Business Intelligence in retail industry. Applications & Tips

Retail businesses face some form of competition from similar establishments. According to research, there are approximately 3.8 million retail establishments in the U.S alone [1], which clearly indicates that you need to develop a robust retail marketing strategy to stay ahead of the competition.

While this may seem like a time-consuming and resource-intensive endeavor, you only need two things to keep your retail business competitive- better business insights and sound decision-making. And this is where business intelligence (BI) and the retail sector intertwine.

Given that the global market for BI is expected to reach $22.8 billion by the end of the year, [2] it’s not a matter of whether or not the retail industry needs to leverage business intelligence. But how do retail stores intend to do it?

Read on to find out how business intelligence is helpful in the retail industry.

Business Intelligence in Retail: Understanding the Fundamentals

Retail business models undergo digital transformation every now and then. Today, retail businesses have access to vast volumes of data, thanks to the surge in popularity of online shopping, social media, and mobile apps. These datasets are about products, customers, sales, suppliers, employees, marketing, transactions, and more. Yet while collecting business data is essential, the question of what you do with it is more pertinent.

Business Intelligence (BI) refers to the use of software and technology to gather, analyze, and transform data into useful and actionable insights that can help businesses make informed decisions. The process involves collecting and integrating data from various sources, such as internal systems, third-party data providers, and public data sources, to generate reports, dashboards, and visualizations that provide a comprehensive view of business performance and trends. The goal of BI is to improve decision-making and drive business growth and profitability by providing executives, managers, and other stakeholders with the information they need to make data-driven decisions.

Business intelligence leverages a range of tools to analyze available data and deliver quick, actionable insights about the current state of an organization or business. For example, how many customers have we gained or lost this month? What are the sales numbers of a recently launched product?

BI does not tell retail store owners what to do or the consequences of making a particular decision. Instead, it is descriptive- it informs retail managers what is happening now and what occurred in the past leading to the current state. Ultimately, it focuses on answering the question: what can we do to make prudent business decisions based on historical and current data?

Key Applications and Case Studies for Business Intelligence in Retail

You don’t have to be a company the size of Wal-Mart to gather, store, and examine data with the aim of deriving meaningful business insights. All you need is a solid integrated software system to transform the vast volumes of varied retail data into insights.

Here’s how you can apply business intelligence in retail industry.

Create a personalized shopping experience

It’s a no-brainer that better customer engagement translates to increased sales. The tricky question is, how can you create that wonderful shopping experience? Well, the answer lies in understanding your customer at a deeper level.

You can improve your cross-selling and up-selling prospects by tracking customers’ browsing habits and buying decisions. This lets you in on:

• Their preferred products
• Whether they like online or in-store shopping
• Their expectations of a given product
• Their preferred brands

By understanding the needs and wants of your customers, you can create a personalized customer journey. This is achieved by tailoring special offers based on what the customer is searching for.

According to Forbes, 44% of shoppers agree that they would repeat a purchase subject to a customized purchasing experience. [3] Macy’s, a top omnichannel retailer, is an excellent example of the proper of personalization toward customer loyalty.

The retailer collects and analyzes customers’ shopping habits, including their buying frequency, tastes, and preferences. They then incentivize their customers with special offers and personalized loyalty programs. The retailer projects its digital sales to hit an incredible $10B by 2023. [4]

Effective marketing campaigns

The retail industry can harness the sea of data collected through hundreds of transactions to craft effective marketing campaigns.
Retail business intelligence can help retailers ask relevant questions, as expressed below, and identify the most relevant key performance indicators (KIPs).

• What are the best-selling products?
• What are the demographics of the consumers?
• Which retail outlets are selling the most of a specific product?

Retail brands that capitalize on business intelligence have more confidence in their marketing campaigns. This is because they better understand their customer base and can adapt marketing messages at the granular level.

By gathering and examining data from web browsers, social media, email campaigns, user forums, and other web sources, business intelligence helps retailers predict buying trends and combine them with an individual customer’s persona to step up marketing campaign activities.

Streamline supply chains

The retail supply chain is a web of complexity involving different players, from production to shipping products into warehouses. This makes it prone to inefficiencies and underperformance from a few involved parties or departments.

Fortunately, business intelligence tools can help you gain better insights from the data generated from your daily retail operations. This allows you to identify the logistical bottlenecks that need immediate intervention to streamline supply chain operations.

McKinsey & Company reveals that organizations that leverage retail business intelligence to streamline their supply chain operations can cut their supply chain expenses by 10%. [5]

Better inventory management

Retailers dread overstocking and understocking in equal measure. When you overstock, you tie up capital and depend on selling fast to achieve cash flow. On the other hand, understocking means you lose out on sales, especially if the product is in demand.

