Why do some retailers succeed while others fail?
Why do some stores know exactly what their customers need and supply it, while others rely on guesswork and make wrong decisions?
Why do some brands seem one step ahead, predicting trends before they happen?
The answer is, successful brands have been leveraging retail predictive analytics.
Although being challenging to implement, the technology provides a world of benefits that put businesses ahead of their less tech-savvy competitors. And we’re talking not about just boosting sales — predictive analytics for retail is capable of much more than that. It creates meaningful, smooth, and enjoyable customer experiences, helps build resilient supply chains without stockouts and overstocks, and provides actionable insights for prudent decision-making, so you always stay on top of ever-shifting trends.
In the following paragraphs, Unicsoft explores the revolutionary role of predictive analytics in the retail industry. We dive into its benefits, showcase popular analytics tools that your competitors use, and review prominent case studies, showing how real-world brands have used the technology to gain a competitive edge.
Read on to learn more!
What is retail predictive analytics?
Retail predictive analytics refers to the technology that uses your retail data (what your customers buy, when they buy it, what products they tend to explore, and other relevant information) to forecast future outcomes, i.e., predict what your customers might want in the future. Think of it as peeking into customers’ minds without breaking ethical laws. Rather than relying on guesswork or your “gut feelings,” you have access to past trends and patterns that enable you to make smarter decisions about your inventory, pricing, and promotions.
Retail predictive analytics is not a silver bullet that would instantly make your business ride high. It’s more of a helping tool that allows you to plan ahead and be aware of emerging opportunities. It can help you predict when the demand for a certain product/service will spike, what products will be trending next season, and what kind of offers can encourage your customers to click on that “buy now” button. It will help you create smoother shopping experiences and thus dramatically boost sales and conversions. Whether it’s suggesting the perfect product or making sure shelves are stocked at the right time, retail predictive analytics enables you to stay one step ahead in a world where customers expect top-notch service.
Why predictive analytics makes retail data more valuable
Retail predictive analytics isn’t about giving you a 100% accurate answer to something that has yet to occur. It’s a complex technology that produces actionable forecasts based on what has already happened or is happening right now. To make these forecasts, it draws on data inputs and machine learning (ML) models. The basic procedure works like this:
- You gather data.
- You set up a machine learning (ML) model to distinguish data patterns in historical data.
- You give the ML algorithm new data, and it makes predictions based on the patterns it found in the historical data.
When it comes to using predictive analytics in retail, the ML model is often designed as a neural network — an adaptive, self-improving deep learning method of data analysis. The data can range from procurement volume and cost to what’s trending and which products customers purchased most in your stores.
As a retailer, you’re apt to already use this kind of data every day, and so far, so good. But you underuse it by analyzing it in hindsight to figure out what you could have done better in the past. Predictive analytics offers a way to act on your data now to reset your business for a better outcome in the future.
Benefits of using predictive analytics in retail, storewide
Your reward cards, sign-ups, POS systems, and inventory trackers all provide abundant data sources for predictive analytics. You can extract detailed data from them, but take care that collecting certain data doesn’t violate privacy laws or ethical guidelines (so yes, you may need to first ask your customers for permission to use their personal data).
If nothing bars you from using the data, there’s no better way to spend a dollar than on adopting predictive analytics in retail. Read on to learn why.
Reduced stockouts
Remember the recent holiday season? The retail frenzy when 60% of shoppers encountered an “Out of Stock” or “On Its Way” sign? Well, if you’d used predictive analytics to prepare for the holiday season, your customers would have been much happier.
Stockouts are a pet peeve for every retailer. But retail predictive analytics gives you the power to minimize the stockout rate in your stores. By analyzing last year’s sales for the same period, current inventory levels, shipments, deliveries, and other metrics, you can be certain when your procurement team needs to shift into a higher gear to keep your shelves brimming with products.
