more popularity, 96% of the sales still take place in the brick and mortar store. The underlying message here is that people don’t only care about discounts. They also care about another thing, i.e. consumer experience. Consumers who are loyal to their brand are seeking privileges, and retail can deliver upon those through their in-store retail experience and analytics platform.
7. The connection between retailers and suppliers
Due to access to various tools and analytics technology, data sharing is becoming more streamlined. With the ability to forecast demands and shopping patterns, retailers and suppliers will be able to improve efficiency and reduce the cost of managing and purchasing deliveries.
Looking to see explore how big data analytics can transform your business? Contact us at Datahut, your big data experts.