Predictive Analytics in Retial: Benefits and Use Case

Predictive Analytics In Retail: How It Works? (5 Big Benefits + Use Case)

The retail industry has undergone significant changes in the past decade, mainly due to the pandemic, which disrupted normal operations. Retailers were forced to adopt a digital approach (An approach that has become a “must” for the industry nowadays).

Demanding consumers and constant technological advancements have brought significant changes, whether in e-commerce or brick-and-mortar stores. 

In this article we will cover the following topics:

The Role of Predictive Analytics for Retailers

Retail predictive analytics allows retailers to combine data elements (Like internal, external, and alternative data) to estimate possible future outcomes.

So, using the right datasets, predictive models, and out-of-the-box thinking, brands can get high-value predictions regarding user personalization, consumer loyalty, in-store improvements, and demand forecasting. 

Right now, retail brands that are using predictive analytics are getting better insights regarding:

  • Pricing strategies.
  • Transactional and brand loyalty behavior.
  • Customer segmentation.
  • In-store navigation.
  • Competitive intelligence.
  • Sales intelligence regarding detecting and forecasting new trends, patterns, and seasonability.

Learn more: Understanding the role of Big Data in Retail Industry (With Examples)

Understanding the different types of Retail Predictive Analytics

There are different types or categories of retail analytics. Each one is useful to approach different data needs and questions. Oracle classifies retail analytics into six different categories:

Shopper-Level Analytics

This type of analytics is about getting to know customers. What they buy, how they shop, and how they feel about a specific brand. 

Transaction-Level Analytics

This focuses on individual purchases. When and how did a customer buy a product? This can help you measure the success of a promotion or a marketing campaign.

On-Shelf Analytics

This is all about your products. Which ones sell fast, which ones don’t, and what are your competitors offering? This helps you optimize your product range.

Use of heat maps to analyze in-store performance
Use of heat maps to analyze in-store performance

Location Analytics

This helps you understand how your stores perform across different locations or regions. It tells you about local preferences, helping you tailor your offerings to suit local tastes.

POI as a example for location analytics
Alternative data like POIs and Foot Traffic to compare performance against a competitor

Learn more: How Does Location Intelligence Work & Why Do You Need It?

Multichannel Analytics

This type analyzes customer behavior across sales channels like your website, app, and physical stores. It shows you which products sell better on which channels.

Outcome-Level Analytics

These analytics are about general results. How are sales, profits, and customer loyalty changing over time? This helps you identify trends, for example.

What are the benefits of predictive analytics?

The good thing about predictive analytic models is their capacity to adapt to specific business needs and objectives. Nevertheless, there are some expected benefits that most retail companies can get from predictive data:

Improved customer experience

Retail is one of the main industries where consumers expect the best customer experience. The good news is that retailers can gather customer data from different sources like online channels, website analytics, social media, sensors, transaction data, foot traffic, and POIs

With all this information, predictive models can improve every touch point between the brand and their users, from personalized promotions, store layout, product delivery times, pricing strategies, and cross-selling activations to post-sale efforts. 

Home Depot is employing predictive analytics, specifically prescriptive analytics, to battle shrinkage (loss of inventory due to waste, fraud, abuse, training, execution, or other reasons). 

Home Depot, in particular, faces a challenge given its large scale, with over 2,000 retail stores and significant sales volume. Issues with shrinkage impact not only the company’s bottom line but also the customer experience, as missing SKUs due to shrinkage can lead to customers not finding what they need on shelves.​

Overall, predictive analytics can help answer the question: How can I provide the best customer experience so my users become loyal to my brand?

Have you ever considered how much Forever21 or H&M would lose in revenue if they ran out of stock due to unexpected demand? Predictive analytics takes past and current user data to identify market shifts regarding sales, customer journeys, or product preferences for deeper market analysis

With this, brands can identify upcoming trends and patterns, take the necessary actions, and develop robust strategies to take advantage of new business opportunities and avoid market threats. 

Big Data Retail case study

Zara constantly uses data analytics to spot new trends within its target market. The brand uses track tags to collect data regarding:

  • How frequently a piece of clothing is tried on and returned to the rack?
  •  How many items make it to the checkout counter? 
  • How quickly do they go from the shelf to the Point of Sale (POS)?
  • Sales levels of each SKU from the inventory levels in each store.

In-depth product development and launch 

A product launch can be expensive and time-consuming. Predictive analytics are great for reducing risks by evaluating and forecasting a new product demand in a specific market or customer segment. 

Retail predictive insights can even guide you into how much product you can expect to sell in a certain period. That is key to managing your production, supply chain planning, and stock levels. 

Big Data for retail market to identify new market opportunities

Also, brands can make important decisions about the placement, promotion, and timing of a new product launch strategy by analyzing planogram and assortment data, point-of-sale transaction details, and customer loyalty intelligence.

Learn more: The Best Way to Do Market Research for a New Product Development

Better marketing campaigns 

Different consumer segments exhibit distinct behaviors and patterns. Seasonal factors may persuade some, while prevailing shopping trends may impact others.

Predictive models can collect past and current data and combine it with relevant external and alternative data to estimate how consumer segments can behave based on specific scenarios. 

This is a goldmine for marketing teams because they can develop specific, highly-targeted, personalized campaigns with a higher probability of success. 

Learn more: How to Get Useful Customer Behavior Analytics? (Guide)

Valuable insights for expansion strategies

To develop successful expansion strategies, retailers need to have the necessary insight. How to determine if the brand will have the expected results in a new geographic territory? How to decide where is the location for a new store? How to choose uncovered marketplaces to place a product?

Predictive models consider past and current results and add external market factors to estimate ROI outcomes. 

Predictive analysis for a new retail store
Predictive Analysis for a new retail store

For example, suppose a brand decides to place a new store in San Francisco. In that case, the model can analyze other stores’ data, like visitor volume, sales transactions, product demand, and market share, to understand which factors determine current stores’ success and failures. 

Then it will combine these insights with specific city factors like demographics, competitors, and purchasing power. 

We recommend you reading our case study: How a major brand managed to find new optimal sales points

Case Study: How Walmart Uses Predictive Analytics?

Big Data Analytics has become essential to Walmart’s strategy to enhance, customize and optimize its shopping experience. These are four keys to how Walmart uses data and predictive models: 

Improve store checkout: Walmart’s stores have improved their checkout process by using predictive analytics. This allows them to anticipate the busiest hours and determine how many associates are needed at the counters. 

Also, by analyzing data, Walmart can decide on the best checkout options for each store, such as self-checkout.

Keep better track of its supply chain: Walmart utilizes predictive estimations to monitor their supply chain and track the “number of steps” from the dock to its stores. This enables the company to optimize routes, analyze transportation lanes, and determine the most efficient routes for its fleet of trucks.

Learn more: How To Use Predictive Analytics For Supply Chain Optimization?

Optimize product assortment: The brand analyzes customer preferences and shopping patterns to make better decisions about what products to stock and display. This helps optimize the product assortment in stores.

Personalize shopping experience: Walmart uses predictive insights to make sure product is always available. For example, the company gathers data on users shopping for baby products to forecast future demand for diapers. 

Start using retail predictive analytics

At PREDIK Data-Driven, we have over 14 years of experience developing customized predictive models for the most well-known retail brands worldwide.

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