More sales
Global retail sales are projected to reach $32.76 trillion in 2026, reflecting a robust and expanding market. In the United States, consumer spending is anticipated to grow by 3.1% this year, with significant contributions from sectors like durable goods and general merchandise.
Retail predictive analytics gives you multiple options to increase sales. With extensive empirical data baked into an ML model, you can act on the shopping patterns and behaviors of those who visit your stores. Behavior predictions can help you target specific cohorts of shoppers, create sought-after bundle deals, cross-sell products, and retain big spenders on the verge of leaving forever.
No missed opportunities
It’s good to know what products you should have in abundance when shoppers’ interest in specific goods or brands is about to grow.
With retail predictive analytics, you can forecast customers’ needs months in advance and update your inventory or pricing. Smart predictions present opportunities to tap into a greater share of the industry with $27.3 trillion in sales worldwide by increasing your stock of trending products or adding new ones to your shelves.
Excellent customer experience
If you don’t provide the best customer experience (CX), the days of your retail business are numbered. Today’s shoppers want to put minimal effort into making purchases. For example, in the US and Canada, 65% of consumers think businesses deliver a subpar CX, indicating that retailers need to up their game to provide exceptional customer experiences.
Adopting predictive analytics in retail stores can set your stores apart from competitors whose CX is lacking. Based on how customers shop, predictive analytics can help you create personalized offerings, which increases shopper satisfaction.
Well-timed scaling
Whether you’re thinking about opening a second or a thirty-second location of your business, thorough planning goes a long way. Planning with retail predictive analytics goes even further.
You can feed your ML model data that’s more than store-deep. For instance, you can use geography-specific data, like retail sales by region, most in-demand items in an area, and rent for commercial space within a given territory to plan your expansion strategy. With that, ML can forecast whether opening a new location in a certain area is a solid move or bodes ill for your business.
How to Use Predictive Analytics in Retail: Tools, Use Cases & Examples
Predictive models can serve many functions in retail. Which you leverage will depend on several things, including data availability and integrations. But the most important factor is the purpose of forecasts.
The specific use cases for predictive analytics in retail determine what kind of data you’ll need to train your ML models. Let’s explore some red-hot use cases for retailers before you can proceed with predictive functionality for your business software.
Stock management systems
While a typical stock management system red-flags products, meaning that it warns about products that are sold out or nearly sold out, a prediction-based system looks ahead. To make forecasts for efficient inventory management, it uses supply chain data, daily turnover numbers, and trend analytics. Here’s a closer look at a stock management system with a forecasting capability.
Behavioral analytics tools
Nothing is more temporary than a shopper swearing by your product today. Behavioral analytics tools can analyze reviews and social media posts to forecast shopper interests and needs that may affect your retail sales in the future. Advanced ML algorithms look at spending habits, demographics, trends, and shifting attitudes on an omnichannel basis.
Demand planning solutions
These solutions closely correlate with stock management systems but may not take the inventory level into account. Instead, they zero in on preselected drivers of retail demand, seasonal fluctuations, in-store deals, and discounts. The forecasts then show how much you can expect in sales at each location.
Price optimization solutions
Are your decisions only a price tag away from causing sticker shock or improving your bottom line? It’s hard to tell without data-backed predictions. These can factor in your current pricing, your competitors’ pricing, GDP forecasts, and shoppers’ motivation to predict the effect of changes in the prices of your products.
Geo analytics tools
Predictive modeling is at the core of tools used to optimize delivery routes or identify the best locations for retail opportunities. It can predict everything from delayed delivery times to dwindling consumer spending in a selected region. You can use these forecasts when deciding how to get through shipping disruptions or whether to open a new location or close an existing store.
E-commerce tools
Are you all in on e-commerce? Retail predictive analytics is as helpful for online sellers as for those who run physical stores. Amazon exemplifies the income-generating use of predictions and uplift modeling by creating shopping recommendations. You can follow suit to identify cross-selling and upselling opportunities as you assess the likelihood of purchase with the ML model.
