Retail analytics is a process that helps to provide crucial data for businesses with regards to inventory, sales, supply chain activities, consumer demand, and more. By implementing an analytics-driven retail environment, organizations can make better decisions for procurement, marketing, merchandising, and other operational considerations. This, in turn, enables retailers to create a better buying experience and for identifying opportunities for organizational improvement. 

Some questions that we will answer in this blog include:

What are the benefits and importance of retail analytics?

What are some current trends in retail analytics?

How can AI impact retail analytics?

What are some challenges to effective retail analytics?

What are some common tools associated with retail analytics?

Benefits and Importance of Retail Analytics

Everyday businesses strive to become more data-driven entities. And retail is no exception; especially given the growing complexity of the industry. This in addition to the amount of information available about any given product or company, plus almost limitless alternatives for consumers, means that the need for analytics has become even more apparent.

Retail analytics benefit all major functions in retail organizations:

Procurement: Understanding trends is key for retailers. Retailers will tell you that much of their buying patterns and forecasts arise from market data. To be successful with trend analytics, a good analytics system and healthy data are key.

Customer experience: Retail is about making sure that the right products are available to the right people, at the right time, in their preferred shopping environment. The more data that retailers have about their customers, products, and similar, the better the odds that the customer will have their ideal experience.

Operations: Every organization is searching for new and better ways to save some money. With better data, comes better understanding, and more chances to save. 

Product development: By understanding why certain products sell best, it is easier to drill down into what about them is selling best and if that trend will continue enough into the future that it is worth developing or integrating new products.

On top of improvements within functions, retail analytics capabilities enable improved collaboration and performance management:

Organizational performance is key: Any effective retail data endeavor will allow an organization to monitor in real-time, which can be integral in ensuring that the right products are in stock. Analytics can also be helpful in identifying both inefficiencies and opportunities with and related to an organization.

The need to remove silos: Because retail covers so many disciplines within any single organization, it is easy for essential data to get siloed in one department when it may very well impact a large number of factors within an organization.

These together can help to create a retail organization that is able to remain competitive – despite the rapid proliferation of brands, products, and regulations to keep it all together.

Building an Omnichannel Experience

Image source: Sheehy

An omnichannel buying experience  is one that can be carried out seamlessly across both digital and physical locations; without ‘losing’ progress between these locations. What this means practically is that customers now expect to be able to use different channels and devices throughout their buyer’s journey, from initial prospecting to after-sale support.

Retail analytics is helpful in this effort because it provides the key data necessary for building and carrying out an omnichannel environment. The key reasons why include:

  • Focused and amplified marketing: Retail analytics help us to understand what makes each customer unique, how their different channels can impact each other, and how these characteristics can be used to create better, personalized promotions and campaigns.
  • A more effective supply chain: Having access to data in real-time to see if promotions are effective and help retailers avoid problems related to stock; in-store and in ecommerce.
  • Optimal in-store operations: Understanding the ‘why’ behind a customer’s journey within a store can help to determine the right strategy for staffing, product placement, and similar.
  • Staying secure: Cybersecurity is at the forefront of any omnichannel retailer’s mind and retail analytics can help to ensure all networks stay secure and to catch suspicious activities before they become problematic.

Trends in Retail Analytics

There are a number of trends that are currently prominent in retail analytics. Some of the most popular include:

Dynamic pricing: When it comes to retail sales, adjusting the price based upon a perceived customer’s ability to pay or other factors is not a new practice. However, with the amount of internal and external data available today, determining the perfect price is becoming less of a challenge.

Dynamic pricing can also be crucial in retail industries where competition is particularly steep, such as electronics. This is because it is becoming increasingly common for buyers to search for the best price of an item online and being just a bit more expensive can quickly remove a retailer from the list.

One common example of dynamic pricing that we see in our daily lives is with Uber and the integration of ‘surge’ pricing in order to try and ensure the highest profits possible during peak times. Though not necessarily a retail brand per se, with the advent of smarter technologies, this example may become more and more commonplace in a wider range of industries. 

IoT-powered advanced analytics: A growing number of stores are making use of RFID tags (radio-frequency identification) and beacons over a wireless network to manage a range of tasks. Some function further out in the supply chain by tracking items as they are produced and delivered. Others help with in-store functionalities based upon customer behavior, as is the case with RFID tags on items to determine their popularity; and to aid in theft prevention.

Additionally, when customers agree to use the wifi provided in-store, they provide a huge amount of data, such as:

  • Pathways throughout the store
  • Popular parts of the store
  • How many times they return
  • The amount of time spent in-store

Image source: TorQEYE Analytics

This information can have and far reaching impacts throughout an organization – particularly with regards to merchandising and marketing considerations. This can ultimately lead to an improved customer buying experience and a more profitable in-store operation

In addition to the utility that IoT sensors have for understanding product and human behavior, they can also help to lower overhead and utility costs. They do so by optimizing the usage of light and power, both when there are customers in-store and when the store remains empty.

Building a better inventory: Analytics ensure that customers in a certain location have the right products for their specific wants and needs. By better using analytics to choose products and deciding their location within a store from these analytics, retailers find that customers have an improved experience and are more likely to return.