Smart business intelligence tools provide a centralized database of all inventory and customer data generated from multiple sources, such as point of sale systems, ERPs, warehouse management systems, and more.

With access to real-time inventory data, retailers can achieve better inventory management by:

• Identifying demand patterns and increasing supply to reduce the threat of understocking.
• Knowing which stores to increase inventory allocation.
• Establishing how to get rid of low-selling products.
• Creating correct inventory projections based on available stock and sales reports.

Optimize prices

Customers today know where to get value for their money. They compare prices online while shopping for a given product and are loyal to retailers offering competitive prices and discounts. Thus, a store’s price optimization strategy is crucial to a profitable bottom line.

Sadly, some retailers have stuck to outdated pricing strategies. They use past trends or even hunches to set a price for their products. But things don’t have to be this way. You can use BI to set the right price point that will increase your revenue but also provide value for money for your customers.

Retail business intelligence considers various factors to set a price point for a product. The factors at play and the data analyzed may include:

• Seasonality
• Market pricing
• Competitor prices
• Customer buying patterns

An optimized price may mean a lower price than the initial price or a higher price. The frequency of price adjustments isn’t cast in stone. It’s an ongoing process, and retailers must make data-driven pricing decisions.

Customer segmentation

Customer segmentation is a powerful application of business intelligence in retail industry that involves dividing customers into groups based on their unique characteristics, behavior, and purchasing patterns. By analyzing customer data, retailers can create personalized marketing campaigns and targeted promotions that resonate with each segment of their customer base.

Here are some ways that customer segmentation can benefit retailers:

1. Targeted marketing: Retailers can use customer segmentation to create personalized marketing messages for each segment of their customer base. For example, a retailer might send an email campaign promoting outdoor gear to customers who have previously purchased camping or hiking products.
2. Product recommendations: By analyzing customer purchase history, retailers can provide personalized product recommendations to customers. This not only improves the customer experience but can also increase sales.
3. Customer retention: By understanding customer behavior and preferences, retailers can identify customers who are at risk of leaving and create targeted retention campaigns to keep them engaged.
4. New product development: Customer segmentation can help retailers identify gaps in their product offerings and develop new products that meet the unique needs of each customer segment.

Overall, customer segmentation is a powerful application of business intelligence in retail industry that can help retailers create personalized shopping experiences and build long-term relationships with their customers.

Tips for retail business intelligence success

The business intelligence market is not short of vendors and tools to choose from. Retailers looking for a business intelligence solution should first carry out an in-depth evaluation of their requirements. Here are some of the tips you can use for retail BI success:

1. Create clear objectives for your BI use case.
2. Pick a solution geared explicitly for the retail environment.
3. Adopt a meticulous change management plan, and strategies to achieve buy-in from all the parties involved.
4. Hire the right personnel to execute the solution. Otherwise, seek the help of private IT experts who have experience in actualizing such projects.
5. Choose a vendor who promises strategic and technical support during the entire lifespan of your BI solution.
6. Create post-adoption guidelines for data-quality maintenance and business intelligence governance.

Business Intelligence in retail industry

Big data is not helpful unless you can draw insights from it to your advantage. Proper business intelligence provides a quick, practical way for retailers to analyze data and draw insights that address the fundamental problems affecting today’s retail industry.

BI tools are vital to optimizing retail operations, like pricing structure, inventory management, and supply chain operations. They also impact customer-facing initiatives, like merchandising, store appearance, employee performance, marketing campaigns, and a memorable customer shopping experience.

The retail industry considers the ability to uncover customer patterns and create a personalized customer experience as the golden ticket to effective business intelligence.

To make sure the adoption of business intelligence in retail industry is successful, we recommend that retailers should focus on setting clear objectives, choosing the right solution, adopting a change management plan, hiring the right personnel or seeking outside help, choosing a supportive vendor, and creating post-adoption guidelines. For more tips make sure to contact our experts!

References

[1] Nrf.com. Retails –Impact. URL: https://nrf.com/retails-impact. Accessed July 17, 2022
[2] Selecthub.com. BI Software Market Growing. URL: https://www.selecthub.com/business-intelligence/business-intelligence-software-market-growing/. Accessed July 17, 2022
[3] Forbes.com. 50 Stats Showing the Power of Personalization. URL: https://bit.ly/3Sg0di2. Accessed July 17, 2020
[4] Rinews.com. How Macys Will Reach 10b Digital Sales. URL: https://risnews.com/how-macys-will-reach-10b-digital-sales. Accessed July 17, 2022
[5] Mckinsey.com. How Advanced Analytics Can Address Agricultural Supply Chain Shocks. URL:  https://mck.co/3J6DQHH. Accessed July 20, 2022