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The global retail analytics industry is expected to be worth $18.33 billion in 2028, growing at a 17.7 percent CAGR between 2021 and 2028. This is evident that chain managers are racing to adopt new technologies to aid their business. Analytics for retail can be helpful in all sales channels, from in-store to online selling or hybrid. Using data to manage a profitable retail business takes the uncertainty when determining where to locate your next store or prioritizing inventory restocks.  

So what is retail analytics? What are some of its types and practical use cases? Let’s find out in this article. 

What Is Retail Analytics?

The definition of retail analytics is just simple – gathering data about your shop, pulling insights from it, and applying them to fine-tune your strategy.   

Retail data includes data from all aspects of your retail chain (sales, marketing, planning, pricing) to eliminate guesswork. You can collect data from surveillance systems or forms filled out by customers, such as shopper reward programs.  

Retail Analytics can tell how well marketing works, what customers do when they enter a shop, customer experiences and what products pull the most sale volume. As a result, businesses may make wise decisions depending on how well their merchants perform and how their customers behave.

They also good at revealing hidden patterns. This offer businesses opportunities to gain competitive advantage on the market at certain period. While at the same time, it give early alert on risks and upcoming challenges.

Benefits Of Retail Analytics Adoption

 A survey conducted by data science firm Alteryx and the Retail Wire forum has shown that roughly a quarter of the respondents know very little about the benefits of retail analytics, while only more than 10 percent responded to being experts. 

1. Improve customer experience

Enhancing the experience is the goal when assessing retail analytics benefits. A company’s strategy to improve the shopping experience can significantly benefit client feedback. Improving operations to minimize wait times and enhance processes would almost always increase visits to a particular site.   

Improve customer experience

One such improvement can be minimizing waiting queues. Customers are frustrated if they must stay in a long checkout lane. Waiting in the shop queues results in losing a prospective sale and damaging the company’s image. It causes the store to revert to the consumers’ following shopping selections. Waiting times and checkout densities are tracked using retail analytics, and staff management is immediately notified in the event of a crowd intensity.   

Most significantly, retail analytics assists firms in better understanding their customers. After all, customer experience is vital, and businesses can understand consumers’ desires and requirements by analyzing data. And then, they might make the buyer’s experience as smooth as possible and increase customer loyalty.   

2. Better in-store and inventory management

When it comes to inventory management, integrating data analytics in your retail chains allows you to actively keep an eye on inventory management statistics, including stock availability, inventory turnover ratio, top-selling products, and other valuable metrics. Monitoring data informs you about market changes and which goods are in more demand; therefore, it would provide insights into product demand and allow you to stock up on the things that are most required. Furthermore, controlling inventories depending on market demand gives you a competitive advantage.   

Retail analytics allows you to keep track of other metrics like total visitor count and average visits, which are essential when managing in-store operations. Using these analytics, you may learn about the most frequent customer visits each day and hire more workers to assist waiting customers at the checkout. Furthermore, you may utilize these analytics to improve beautifying shop decisions and build better marketing strategies to help you attract more people, which leads to increased income.   

retail analytics Better instore and inventory management

Employee numbers and staff qualifications are critical factors in achieving customer happiness in retailers. Retail analytics can detect active regions and rush hours, allowing you to rearrange your workers accordingly and more effectively. As a result, your customers will always have a positive experience in your store. Furthermore, competent personnel may provide accurate product information to customers and help them make better shopping decisions. It enhances the likelihood of a sale. Retail analytics may measure where people spend their time, how many consumers they engage with, how long the encounter lasts, and how many of them buy a product. 

How can In-store Analytics benefits to Retailers to accelerate sales

3. Increase overall revenue and marketing

Of course, when mentioning the benefits of retail analytics, you would arguably consider the profit factor. Retail analytics helps you enhance your business’s success by measuring essential indicators like sales per square foot or average unit retail. Using the aforementioned data, you may learn about your industry’s actual performance and design plans, such as growing stores in ideal locations, delivering competitive offers to customers, making timely and speedier deliveries, and more. This will assist you in improving performance and, as a result, increasing your company’s earnings.  

Using data from retail analytics, you may analyze your promotions’ impact and improve your marketing approach’s efficiency. You may also assess the efficiency of your shop’s advertising and action areas. You can determine which of your displays is more appealing to customers. Furthermore, marketing methods may be adjusted by assessing consumer quantity and time spent in the department. You can also make in-store comparisons with the options given by retail analytics. You may discover the most efficient area and adjust your marketing plan based on the comparisons you make inside the shop and across retail sections. 

