Whatever you do in today’s digital world, you leave a digital footprint and generate vast amounts of data. Whether you withdraw money from the bank, shop online for your favorite shoes, or get groceries, technology has changed the way we live our everyday lives. Ever wonder how you always get recommended just the right product for you? That’s retail data analytics working for you! 

As much as retail data analytics helps the customer, it also helps retailers provide tailored offers, creating a “win-win” situation. This isn’t something new and groundbreaking, but a necessity for every modern retail company that wants to remain relevant in a highly competitive market. 

So, let’s see how they do it and what goes on behind our screens.

What is Retail Data Analytics and What Does It Do?

Retailers use data analytics to collect and analyze data from multiple physical and online sources with the goal of acquiring insights into customer behavior and shopping trends.

They can then use this data to improve their decision-making processes and various other aspects, including pricing, inventory, marketing, merchandising, and store operations. 

More specifically, with retail analytics, retailers can:

• Create more effective sales and marketing strategies that match their needs
• Optimize their operations by learning which products/services are the best performers
• Improve resource allocation by adjusting their investment strategy according to the specific performance of each product/service
• Reduce costs by eliminating low-performing products/services
• Increase sales with the help of customer behavior data

The Four Types of Retail Data Analytics

There are four main types of data analytics used in retail, which are as follows:

• Descriptive analytics – This type is the foundation of retail data analytics. It answers questions such as how much a customer spends on average on products or how many units of a specific product have been sold in a particular period. The data gathered from descriptive analytics is essential for creating dashboards and reports, enabling retailers to review and monitor their business performance.

• Diagnostic analytics – This type delves deeper and focuses more on why something happens. More specifically, diagnostic analytics is employed to identify the underlying causes of problems and uncover new opportunities. For example, retailers can use diagnostics to discover what is causing the sales of a particular product to drop, why customers are abandoning their shopping carts, etc. These insights will, in turn, enable them to take the appropriate actions to fix the issues and optimize their operations.

• Predictive analytics – This type of analytics uses data to forecast trends, allowing retailers to make more informed decisions regarding pricing, marketing strategies, product development, etc. For instance, with predictive analytics, they can anticipate peak demand for a seasonal product or forecast customer buying patterns, allowing them to plan and organize their workload effectively.

• Prescriptive analytics – This is a more advanced analytics type that leverages data to provide actionable suggestions tailored to each retail business. It can recommend optimal product inventory levels, suggest new store locations, help allocate marketing resources and much more. Prescriptive analytics can help retailers optimize every aspect of their business and improve its performance.

What Kind of Data Does Retail Analytics Collect?

Retailers use data analytics to collect many types of data that will help them gauge what is going on in their business. Here are some examples of the types of data that are gathered:

• Transaction data contains details regarding each purchase that was made in a business. It reveals information such as what product was purchased, when it was purchased, how many units were bought, and how much it cost. This type of data gives retailers a clear overview of buying trends among their customers.

• Customer data allows retailers to understand their customers better, which is vital for the success of their business. They collect information like age, location, and purchase history to create a personalized shopping experience for each customer and improve their marketing strategies.

• Product data refers to product names, prices, descriptions, and stock levels. This data can help retailers create more efficient pricing strategies, improve inventory management, and optimize product selection.

• Online behavior data is vital in today’s digital age, where over 33% of the world’s population shops online. Retailers track website and app usage data such as page views, clicks, and abandoned shopping carts to learn how customers interact with their products/services and improve the online shopping experience on their sites and apps.

• Customer feedback, including surveys, product/service reviews, social media comments, and forum posts, provides invaluable insights about customer satisfaction and preferences. Retailers can use this data to improve their offerings and entice customers to return.

How Data Analytics Helps Retailers Succeed 

Now that we know more about the topic let’s see which aspects of a retail business can be improved by implementing retail data analytics.

Personalized Marketing Approach

Due to the fierce competition in the online shopping sphere, even the most prominent retailers have to continuously improve their shopping experience and make it as personalized as possible. Retail data analytics helps with that because it allows retailers to analyze customer data and extract valuable insights from it, which they can use to target specific customers with relevant offers. 

Additionally, retail analytics can help retailers identify high-value customers, enabling them to create tailored marketing campaigns to increase customer retention, offer appropriate rewards or discounts, and ensure that they continue to choose their products and services.

Optimized Pricing Strategies

Finding the perfect price point is essential for each retailer, no matter what kind of products or services they sell. That is why retailers use the power of data analytics to analyze customer behavior, purchasing psychology, and sales data to arrive at the most optimal price for each product/service. 

This takes all the guesswork out of the equation, allowing them to maximize their profits without chasing potential customers away due to high or disproportionate prices.

Efficient Inventory Management

No more product shortages or excess inventory! Retail data analytics can help retailers improve the efficiency of their supply chain and optimize the entire process.

Namely, it allows them to identify customer buying patterns, forecast trends, and monitor metrics like shipping times and supplier availability. These insights help retailers streamline their operations, paving a smooth pathway for their products to reach the customer.

Tailor-Made Product Recommendations

Recommendation systems analyze customer purchase history and use algorithms to recommend products/services that complement the customer’s previous purchases. 

For instance, if a customer buys a pair of pants, the recommendation system might suggest purchasing a tie, belt, or shoes that match the pants and complete the look, which is a great way to increase customer satisfaction and sales.

