Ultimate Guide to Retail Analytics (Definition, Types, Examples, Tips)

8 minute read

Ultimate Guide to Retail Analytics (Definition, Types, Examples, Tips)

Why should retailers care about data analytics?

It’s really quite simple. Today’s retailers are facing a bevy of new challenges, including declining sales, fierce competition from online-only stores, and changing consumer preferences.

In the digital age, there are a million things retailers have to stay on top of, and simply not enough time in the day. Yet, despite these challenges, some traditional retailers are managing to grow year-over-year, shredding previous sales records time and time again.

The winners are doing something differently — something that not only helps them survive, but also thrive in this quickly unfolding retail apocalypse.

According to McKinsey & Company, the reason some retailers are winning (while others struggle) is advanced analytics. New research says that retailers using advanced analytics outperform the competition by 68% in earnings — and the disparity is growing exponentially.

But what exactly is “advanced analytics,” and how does it differ from regular old Excel analysis?

To explain what makes advanced analytics so special, we need to start at the beginning. 

What is Retail Data Analytics?

Retail analytics on a chalkboard

Retail data analytics is the process of collecting and studying retail data (sales, inventory, pricing, etc.) to discover trends, predict outcomes, and make better 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.

To understand this difference, you first need to understand the 4 different types of retail data analytics.

Types of Retail Data Analytics

1. Descriptive Analytics

The most common type of data analytics, descriptive analytics helps retailers organize their data to tell a story.

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.

2. Diagnostic Analytics

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. 

But in today’s data-heavy world, this is nearly impossible to do for any human to do. With billions of data points and increasing complexity, larger retailers can’t effectively use diagnostic analytics without machine learning and AI.

As you’ll find below, there are virtually no standalone “diagnostics” solutions for retailers. This is because the fundamentals of diagnostic analytics (discovering hidden relationships between variables in your business) are much better used to predict the future and automate complex analysis. 

3. Predictive Analytics

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.” 

This is the second most advanced type of analytics.

Effective predictive analytics uses findings from both, descriptive and diagnostic analytics to forecast the future. This is because, in order 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 with other types of analytics, many retailers attempt to do this work manually — 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 behavior, 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, in order 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.

4. Prescriptive analytics

Prescriptive analytics is the final frontier of analytics, and also the most advanced type. 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 it also needs to know what actions will lead to the best possible future outcome. This is a difficult proposition, because there are a near infinite number of actions a business can take to generate some change in the numbers. 

There are multiple approaches:

  • 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
  • Using algorithmic AI, purpose-built for retail to make recommendations that lead to the best possible mathematical outcome (profit, GMROI, etc.)
  • Teaching a machine learning program to identify patterns and clusters of actions that lead to the best outcomes

Of course, the specific way different analytics companies achieve this is a closely guarded secret. But fundamentally, the process needs to generate recommendations that retailers can confidently follow 99% of the time. 

Examples of Retail Data Analytics Applications

Man reviewing retail data analytics report

One of the biggest reasons to use data analytics to guide decision making is to ensure your decisions are based on actual truth (cold, hard numbers), not just someone’s perception of reality. Analytics can also help you understand what’s going on with your business in much greater detail than you could otherwise.

Practically speaking, a retailer can use data analytics in order to:

  • Understand the value and number of products sold in an average order
  • Identify which products sell the most, the least, and everything in-between
  • Identify your most valuable customers
  • Identify what your true demand was as well as past lost sales
  • Determine optimal suggested order quantities and recommend purchase quantities and allocations
  • Determine the optimal price point for a specific product at any specific location

These (and other) insights can equip you to better understand the metrics of your business and implement strategies that help you get to where you want to go. As you grow, analyzing data needs to become a core part of your business in order to improve decision-making and come up with effective retailing strategies.

It’s no surprise then, that there exists a massive, thriving industry for retail analytics solutions. Below, we’ll discuss some of these applications, how they work, and what benefits you could see from using them.

1. Business Intelligence

To effectively manage and organize their data, many businesses turn to Business Intelligence tools. Because BI tools help you structure and visualize your data, they are an example of descriptive analytics.

Many retailers conduct basic BI using native features in their ERP (Enterprise Resource Planning) system, or by importing data directly into Microsoft Excel.

Slightly more sophisticated retailers will use dedicated BI software like:

  • Power BI
  • Tableau
  • SAP
  • QlikView
  • Apache Spark

These applications support multiple data sources, appealing visualizations, and some degree of data manipulation. 

The most sophisticated BI usually involves data scientists that use programming languages (like Python) that give them a greater degree of flexibility for data manipulation, data visualization, and data modeling.

While valuable, all of the examples above require a lot of human input, and are quite time consuming to manage. This is especially true for medium to large retailers running hundreds or thousands of stores (and tens or hundreds of thousands of products). This is why many retailers have dedicated teams of analysts in most departments to generate reports.

By virtue of their sophistication, advanced analytics solutions like Retalon can often automate most of the manual, repetitive tasks associated with traditional BI practices.

2. Sales Forecasting Software

Another common application of data analytics in retail is forecasting sales.

Simply put, sales forecasting is the process of looking at historical sales data, finding trends, and projecting them into the future to predict sales. This helps retailers with everything from purchasing inventory and managing their OTB budgets to setting high-level financial targets for the company.

As the name suggests, sales forecasting is predictive in nature — and it is the most rudimentary type of predictive analytics used by retailers. 

