Retail analytics is the process of providing analytical data on inventory levels, supply chain movement, consumer demand, sales, etc. that are crucial for making marketing, and procurement decisions. The analytics on demand and supply data can be used for maintaining procurement level and also for taking marketing decisions. Retail analytics gives us detailed customer insights along with insights into the business and processes of the organisation with scope and need for improvement.
Retail data analytics is the process of collecting and studying retail data (like sales, inventory, pricing, etc.) to discover trends, predict outcomes, and make better business decisions. Done well, data analytics allows retailers to get more insight into the performance of their stores, products, customers, and vendors and use that insight to grow profits.
Virtually all retailers are doing some form of data analytics even if they’re only reviewing sales numbers on Excel. But there is a very big difference between an analyst firing up Excel to sift through spreadsheets and using purpose-built AI to analyze billions of data points at once.
There are four types of retail data analytics that each play an important role in providing today’s retailers with key insights into their business operations. The four different types are;
It works by bringing in raw data from multiple sources (POS terminals, inventory systems, OMS, ERPs, etc.) to generate valuable insights into past and present performance.
Traditionally, analysts did this manually in Excel; gathering data from different sources, formatting it, charting it, etc. Today, a lot of this data gathering and reporting work can be automated with BI tools and integrations.
Simply put, descriptive analytics uses data to describe “what” is happening in your business. But it doesn’t do much to answer the “why” unless combined with other types of data analytics that can show patterns and correlations.
The simplest form of “advanced” analytics diagnostic analytics helps retailers use data to answer the “why” of specific business problems.
Taking the same raw data used in descriptive analytics, diagnostic analytics uses statistical analysis, algorithms, and sometimes, machine learning, to drill deeper into the data and find correlations between data points.
Diagnostic analytics can also be used to find anomalies and flag potential problems as they happen (if results do not match pre-programmed benchmarks and business rules).
Historically, the most accomplished analysts did all of this manually. They would sift through data, apply statistical models, look for patterns, and find correlations.
If descriptive analytics shows you the “what” of what’s happening in your business, and diagnostic analytics tells you the “why” predictive analytics tells you “what’s next.”
Effective predictive analytics uses findings from both descriptive and diagnostic analytics to forecast the future. This is because to accurately predict what happens next, you must first understand what’s already happened and what caused it.
Predictive analytics automatically detects clusters and exceptions and uses complex algorithms and statistical methods to predict future trends.
Like other types of analytics, many retailers attempt to manually do this work, with analysts compiling data in Excel and applying generic statistical models to project trends into the future.
Unfortunately, retail businesses are very complex, and there are too many correlations between factors (demand, price, inventory, product assortment, competitors, consumer behaviour, etc.) for any human to account for all of them manually. That’s why simple sales forecasts are much less accurate than demand forecasts.
Thus, to accurately forecast the future and account for the most important correlations, retail predictive analytics must use a combination of AI, advanced mathematics, and intelligent automation.
The previous types of analytics can tell retailers “What” is happening, “why” it happened, and “what will happen next.” Prescriptive analytics can tell retailers “What you should do next” to get the best results.
To make good recommendations, a prescriptive analytics system needs to not only know what is likely to happen in the future but also needs to know what actions will lead to the best possible future outcome.
This is a difficult proposition because there are a nearly infinite number of actions a business can take to generate some change in the numbers.
There are multiple approaches:
- Using algorithmic AI, purpose-built for retail to make recommendations that lead to the best possible mathematical outcome (profit, GMROI, etc.).
- Running simulations on a finite number of different initial conditions (different assortment, allocation, pricing, etc.) and choosing the conditions that lead to the highest profit.
- Teaching a machine learning program to identify patterns and clusters of actions that lead to the best outcomes.
Data analytics is being used to disrupt retail today.
In-Store Experience Tracking
For omni-channel and brick and mortar retailers, improving the in-store experience is critical today. One way data analytics can improve merchandising is through tracking. To do so, sensors, beacons, and mobile applications are used to track customer movements throughout the store. Once this data is collected it’s then analyzed for insights around how to deliver excellent customer experiences, where to focus attention and what leads to consumers coming back to shop again in the future.
Understanding Customer Behaviors
Data analytics that tracks customer behaviors help retailers to understand how their customers are shopping, what they like and where they prefer to receive promotions. Using this data they can optimize marketing efforts and create offers best suited for customers as well as uncover tactics for retention and personalization that improve sales and lower costs.
Improving conversion rates is essential in retail as online real estate for goods continues to be flooded. With predictive analytics and targeted promotions, it’s possible for retailers to uncover what offers and products are most popular, what’s being signed up for, and more. With this data, they can continue to test and analyze new ways to capture customer interest.
Operational and Supply Chain Improvements
Using analytics in the supply chain is one of the more recent ways retailers are upping their game. The data can be used for everything from product tracking to improved quality, real-time inventory management, and better forecasting. It’s also speeding up delivery of goods something consumers have come to expect. Making better decisions at this level of the operations not only cuts down on costs it also has the ability to impact the bottom line.
In the end, retailers who leverage the power of data analytics will continue to win more customers. A deeper connection to their desires and needs and stronger bonds formed using actionable insights from data are all key to increasing sales in the competitive marketplace.
Tracking the Customer Journey
Perhaps one of the most critical facts about customers is their journey an overall look at their entire experience with the brand. From communications to online and in-store experience, purchases and their interactions with ads. Getting a full view of this helps reduce cart abandonment, increases the likelihood of closing a sale and helps retailers understand how to best guide customers to complete the buying process.