What Are The Types Of Retail Analytics?

So now you know the definition as well as the benefits of retail analytics. Let’s look at its type and the following retail analytics use cases. 

1. Descriptive analytics

Descriptive analytics provides retail chains with a summary of the performance of most business operations – for example, transactional history, inventory changes, promotional success, and so on. These types of retail analytics are not uncommon. Retailers have employed descriptive analytics to assess direct mail initiatives’ response rates, cost per lead, and conversion rates. However, with the advent of Big Data, descriptive analytics has taken on a new shape. Retailers can utilize website tracking data to establish how many visitors visited a site, which pages they viewed, how much time they spent on each page, which links they clicked, which links led to purchases, and so forth.  

Today, descriptive analytics, the most common kind of analytics, is used by 90% of enterprises. Descriptive analytics enables you to track which of your implementations performs better and creates more income and which performs poorly and drives your business down. This would require aggregating and mining data. In practical applications, descriptive analytics can vary from gaining insights into behavioral patterns to understanding overall product demand. Both analyze demand within a certain period or customer segment. In business planning, it could determine marketing strategies’ efficacy and help compare essential metrics over time. 

2. Diagnostic analytics

Diagnostic analytics is retail analytics that seeks to find out what has happened. In today’s data-driven environment, this task is challenging for a human to accomplish. Larger shops, with billions of data points and rising complexity, cannot successfully deploy diagnostic analytics without machine learning and AI. Companies may acquire insights into the sources of trends in their data by employing diagnostic analytics with different approaches, such as data drilling and data mining. Firms may need to analyze various data sources, including external data, to understand the fundamental cause of trends. Diagnostic analytics, for example, relies on other data sources, such as open email rates or public holidays, to explain why conversions increased. 

Diagnostic analytics

Descriptive analytics is typically used as the initial phase in data analysis. Diagnostic analytics then investigates the sources of specific patterns, assisting businesses in understanding why they occurred. For example, suppose the most recent sales report reveals a higher-than-average rise in sales. In that case, the firm can delve into internal sales data to determine if the increase was due to specific customers or new items. External data such as weather trends or competitor activity can also be used in diagnostic analytics. 

3. Predictive analytics

Predictive analytics enables businesses to forecast trends and customer behavior based on previous associations identified by diagnostic analytics. Suppose descriptive analytics necessitates the ability to read numbers and charts. In that case, predictive analytics requires in-depth expertise in translating these figures into a response to the question, “What will happen next?”. This retail analytics forecasting trend lines by applying different statistical approaches, like machine learning and data mining, to large amounts of data. Predictions are always uncertain; thus, predictive analytics is no exception. A manager must confirm with their analysts the accuracy of the data, whether it’s representative of their customer base or biases, and what conditions could change the whole pattern.  

The retail analytics use cases using this type of analysis can vary greatly. The entire data set you collected from a period and processed allows you to predict goods that will be in high demand throughout each season, which, in turn, helps determine the optimal market pricing. This also benefits staff management as you can estimate the minimum number of employees required at different times. Ultimately, you can use the analytical result to optimize your operations in each sales process step. 

Also read: 10 uses cases of Predictive Analytics in Retail

4. Prescriptive analytics

Unlike the more commonly adopted diagnostic analysis, prescriptive analytics only accounted for roughly 3 percent of firms utilizing data tools, according to a Gartner report. Although finding a retail data analytics case study that uses this type of analysis is not simple, it is equally important compared to other types. Prescriptive analytics assists businesses in developing a plan based on real-time data. In a sense, it enables the development of existing solutions to current problems predicted by real-time and historical data analysis. Seasonal shifts in staff rotations are one instance. Suppose the last quarter of the year witnesses a significant rise in foot traffic. In that case, these predictive analytics will remind you to add seasonal personnel and alter open-to-buy expenditures to meet increased customer demand.  

Some common use cases of prescriptive analytics might include the warning of possible stock-outs or displaying optimal price points to customers with different incomes. Besides recommending relevant goods to people with identified purchasing behaviors, it can also analyze which products they are likely to consider next. 