Now, let’s take a look at how big brands leverage retail predictive analytics to stay ahead of the competition.
Amazon
Amazon’s recommendation engine uses retail predictive analytics to analyze customer purchase histories and browsing behavior. It suggests relevant products to customers, significantly increasing revenue and customer engagement. According to Amazon, this personalization contributes to a whopping 35% of sales on the platform.
Walmart
Walmart employs retail predictive analytics to optimize inventory management across its extensive store network. This reduces excess stock and prevents stockouts, leading to millions of dollars in annual cost savings by maintaining optimal inventory levels and improving supply chain efficiency.
Starbucks
One of the most prominent predictive analytics retail examples is Starbucks. The coffee giant uses retail predictive analytics to personalize promotions and marketing campaigns. This approach has increased customer engagement and loyalty by 10% year-over-year, boosting sales and improving customer retention.
Zara
Fashion retailer Zara utilizes retail predictive analytics to enhance its agile supply chain strategy. By analyzing sales data and customer feedback, Zara quickly adjusts production and inventory levels to match current fashion trends. Thanks to this responsiveness, the store minimizes excess inventory and ensures that popular items always remain readily available, so the customers are satisfied.
Nike
Another curious retail predictive analytics case study is Nike. The brand uses the technology to personalize customer experiences and streamline inventory management. It analyzes data from its apps and IoT devices to anticipate purchasing behavior and tailor product recommendations. This data-driven approach enhances customer engagement and optimizes operations across the supply chain.
What to look for when adopting predictive analytics in retail
Not to take the wind out of your sails if you’re ready to adopt retail predictive analytics for your business, but don’t expect smooth sailing when getting started. Why? Because the adoption process can be stormy with huge waves lapping against your boat.
Clean data
For starters, you need to train an ML model with data that is:
- Neatly extracted and abundant
- Relevant to specific use cases
- Accurate and clustered
Issues may arise with each criterion. While you may have a lot of data across your POS systems, CRM software, and inventory management solutions, bringing it all to a centralized space in a shareable format is impossible without the right ETL (extract, transform, and load) process. This should be followed by identifying which data types apply to the intended use of predictive analytics and refining them to eliminate duplicate entries and ambiguous data.
Expertise
Another roadblock of adopting retail predictive analytics is acquiring the necessary expertise. You should hire the best talent you can to train an ML model for your retail business. Ideally, you want to look for specialists with the following qualities:
- Knowledgeable about big data and data analytics in retail
- Fluent in R and/or Python programming languages
- Trained to apply ML modeling and deep learning technologies
Actionable data visualization
Last but not least, you need something you can act on after your ML model generates predictions. A best practice is to create dashboards to visualize forecasts for each use case that your decision-makers can explore with ease. For example, you might have one dashboard for employees who make decisions about your supply chain and another for employees concerned with sales trends. This way, decision-makers get automated recommendations based on prescriptive analytics and then create an efficient action plan.
For visualizations and recommendations based on retail predictive analytics, your team can deploy either of two kinds of ML models:
- Standalone web service
- Part of your existing inventory management, ERP, or other systems
The ideal deployment option for predictive analytics in retail is the one you find to be flexible and convenient. If you’re of two minds, an expert opinion can help.
A quick recap and next steps
Retail predictive analytics unlocks a world of benefits for your business. With data-based forecasts, you can deliver a better CX and maximize your profits by giving shoppers what they want, where and when they want it.
However, leveraging predictive analytics in retail or any other industry requires the diligence of the right team. Want to save yourself at least the headache of searching for ML experts? Unicsoft is home to an extensive talent pool. Big data and ML development services are our strengths, and we know how to tame your data, so it serves the desired function in predictive analytics.
Here’s your first actionable prediction: Your predictive analytics project will take shape soon after you connect with Unicsoft.
