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5 Best Applications of Predictive Analytics in Retail

Software Testing in Retail

In recent years, the retail industry has faced supply chain disruptions, changes in customer behavior, and other challenges negatively affecting business. Only a few retailers have been able to meet these challenges without any financial loss; even fewer have been able to thrive under such harsh conditions.

At Itransition, we believe that retailers can mitigate many business disruptions by adopting predictive analytics in their technical stacks. The technology allows forecasting market fluctuations and future trends, helping retail enterprises adapt to ever-changing market conditions.

This article covers the concept of predictive analytics in retail and provides five top technology use cases.

Why use predictive analytics in retail?

According to the 2023 Predictive Analytics Market report by Reports Insights, the predictive analytics market reached $13.5 billion in 2022 and is expected to exceed $44.3 billion by 2030. Such statistics show that increasingly more enterprises across multiple industries are adopting predictive analytics, and retail is no exception.

So what are the reasons behind this transition to predictive analytics? One of them is the natural limitation of descriptive analytics, commonly used by retail enterprises to analyze historical data.

The point is that descriptive analytics can highlight only those things that have already happened. However, the results of the descriptive analysis cannot provide the complete business picture and help in anticipating what will happen next.

Predictive analytics can overcome this limitation. Although it also works with historical data, predictive analytics can make forecasts based on this data using advanced techniques such as data mining and machine learning.

In practice, predictive analytics help retail enterprises take a more data-driven and proactive approach to decision-making. This advantage allows retailers to reduce risks and enhance performance and service quality, increasing business competitiveness.

Top five use cases for predictive analytics in retail

Here are some business areas where retailers can apply predictive analytics:

1. Market trends forecasting

Enterprises can fuel their predictive analytics solutions with market and customer data to generate short and long-term market predictions. Thus, retailers can plan their future activities more accurately and efficiently, relying on data instead of guesswork.

The technology can help retailers anticipate volatile customer behavior and demand. For example, a retailer might analyze factors such as product views, customer interests, or past purchases to understand customer needs better and thus gain an ability to meet them.

In an alternative scenario, retailers can use predictive analytics to analyze the market potential of their new products. For example, a retailer can study the sales dynamics of similar products in specific local markets to understand whether a new product would be in demand in these regions.

Among other things, retailers can use analytics and empower it with machine learning to analyze customer data from marketing channels such as websites or mobile apps. The results of this analysis help create more targeted and effective marketing campaigns that attract more customers at a lower cost and effort.

After all, predictive analytics can forecast a retailer’s future sales and earnings. For example, a retail enterprise can forecast revenue for the next year to calculate its annual budget and finances more accurately and adjust its business development strategy.

2. Inventory demand forecasting

With predictive analytics, retailers can analyze sales and market data to predict inventory demand. Typically, such an analysis involves a combined study of all factors that can influence consumer demand; these factors can be external (weather conditions) and internal (marketing activities of a retail enterprise).

Inventory demand forecasting allows retailers to achieve two significant business advantages. On the one hand, with more accurate planning, retailers can mitigate overstocking and thus avoid unnecessary expenses. But on the other hand, retailers can guarantee they always have enough stock to meet customer demand continuously.

3. Dynamic price optimization

With dynamic pricing, retailers can more precisely calculate prices for their products, tailoring them to current market demand. Likewise, with the right analytics solution fueled with the correct data, enterprises can adjust their prices even in real-time, thus maximizing growth and revenue generation opportunities.

Depending on the retailer’s needs, dynamic pricing algorithms can take into account different types of data when determining the prices of specific product positions. For example, retailers can analyze sales rates of certain products, collect pricing data from their competitors, or use information related to their business costs and expenses.

4. Supplier risk forecasting

By predicting supplier risks in advance, a retailer can avoid them, thus building a more healthy and more robust supplier ecosystem. A retailer can easily achieve it today using a powerful predictive analytics solution.

For example, predictive analytics can assess suppliers’ financial health, technological capability, or associated environmental and political risks. Augmented with machine learning capabilities, such a solution can identify the strengths and weaknesses of an existing supplier network and provide recommendations for improving it.

5. Predictive personalization

Predictive personalization technology helps retailers analyze consumer behavior and preferences to tailor products, offers, and content to customers. By using this technology and empowering it with relevant machine learning models, retailers can automate marketing across multiple channels while making it even more engaging and resulting.

Final thoughts

Market fluctuations, changes in customer behavior, supply chain disruptions, and other hard-to-predict factors make the retail business more challenging and riskier than ever. Fortunately, retailers can prepare themselves for these disruptions with predictive analytics technology at their stacks.

Using predictive analytics solutions, retail enterprises can analyze historical data to make accurate forecasts and plan further actions based on them. Market trends and inventory demand forecasting, dynamic price optimization, supplier risk forecasting, and predictive personalization are just some use cases of predictive analytics in retail. 

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