Benefits of Predictive Analytics for The Retail Industry

Running a business in 2020 comes with a considerable number of challenges. Especially when it goes about retail, with huge pools of data that don't always provide successful results. While retailers try to transform this information into unique ideas to attract customers, competition continues to grow.

The good news is that there is a workable solution you need — predictive analytics.

It has captured different organizations' support, with a global market that will reach about $10.95 billion by 2022. It is used in banking, healthcare, transportation, and any industry where customer satisfaction plays a crucial role. Netflix and Spotify are among companies that fully rely on data analytics. The retail industry appreciates these tools as well. Amazon and Walmart learn everything they can about their customers to optimize their marketing campaigns and get more profit.

What if we say you can put your name along with these world-famous brands using predictive analytics for retail?

Brands have conducted a lot of research interviewing customers or promising a discount for several insights. However, predictive analytics for the retail industry will show you: there's much more to discover. It is because your audience doesn't know it yet as well. As Steve Jobs once said, "Our job is to figure out what they're going to want before they actually do." This is when predictive analytics comes in.

While a lot of companies work on demand, your task is to create this demand. All the ways to succeed boil down to analysis. The analysis of the market, trends, and economic situation — retailers gather all this data and come up with new offers. That is how predictive analytics works. It makes predictions using data mining and modeling tools. Today, the popularity of these systems has reached such a level that there are companies that are providing recommender system as a service.

Algorithms determine how long the client will remain with your company and how you can retain them. From the moment of the first purchase, you already receive valuable information about the client: their email, demographic and geographic characteristics, etc.

Comparing a new customer with regular ones, you can accurately predict one's value throughout the life cycle. This kind of information helps to make strategic marketing decisions. In this case, predictive analytics in retail is like a sales funnel with efficient stages for your business. The main task is to strengthen all links in the chain.

This algorithm analyzes how customers behave when making a purchase. Thanks to it, you will know:

• If your customers use a website or call center;
  
• If they buy more during promotions;
  
• How often they shop;
  
• How much money they are spending;
  
• The time between purchases.
  

Analyzing customer behavior, you can choose the right way of targeting and communicating with the client. For example, people who buy products with discounts only are more likely to be interested in all-out sales at extremely low prices. And vice versa, customers who typically buy at full price are perfect for new product promotion.

Projects in big data provide the basis for performing subsequent analysis and processing of data. Predictive analytics uses cumulative data to predict what a customer is likely to buy next. Based on this, it generates appropriate recommendations, suggesting a sleeping bag or a set of camping tools when you are looking for a good tent.

Back-office operation is an area that is often overlooked. Operational efficiency is impossible without optimizing supply chains. Use predictive analytics in the retail sector to answer questions like what to store, when to store, what to discard. You should always have control of long-term storage goods and a stock of popular ones. With this tool, you won't make purchases based on guesswork.

This algorithm determines the brands and products preferred by customers. When a brand launches a new product, it is essential to understand how to promote it to boost your sales and satisfy the customers' needs.

The analysis of trade preferences provides in-depth data about products from other manufacturers that may interest your audience. For example, people who prefer brand A are also willing to buy products from B or C, but will not show interest in brands like D or E.