Fraud Detection

Fraudulent transactions can cause many problems for retail businesses and affect their reputation. However, detecting fraud can be quite taxing, especially for companies that process a high volume of transactions. 

That is why retailers use data analytics, which allows them to analyze data accurately and quickly identify suspicious transactions and activity, saving them a lot of time and preventing potential financial losses.

Accurate Sales Forecasting

Data analytics allows retailers to evaluate historical sales data and identify trends and customer patterns, giving them an insightful overview of their business performance and the popularity of each product/service they offer. 

This way, they can predict future sales and peak demand periods more accurately, plan and manage their inventory better, avoid missing sales opportunities, and reduce overhead costs.

How to Implement Retail Data Analytics 

Step 1: Set objectives and scope

First, retailers need to determine what they want to achieve and what they want to focus on. They need to decide where they want to start, for example, sales or customer feedback, and expand the scope of their data analytics initiative from there.

Step 2: Assess the data infrastructure

Then, retailers need to evaluate their current data collection and storage systems to identify their strengths and weaknesses. They also need to find out if and where they have gaps in their infrastructure and check whether there are valuable data types that are not being collected.

Step 3: Data collection and integration

Retailers need to track down the different data sources they need to achieve their goals, such as transaction data, website and app data, supply chain data, customer feedback, etc. Also, they need to create a strategy that will integrate these different data sources. It’s important to note that this might require data warehousing techniques or a cloud-based solution.

Step 4: Choose the right tools

Retailers need to decide whether they want to use cloud-based services and find the right data analytics tools that suit their needs and infrastructure. Before choosing the tools, they need to make sure they are scalable and offer the features they need.

Step 5: Get the right people

No matter which tool is used, retailers will need someone who knows how to use it. Therefore, they will need experienced data scientists, analysts, and IT professionals to ensure the collection, analysis, and interpretation of data correctly. Also, they need to train the existing staff to perform data analysis and other relevant tasks independently whenever needed.

Step 6: Gradual implementation

To ensure the implementation of retail data analytics goes smoothly and to understand its benefits and practical challenges, retailers need to start with a demo program that’s focused on a specific aspect of their business. Then, they should gradually expand the implementation to all relevant business areas.

Step 7: Data analysis and insight generation

By conducting continuous data analysis, retailers are able to find patterns and trends among their customers. This way, they can modify and optimize their business and achieve their goals based on the generated insights.

Step 8: Review and refine

Retailers need to monitor and compare the results of their data analytics to their goals so that they can see if the strategies need adjusting. They also need to stay updated on the latest retail trends and refine their strategies and goals accordingly.

One-Stop Shop

Of course, there is an alternative route retailers can take if they do not have the time or infrastructure to perform steps 1-8 on their own. This includes finding someone who will help them every step of the way and work with them to create a tailor-made retail solution! 

What Will the Future of Retail Look Like

The retail industry is constantly changing, with trends coming and going in the blink of an eye. Therefore, each retailer needs to know the future trends in the field so that they can be one step ahead of competitors. Here are some of them:

Using Omnichannel Tools for Increased Reach 

Customer experience research shows that over 80% of shoppers research online before deciding which product to buy. So, for a customer to even consider a retail business, it needs to be present across multiple channels.

By having an omnichannel presence, retailers increase the chances of appearing when customers search for the products and services they sell. 

Task Automation

In recent times, employee retention in the retail industry has become an issue. Therefore, to overcome this challenge, retailers are turning to task automation to reduce their employees’ workloads and enable them to focus on more important tasks.

Task automation is most popular among retail businesses in areas such as order tracking, maintaining customer loyalty programs, and some customer service forms.

Voice Search

The practicality of digital assistants like Alexa, Siri, and Google Assistant has given rise to the use of voice searches among many customers as a way to make their online shopping experience simple and easy.

Therefore, many retailers are optimizing their online presence not just with SEO for their sites but also by using natural speech patterns and user intent in their keywords so that when customers use voice search to find the product/service they sell, their business is among those that pop up.

AR and VR Experiences

As technology becomes more and more sophisticated, customer expectations regarding their experience with a brand have also evolved.

Namely, retailers use extended reality, which consists of Augmented Reality (AR) and Virtual Reality (VR), to create a unique experience that will convert a potential customer into a returning one.

For example, AR and VR in retail can create 360° immersive views of products so that customers can see what the product would look like in real life.

Machine Learning

ML (Machine Learning) is a subset of AI (Artificial Intelligence) that enables computer systems to automatically learn and improve based on previous experience, without human input or additional programming.

In retail, some of the most common applications of ML are for recommendation systems, conversation bots, and visual searches.

A Bright and Empowering Retail Future

Due to the ever-increasing number of online shoppers and the massive amount of data they produce, data analytics has become necessary for retailers of all sizes. 

By using the power of retail data analytics, retailers have been able to unlock a trove of benefits, transforming their businesses into top performers in a short time period. Additionally, by extracting valuable insights from their data and transforming them into actionable strategies, retail businesses gain a competitive edge that can make them stand out in the massive crowd of online retailers!

If you, too, are a retailer who wants to tap into the power of data and leverage its full potential, contact our experts at Data Masters today and let them help you discover the hidden gems within your data.