Did you know that the retail sector is one of the most competitive industries? Retail industry growth by 2023 is expected to reach $ 29.763 trillion.
When we talk about the retail industry, we mean the sector that focuses on the sale of finished products to end-users. The companies in these sectors are one of the most popular ones like Walmart, Amazon, Costco, etc.
With all of this in mind, it’s no wonder that retailers are fighting a fierce battle with each other to attract new customers.
Predictive analytics has proven to be one of the best tools to gain the desired competitive advantage for retailers and has been increasingly implemented since the discovery of its benefits for businesses.
In this article, we’ll delve into the reasons that make predictive analytics a go-to tool to drive retail sales.
Our calendars are full of special events and dates that are associated with different holiday offers. Customers often wait patiently for these days to get their hands on the desired products for less money.
This increase in sales of specific products often leads to a shortage of stocks, causing retailers to lose additional profits. With a wide range of factors such as holiday sales, trends, etc., predictive analytics uses its statistical algorithm and machine learning techniques to predict future demand for certain products with high precision.
It is important to understand that predictive analytics does not provide a forecast based on past sales, but rather includes other influencers who may also have an impact on demand.
Forecasting customer demand not by instinct, but by predictive analytics, ultimately helps retailers properly prepare for holiday events and ensure that supply matches demand. In this way, customers are satisfied due to the increased availability of the desired product and retailers are happy as they make more sales.
Customer behavior prediction
It is a well-known fact that understanding customer behavior is vital for businesses as it also clarifies what makes a consumer use a product/service. That is why companies invest a lot of resources in customer behavior analysis tools such as google analytics, heat maps, and surveys.
But can you imagine if companies could not only know the current patterns of customer behavior but also predict them? Well, it is not necessary to imagine it because this is already a reality.
We already talked about how predictive analytics forecasts customer demands on certain products. However, this technology goes one step further and can even predict which customers are likely to leave your business.
With the availability of big data, smart algorithms can compare the behavior of specific customers with that of the customer who churned and based on that information, warn a business of a potential loss.
In this way, companies can send promotional campaigns to retain customers with a high probability of abandonment. Such predictions are invaluable, as acquiring a new customer can cost 5x more than retaining an existing customer.
Today, in the retail sector, many processes such as package collection and allocation are automated. In fact, intelligent automation is one of the most important factors for reducing costs and increasing efficiency.
The daily operations of many retailers depend on the flawless functioning of this automated equipment. However, it is impossible for equipment to function flawlessly and not require maintenance from time to time.
This is not an easy problem and causes big problems for retailers, ultimately resulting in dissatisfied customers and huge expenses.
While it is challenging to ensure permanent good health of equipment, it is possible to predict the probability of failure and be proactive rather than reactive.
By collecting data with the help of IoT on equipment health, production data, ambient temperature, etc., intelligent algorithms can predict the probability of equipment failure, resulting in:
In an industry as competitive as retail, predictive analytics is a powerful tool for companies that ensures a competitive advantage and helps them make sense of structured and unstructured data and turn it into actionable information.
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