Examples Of Retail Analytics

Previously, marketers could only analyze sales and traffic patterns to measure the efficacy of media and promotions in boosting in-store visitation, sales, and brand awareness. However, analytics may provide essential insights into a retail firm in various ways. Multiple components can be utilized in different ways for various circumstances. Below are some of the most popular retail analytics examples: 

1. Tracking Customer Behavior

Tracking customer behavior is essential for getting to know your consumers and building loyal, long-lasting connections. Thanks to the already available CCTV system, this can be one of the first retail analytics examples that any manager will consider. This implies that all of the activities taken by a single user will be scrutinized. Based on their last activity, the platform will supply customers with more tailored product options. This data will include prior products seen, pages visited, and the length of time spent viewing items, among other things. With the overwhelming number of retail chains located on every corner, providing a personalized user experience is more important than ever, as customers will not hesitate to switch over to the competition if they think their needs are better met.  

In terms of physical locations, it would be beneficial if you also recorded consumer activity here. Stores often do this by putting cameras that track how customers move throughout the store. Such data will be invaluable to your retail data analyst since it gives insights into the popularity of specific items and the ease of navigation in your shop, allowing you to make modifications accordingly. If you want a more in-depth study and insight into user behaviors, you may create a unique retail solution that will offer you the precise data you require. 

2. Customer Personalization

This can be seen as a continuation of behavior analysis. Understanding customer behavior and connecting it with consumer demographics is the first step in predictive analytics adoption. Retailers may use it to provide targeted and highly personalized offers to specific customers.  

Before the widespread use of data analytics, the possibility of tailored offers was either non-existent or limited to big groups of clients who shared one or two criteria. However, with the advent of internet shopping and data analytics, tracking a buyer who researches the digital store and then purchases the item in the physical store is now feasible.   

retail analytics customer personalization

The customer data, combined with retail predictive analytics, now enables retailers to deliver highly tailored offers to customers at a granular level. Retailers, for example, may tailor the in-store experience by providing discounts to promote frequent purchasing, resulting in more purchases and increased sales across all channels. 

3. Tracking Incoming Traffic

Businesses frequently use numerous marketing channels to contact clients and lure them to their websites. You must be able to track where your users are coming from to determine what is working. This includes social networking networks, blogging platforms, marketing, and everything else you do to attract new clients. This is significant because you may spend a lot of money on Facebook advertisements, for example, only to discover that your clients are finding you via search engines. This will help you to optimize your expenditures and get more bang for your buck. 

4. Tracking Demographics

Another retail analytics example is examining the demographics of your customer base. Understanding the demographic mix of customers across a chain or at specific locations helps determine the efficacy of initiatives that engage target demographics. This implies that advertising messages displayed on digital signs, such as at POS or Point-of-Sale (time and place of the retail transaction completed), can be personalized to a specific target group, increasing engagement due to the message’s high relevance. It’s no secret that targeted advertisements outperform generic ones. Ad targeting is standard practice on the internet, but it has a long way to go in the physical world. The combination of audience measuring software with content management systems (CMS) used to plan and manage digital material allows for the delivery of dynamic advertising in physical retail. Digital displays are becoming increasingly popular in retail businesses, owing to their flexibility and ability to modify advertisements on the go. 

Synodus Retail Analytics Solutions

Synodus is a data analytics services provider with hands-on experience on retail analytics projects. We are well-prepared with state-of-the-art industry solutions and research, additionally, we are already in the top 1% of Gold Certified Partners in Data Analytics, the highest level of certification given to Microsoft Partners. 

synodus

Highlights of our Retail Analytics Solution: 

  • Provide business intelligence (BI) dashboard – an essential part of descriptive analytics.  

It is a data visualization and real-time analysis application that presents key performance indicators (KPIs) and other significant metrics for decision-making on a single screen. You can access reporting tools online and track your business performance anywhere. 

One of the proudest moments of Synodus team is when we assisted a notable Australian-based retailer successfully transformed their business with data integration, every source of data in their 10 years doing business is centralized in just one single database. More than that, we are setting up the foundation to help them with building Machine Learning models in years to come!

Synodus still have more clients’ stories to tell and we would love to let those stories speak for our business!
  • Provide an end-to-end analytical tool to every department, from HR, Sales, and Financial Analytics. 
  • Support asset and risk management.  

Conclusion

Businesses who want to accelerate exponentially should not make choices without retail analytics. Although setting up the correct processes and understanding how to gather intelligence needs effort, the insights you obtain will make it all worthy. Your retail chain, regardless of size, now has the potential to achieve extraordinary growth. You only need to use our world-class data analytics solutions. Having your data analyzed meaningfully is undoubtedly beneficial in the short and long term. 

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author avatar

Anna Nguyen

A tech-savvy with 2 years of experiences in creating content on Data Analytics and Digital Transformation. I wish to share daily news, tips and ways to make businesses elevate in the data-driven age.

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