Because businesses have been attempting to forecast sales for centuries, there are many different approaches to doing so:

  • Using last year’s numbers to estimate sales for this year
  • Market research (surveys, observation, etc.)
  • Pundit estimates
  • Statistical models in Excel
  • Dedicated software

Many retailers have their own homegrown solution to predicting future sales, usually combining dozens (if not hundreds) of Excel sheets, ERP features, dedicated software, and teams of analysts.

While sales forecasting is the backbone of many retail planning processes — this is perhaps the biggest area of data analytics in need of an overhaul. This is because sales forecasting is quite often inaccurate, and fails to account for the complexity of retail business.

For example, if retailers sold out of a product last year, most sales forecasting methods would lead them to make the same mistake — even if they could potentially sell substantially more. 

For this reason, most sales forecasting has fallen out of vogue, replaced by a more sophisticated predictive analytics.

3. Demand Forecasting Software

As mentioned above, demand forecasting is a much more sophisticated type of predictive analytics in use by retailers. Rather than attempting to predict sales using merely historical sales data, demand forecasting uses a much broader range of data to calculate the demand of each product, at each store, at specific time intervals. 

This makes demand forecasting much more accurate than traditional sales forecasting. Read more about sales forecasting vs. demand forecasting here. In short, the main benefits of this type of retail analytics are:

  • More accurate prediction of the future state of the business
  • The creation of simulations or “what-if” scenarios
  • Ability to adjust on the fly as things change as on the ground
  •  Unification of key retail functions (eg. Promotions and inventory management)

As always, there are multiple ways to forecast demand. In increasing order of sophistication, retailers can use:

While the two former options can be enough for smaller retailers — they become cumbersome (if not impossible) to use with very large data sets (like those found in medium to large retailers). This is because demand forecasting doesn’t just look at sales data. A good demand forecast will also make use of data from:

  • Historical pricing
  • Historical inventory
  • Assortment breadth and depth
  • Product clusters and families
  • Seasonality
  • Supply chain variability
  • Competitor activity
  • Consumer trends
  • Etc.

You can imagine how difficult it would be to manually compile, analyze, and model all of this data for billions of unique Store / SKU combinations. 

The best way retailers have to make use of demand forecasting is to find a retail predictive analytics software vendor with a proven track record of working with retailers in your vertical.

Using a specialized software like this gives retailers a slew of benefits.

For example, you can test changing individual variables such as product price, new store openings, new product launches (and others) to see the impact this might have on your bottom line metrics — and adjust your inventory, prices or marketing strategy accordingly.

4. Unified Advanced Retail Analytics

This is the most powerful form of analytics that can produce the highest ROI if applied correctly. 

Falling under the final type of analytics (prescriptive analytics), unified advanced analytics aims to combine the benefits of business intelligence, powerful diagnostics, and accurate demand forecasting with intelligent automation that recommends the most profitable actions across the businnes. You can expect a good unified analytics software to:

  • Automate reporting and data visualization
  • Accurately forecast demand for every product at every store at specific time frames
  • Allow for flexible simulations and “what-if” scenarios for new product launches, store openings, etc.
  • Automatically recommend thousands (if not millions) of micro-optimizations across assortment, allocation, pricing, etc.
  • Reconcile all changes and updates across all departments and data sources

By virtue of its complexity and specialization, this type of analytics can only be provided by software vendors that specialize in advanced retail analytics.

With it, you can not only automate hundreds of repetitive tasks (compiling reports, consolidating data between departments, analyzing etc.) but also optimize at a granularity that human analysts are simply not capable of.

There are various solutions that provide this level of advanced data analytics such as Retalon’s analytics platform, which leverages highly accurate demand forecast and advanced AI to generate hundreds, thousands, or even millions of granular optimizations that improve the bottom line. Furthermore, this type of software is fully customizable and can be configured to auto-accept certain suggestions or require human approval for others for more control.

When Should You Upgrade Your Retail Analytics?

Retail analysts discussing data analytics at a meeting

Any medium to large retail business needs to use some level of data analytics if they’d like long-term success. That’s because you need to get accurate insights, proactively, in order to bring the right product, to the right location, at the right time, and in the right quantity.

Even if you’re using some form of analytics, you will likely want to upgrade sooner or later to stay ahead of the competition.

Usually, as your business starts expanding, so will the amount of data and complexity of the decisions involved.

 But what do you do when you have way too much data, and no idea what to do with it? 

To figure out if it’s time for an upgrade to the data analytics tools you’re using, you can start by asking the following questions:

  • How deep do I need to dive into the data? Are the answers to my problems obvious?
  • Am I constantly running into exceptions and needing to manually adjust my forecasts?
  • Are my analytics tools in various retail functions accounting for each other?
  • Am I repeating the same mistakes year over year?
  • Do I still run into inventory distortion issues such as lost sales, overstock, and out-of-stocks?
  • Do I end up with too many markdowns at the end of the season?
  • Do I have an effective way to deal with new products that do not have any sales history?

The answers to these questions will help you know if you should upgrade your approach to analytics.

But don’t fall into the all-too-common analysis paralysis trap. 

Retailers investing in advanced analytics are taking market share from those that are still on the fence. As we enter the digital age of retailing advanced data analytics and retail AI is no longer a “want” but it is an imperative “need.”

Start with a pilot on a small segment of your business or run a proof of concept on your own data. Get in touch with our team and we can show you how transitioning to a more intelligent approach to retailing is easier and more rewarding that you can imagine.