There are a number of factors that are taken into account when making such decisions:

  • Historical sales transactions
  • Local events and holidays
  • Local weather
  • Demographics

By closely watching these and other trends, retailers greatly improve the odds of having the right items in stock at the right times. 

An increase in microsegmentation: Now more than ever organizations have the capacity to group people together in order to put the right products in front of them. Segmentation can impact a wide range of functions, such as promotions, pricing, inventory, and assortment.

Some criteria that are often used in retail segmentation include:

  • Product performance
  • Store performance
  • Store attributes
  • Product attributes
  • Ad hoc customer segments
  • Demographics

Advanced tools: The days of patchwork software solutions are becoming increasingly numbered as the utility and range of retail analytics software grows. Today, platforms come with advanced capabilities and increasingly flexible, scalable, and usable.

Future of Retail : AI

Image source: Total Retail

A recent Gartner study expressed that AI solutions in retail companies could autonomously manage up to 85% of customer interactions [1]. This number demonstrates how integrated AI applications and machine learning are becoming. Other places where AI stands to become a bigger part of retail include:

  • Staff optimization
  • Supply chain management
  • Product assortment
  • HR & hiring

Presently, a large part of AI integration is felt in the retail analytics side of operations, rather than in physical automation. Some places where we can expect to see an increase in AI in the future include:

Stock and inventory: Retailers may be able to use historical purchase data to predict inventory needs in real-time. This could lead to a dashboard or an AI system ordering more of an item before it has the chance to run out. It could also prove helpful in finding the causes of anomalies in sales and product volumes to help determine better ways to manage them.

Theft prevention: When there are beacons on items, their detection becomes easier. However, with machine learning, this can extend to products that are concealed – without IoT. It can also be helpful in catching odd behavioral patterns that may suggest the likelihood of a theft to take place.

Marketing and merchandising: As an increasing number of IoT sensors and beacons become part of the retail experience, more and more data will become available for key decisions. With this data, a growing number of tasks can then become automated, ensuring that each customer always receives the right promotional efforts and that their related products don’t go out of stock. 

Overall operational efficiency: The more you know about what’s important, the less time you spend on the things that don’t qualify. For retailers, this is particularly true. As more and better information becomes available through AI about customers and products, more time can be spent connecting the two.

Personalization: It’s easy to sell a product, but what keeps that buyer coming back is the experience. Through AI, bespoke communications and product suggestions can be made without a moment of human interaction.

Challenges to Retail Analytics

Though the benefits of retail analytics are felt throughout an organization, it is not without its own challenges. Some of the most common challenges include:

Data security: When a growing volume of customer data (in-store and online) is stored and analyzed, the need for security only stands to grow. And as this data becomes more personalized and sensitive, the stakes only become higher if something goes wrong. 

Data governance: The methodology for collecting and using your data must be immaculate, not only for compliance reasons, but also for keeping the trust of your users. There also need to be defined limitations about how much data is used and why it is used.

Data utilization: Collecting mountains of data is useless if it’s not the right data or isn’t used properly. This means not only being able to understand the data that is collected today, but also how it relates to the data collected previously – and what this may mean for the future.

Retail Analytics Tools

Every day the number of retail analytics tools available on the market grows. The following are a number of features that any potential tool should include:

  • Trend prediction: Being able to identify trends in order to build a better product catalog is both essential and difficult. With the right solution, reports can be built to determine which items are selling the most quickly so the right steps can be taken to adjust in terms of supply chain and merchandising considerations. Over time, as more historical data is collected, this becomes easier to achieve.
  • Real-time benchmarking with competitors: In a world where price can make or break a buying decision, staying priced competitively is key. Any tool should be able to automatically keep track of what’s happening with your competitor’s pricing so that you can be sure to stay close.
  • Getting into detail: Having a larger overall view of an organization is key, but being able to dig deeper into the details is just as important. This means having the capacity to track data from all stages; including supply chain, product assortment, retail pricing, merchandising, and more. Knowing the details about these different factors can be the key to having the right products at the right time and price.
  • Recommendations and cross-selling: When your customers are online, a recommendation engine should be making suggestions based on the items and pages they view. This can make a huge difference in up-selling to online buyers.
  • Automated triggers and alerts: When certain ‘events’ pop up, the right responses in terms of pricing and similar should be immediately set into action. This is because the modern retail environment is simply too complicated for any single group of employees within an organization to oversee.

Businesses could choose to use these functionality packaged together in an intuitive and easy to use tool or they could choose to work with multiple vendors.

Some common tools on the market today include:

NameFoundedStatusNumber of Employees
1010 Data2000Private201-500
Bloomreach 2009Private201-500
Blue Yonder (JDA)1985Private1,001-5,000
Information Builders1975Private1,001-5,000
Lightspeed Retail POS2005Private501-1,000
Microsoft Dynamics RMS1975Public10,001+
Retail Information Systems1995Private11-50
Revel Systems2010Private501-1,000
Vend POS2010Private201-500

Retail analytics is just another example of how the technology we take for granted is changing the way even the simplest of tasks are carried out. If you’re interested in learning more about AI, data, and where technology may go in the future, be sure to keep an eye on our blog. Already need an AI solution for your retail brand? Be sure to check out our library of over 3,000 vendors and use cases.

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