Integrating Retail Analytics: Top Tips for Businesses

From employee management to forecasting sales trends to improving customer service strategies, retailers need the right data analytics solution to keep their businesses optimized. This is where retail analytics comes in.

A retail analytics solution is there to help you apply intelligent models to your retail business for increased efficiency, customer satisfaction, as well as profits. This post will go over everything from what retail data analytics is, the 4 types of analytics, some examples and practical applications of retail analytics, as well as looking at what retail analytics solution is needed for your business. Let's go!

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What is Retail Analytics?

Simply put, retail analytics is the task of gathering and examining all available retail data in order to optimize for better business practices. The types of data analyzed can be anything from customer behavior (things like browsing, time spent in the store), movement in the checkout line, inventory, price trends, and more. Furthermore, it can provide retailers big and small with an in-depth analysis of the key market trends that they need to be aware of in order to stay competitive.

Retail data analytics is particularly important in the context of brick and mortar businesses. If you are a brick and mortar store operator, you're inevitably encountering many disadvantages when it comes to data collection compared to online retailers. As we already know, the data of digital consumers is collected through every click, mouse movement, time spent on the webpage, any links clicked – the list goes on. And while online retailers can easily get access to that data in order to improve their business models, in-person retailers have had to rely on guesswork and creative strategizing to stay optimized. How can you maintain a competitive edge? Through a retail analytics solution software for your business.

Implementing a computer vision system to actively and effectively track this retail data analytics finally puts those cameras that you have installed in your store to better use. Through the use of a software solution integrated with your in-store cameras, your business can get the data that it needs to recognize everything from customer behavior, trends, pricing, inventory, and more, all of which will provide invaluable insights to help you improve and evolve your business.

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Four Types of Retail Analytics

Before going into more detail about the specific needs of brick and mortar businesses when it comes to implementing an AI analytics solution, here's an overview of the 4 main types of retail analytics.

Descriptive Analytics: This is the simplest type of retail analytics, where the system uses the data it receives to recount what is happening in the store. You need to start with "what's happening" before diving deeper into finding any patterns in the business.

Diagnostic Analytics: The second level of retail analytics takes the "what's happening" and then applies a combination of statistical analysis, machine learning, and algorithms in order to detect meaningful connections between that data and identify any potential irregularities.

Predictive Analytics: This type of retail analytics takes things further to project any future trends by identifying and analyzing the groups and inconsistencies in the data provided. This is useful for tasks like forecasting how much stock to order, or how many staff will be needed on an upcoming holiday.

Prescriptive Analytics: This is the most complex type of retail data analytics. Prescriptive analytics takes all of the what's happening, why it's happening, as well as the predictions of what will happen next from the first three levels of data analytics and provides you with recommendations on what to do next in any scenario to generate the best possible outcome.

The raw data for these kinds of analytics can come from many sources. Sources like point of sale (POS) systems and inventory management systems are important data for understanding the products being sold. But they can tell you very little about customers' browsing habits, and everything interesting that customers do which is not actually buying something. For that, you need physical sensors in the store. Luckily many stores already have fantastic sensors already installed: cameras.  But these are generally not used for analytics, because converting the raw video streams into actionable data requires advanced AI. This is why integrating an AI-based computer vision system can help your business.

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Understanding Customer Behavior

A key piece of retail analytics is knowing, 1-how many people are in your store at any given time, 2-what part of the store they are in, and 3-how long they are there. And this requires analyzing the data from your customers rather than employees, whose movements and activity on the sales floor aren't relevant to collecting the appropriate market data. This leads to one of the biggest obstacles that retailers face: not being able to differentiate between customer and employee on the sales floor.

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Most computer vision-based retail analytics solutions can count how many people are in your store, where they are, and how long they spend in each area. But being able to reliably identify employees is not possible for off-the-shelf CV solutions. Identifying your store's uniform requires a training customized model, which often requires an expensive bespoke solution.  Groundlight's human-assisted computer vision will automatically build a custom model for every situation, and can quickly distinguish customers from employees in any environment.

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Advantages of Retail Analytics

Think of every occurrence that happens on your sales floor on a typical business day. Instances like when a customer is wandering around for minutes at a time in a high-value section of the store and is not offered any service, or when a customer is standing at the service desk and not getting service from an employee. Can management, or other employees, detect these types of missteps 24/7? Of course not.

Retail analytics is there to analyze all the moving parts throughout the day to help you keep on top of managing your business. Identifying and familiarizing yourself with the behavior of your customers is key, and retail analytics software solutions help you do that.

Traditionally, cameras are installed in a retail store solely for security purposes. And typically, the camera recordings are only looked at after an instance for a crime. All of that other data being recorded on the camera is essentially wasted, but retail analytics can put it to better use. Think of it as a more intelligent vision system, where you can accurately analyze and integrate that customer data to improve your business practices and maximize your profit margins.

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Examples and Uses of Retail Analytics in Your Business

Here's some examples of how retail analytics can be used and what it can help with when it comes to the success of your business.

1. Identify key market trends

The right retail analytics solution can accurately identify trends in your customers behavior, including the busier and busiest times of the day as well as the days of the week with higher customer traffic. Taking the data from these key trends, you can then use the information to plan targeted marketing campaigns: think promotions to help boost sales and increase your customer engagement.

2. Improve store layout

A retail analytics system will help you better understand and analyze things like how often and when your customers use the service counter. This will provide you with insights into the effectiveness of your store's layout. 

Retailers can use this information to make data-driven decisions about rearranging the store's layout to encourage customers to visit the service counter or explore other parts of the store.

3. Increase customer satisfaction

Get your queue management optimized using retail data analytics and enjoy increased customer satisfaction. No one likes waiting in a long line, so by monitoring the usage of the service counter and proactively addressing long wait times or crowded areas in the line retailers can improve customer satisfaction and loyalty. A positive customer experience can lead to increased sales and return visits.

4. Optimize your staff management system 

By analyzing the rate of use of your service counter, store managers can optimize staff allocation and scheduling, ensuring that there are enough employees serving customers during peak hours. Not only will this reduce wait times for your customers, you won't be over-working your staff during the most hectic times of the work day.

This list was inspired by one of Groundlight’s retail analytics solutions, which monitors the usage of a service counter by customers throughout the day. The application creates a detector to identify when the service desk is being utilized by a customer. It checks the detector every minute and once every hour, it prints out a summary of the percentage of time that the service counter is in use. At the end of the day, it emails the daily log.

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A Retail Analytics Software Solution

When planning to integrate retail analytics to your business, you have to think of the particular needs of your store. Every store is different, as there's distinct lighting and camera placement that might require a huge consulting or setup fee in order to help with the installation process.

Groundlight AI provides computer vision technology that can be customized to the unique needs of your retail business. It has the ability to be tailored to each of your stores so you don't have to integrate each system individually.

If you're a developer and looking to build this solution yourself, visit our documentation and learn how you can get started building a retail analytics solution.