Predictive analytics in retail and eCommerce

Predictive analytics in retail and eCommerce

Have you ever wanted to know ahead of time what services or products your consumers are likely to purchase?

Predictive analytics ecommerce could be the answer. The software assists retailers in determining the highest price a customer will pay for their products, as well as analyzing buyer behavior, inventory management, and customer turnover.

That’s only a small part of what this “forward-looking” study may accomplish.

We’ll go through predictive analytics ecommerce, their advantages, and how they’ve changed shopping in this tutorial. We’ll also look at some real-world applications of predictive analytics at organizations like Netflix, Amazon, and others.

What function does predictive analytics in retail play?

Predictive analytics in retail is a collection of tools and methods for analyzing data. Future forecasts and hidden patterns are found using analytics-based technologies. For example, analytics solutions are used when an online business proposes adding specific products to your shopping cart.

Almost anything may be predicted using data modeling, machine learning and artificial intelligence, data mining, deep learning algorithms, and mathematics. Predicting the position of a comet in the sky, early symptoms of Parkinson’s illness, and debt collection predictive models are just a few examples.

Data analytics is most commonly used in marketing campaigns to forecast long-term product demand or anticipate a specific user’s behavior. Of course, retail organizations benefit from this technology because they store a lot of client data, such as purchases and shopping patterns, as well as their location.

Retailers may predict customer purchase behavior, trends, future activity, and even the amount of money a shop can generate in a month using all of the data. They track customer behavior and market trends to forecast changes and assist managers in making real-time, data-driven choices, giving them a significant competitive advantage.

Let’s take a look at some of the most common predictive analytics ecommerce applications.

Recommendations for Products

Before recommending purchases, the online retailer considers not only your purchase history but also factors such as the current season or items you’ve recently added to your cart. Amazon suggests products and services based on a user’s previous actions, such as browsing and purchasing history. Up to 35% of sales are generated as a result of their recommendations.

Due to retail’s forward-thinking research, an internet business will never recommend a frying pan for steaks to someone who does not consume meat.

By the way, Netflix’s popularity is largely due to its intelligent suggestion algorithm.

Netflix’s software engineers devised algorithms that steered viewers away from popular blockbusters and toward lesser-known shows like “House of Cards” and “Bird Box.” In the first week after its release, 45,037,125 accounts viewed it.

Formation of Prices

Many businesses were able to transition from fixed to dynamic pricing as a result of the forward-looking study. For example, Airbnb pricing is determined by analyzing a variety of trends such as season, day of the week, near-events (such as film or music festivals), holidays, and so on. The Airbnb software proposes that landlords choose an amount for which they will most probably rent out their flat based on the data.

The cost of a ride is determined by traffic, weather, and other factors in Uber, Bolt, and other taxi aggregators. This is one of the predictive analytics retail examples. 

The same can be said about predictive analytics for retail: retailers utilize machine learning algorithms to determine prices, and once these prices are determined to be ideal, they begin selling even more.

Sales have increased.

You can also use predictive analytics for retail to assess the quality of each individual ad campaign and eliminate unproductive ones that waste your money. This way, you can concentrate just on profitable campaigns.

Macy’s, a department store, is the ideal retail predictive analytics case study. By sending customized emails based on customer data, the company was able to improve revenues from 8% to 12% in three months. A 4% rise is a big volume for such a large retailer.

Retail predictive analytics of goods enable retailers to research individual consumers and subgroups in order to provide them with the appropriate product. This is when customer segmentation comes into play. Furthermore, it enables retailers to interact only with interested customers rather than random ones.

Storage & Logistics

Both physical and online benefit from analytics use cases in retail. For example, you may learn how people go around a supermarket by analyzing videos from surveillance cameras, as well as what discounts they pay attention to and what commodities they are interested in (but not enough to purchase them). It’s simple to change the marketing plan to attain better outcomes now that you have this information.

The Amazon Go chain operates without any employees: the system analyses a customer’s shopping cart, what products are missing from the shelves, and so on. Analytics calculates the amount of stock that needs to be replenished, revealing exactly what has to be reordered and what is in excess.

Of course, the technology isn’t just for the retail industry. Many industries, including healthcare (allocating beds inwards), aviation (learning about potential faults), finance (analyzing fraudsters’ conduct), and others, use predictive analysis.

Predictive analytics retail examples

This technology may appear to be too expensive to be used by anyone other than well-known firms like Walmart or Amazon. However, these behemoths aren’t the only ones who use predictive analytics for retail.

Let’s look at a few lesser-known businesses that use it.

Geo-Tracking, Auchan

Auchan uses geo-tracking to inform customers about neighboring stores. When people are inside the business, they can also receive push notifications about current specials or items on sale. That’s a fantastic method to build a strong consumer base. And the more devoted your clients are, the more money they’ll spend with you.

Auchan, a large retailer, collects data from a variety of sources all at once. The sales data they acquire at the sales counter’s point is still the most important information.

This is how they can predict which things will sell well and which will not. Data can also be gathered through engaging with customers personally, observing their behavior, and soliciting feedback.

Inventory Control, Carrefour

Carrefour, a French retailer, employs AI analytics use cases in retail in its operations. To improve inventory management, they deployed Viya, a SAS AI-powered program. To forecast demand and provide orders, the platform gathers information from warehouses, retailers, and online. It aids Carrefour in reducing stock outages and overstocking in its stores and warehouses.

Battling Customer Churn at

Customer churn prediction software is designed to detect straying consumers before they transfer companies or service providers. Of course, you should be aware of such attempts and, in an ideal world, be able to prevent them from happening again.

Analytics use cases in retail can help you figure out which customers are most likely to leave and when. One of the predictive analytics retail examples,, a French e-commerce site, manages client attrition through forward-looking intelligence. They assess each new client and utilize the information to create targeted marketing efforts. Customers respond more to ads because they are more tailored.

Wrapping Up

The final line is that developing retail predictive analytics is a lengthy and complex process that should be left to analytics and data science experts. Working with prediction models necessitates an understanding of big data, artificial intelligence, and machine learning. The majority of the time, retail software development is handled by an IT firm. The procedure does not refer to a single program, but rather to a collection of data-processing approaches.

That’s why, in the retail market, billion-dollar companies like Netflix, Amazon, and Walmart can usually afford predictive analytics.

That isn’t to say that smaller businesses can’t benefit from predictive analytics. They are capable of doing so. They have the option of using ready-made third-party solutions or outsourcing development to a different country.

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