These days, companies are striving to generate data troves, and by default, a competitive edge. However, these vast sets of data are not only made unfeasible but if not used properly, they may incur unsustainable data storage and maintenance costs. Organizations are introducing predictive analytics to take full advantage of these troves of data sets to see trends before they occur and make strategic decisions. When all sectors are nearly taking advantage of analytics, the retail industry is undergoing dramatic changes due to predictive analytics.
The retail industry uses predictive analytics to consider revenue growth, consumer behavior, and forecast broader business trends. Retailers in the industry generate more data than ever before, but their majority of datasets don’t always translate into successful outcomes. As there is much more information, and the competition continues to grow, and it becomes more challenging to turn information into new insights that provide them with an edge in gaining potential sales. These accomplishments are often claimed to be harder than accomplished.
How Retailers rely more on Predictive Analytics?
In the growing industry, many companies have progressively switched to predictive data analytics. Even in some industries, the technology can be as advanced as in retail. Predictive analytics may be the difference between a reliable revenue stream and a declining market pool as businesses in a sector where they excel in effectively reporting what consumers will prefer or want next.
Here are the reasons why retailers want to take advantage of these easy-to-deploy strategies to improve their business.
- Enhance Engagement and Shopping Experience: Today, retailers face one of the toughest obstacles in a commoditized market, transforming one-time shoppers into brand loyalists. Personalized marketing is thus more specific than the wide variety of marketing strategies, but this initiative in itself poses a challenge for retailers. So, here is predictive analytics, and businesses can take shopping experience personalization to a whole new level, and the insights gained can be optimized more. For example, several retailers, e-commerce giant Amazon, are already monitoring consumers’ habits, browsing history, shopping preferences, and more.
- Defining Behavior Analytics: In addition to transforming consumers into brand loyalists and enhancing shopping experiences, raising engagement rates, reducing customer turnover, and decreasing consumer acquisition costs are critical encounters that retailers tend to face today. Thus, retailers will obtain more in-depth insights by accepting data analytics into their operations, as it has resolved some of these issues. Insights include identifying high-value customers, causes, and patterns of purchasing and favorite platforms for transforming sales.
- Enhancing Customer Journey: Customers are increasingly discerning and looking for all sorts of information in today’s digitized era and discovering it through various numbers of platforms before making a purchase. Here, predictive analytics allows companies to be a step ahead of others, first, to be aware of what consumers are searching for and to reach out to them and provide them with the information they are looking for. This would allow retailers to recognize customer profile and their antiquity along with the various points of contact.
- Enhancing Operational Performance: Today’s dynamic marketplace and more innovation have shorter life cycles with compound supply chains and distribution channels in technology, goods, and services. Predictive analytics thus removes the ambiguity from the equation, empowering businesses to increase asset utilization and operational efficiency while advancing the quality of service. Additionally, retailers will reduce costs and increase income because it offers more reliable forecasts of demand. Taking advantage of predictive analytics will help retailers find the best times to roll down prices or slightly impel them in either direction. The studies demonstrating incremental price increases are more successful than sudden spikes.
By 2024 the global predictive and prescriptive analytics market is projected to hit $22.50 billion. The market was valued at $6.64 billion in 2018 and will expand over the expected timeline at a CAGR of 22.53 percent.