What is Retail Data Analytics and How to Use It

As a 21st century retailer, you have access to enormous amounts of data - data which can be used to anticipate things happening within your business before they actually happen. How would you like to know what your customers want before they do? You can, and without the powers of the Oracle of Delphi. 

Unlock the potential of sales and customer information with retail data analytics, helping you to . make decisions and enact strategies based on hard data. Read on to find out what retail data analytics is and how to make it work for you.

What is retail data analytics?

The term retail data analytics describes the process of examining historic retail information. Through examination, you can gain those valuable insights to help improve your business. 

Data is gathered through a variety of means and there is software available to help you sort and organize this vital information. There are several retail data sets that are good candidates for analysis, with the list below outlining just a few possibilities.

• Sales data: total sales by volume and by value give you benchmarks to work off.
• Footfall numbers: tracking how many customers are coming through the doors lets you spot patterns early.
• Stock information: it’s vital to monitor your inventory so you can optimize your ordering process.
• Seasonal sales variations: understanding how sales vary across the year helps you plan your budget.
• Email analytics: metrics like email open rates indicate how effective your email marketing is.
• Online sales data: there’s often more granular data available for online rather than physical sales thanks to modern analytics tools.
• Customer engagement statistics: keeping an eye on engagement trends can help boost customer retention. 

Why is retail data analytics important?

Experience and instinct are valuable assets for retailers, and when combined with knowledge, form a recipe for business success. However, as we have improved the scale and detail of the data we can capture, there’s too much information for any individual or team to use effectively. 

Retail data analytics lays out a process for examining and using these levels of data. With the assistance of software, a business can quickly gather, process and interpret the information being collected and use it to predict changes in customer demand, taste, or needs. 

Competition is high in the retail space. Margins are thin. That means investing in the technology to allow the best understanding of your retail data, and therefore allowing your team the platform to be ever more insightful and effective, may just give you a crucial edge over competitors.

How does retail data analytics work?

You need the data to analyze to begin with. But how do you decide what data is worth gathering and analyzing? Well, retailers need to make sales and they need customers. So all sales and customer behavior data should be the focus of analysis. 

Some data you’ll gather just by doing business. This would include sales information like year-on-year growth. You’ll also have a good idea of how many customers you have through footfall numbers and sales volumes.

Other data collection will require some investment. For example, you should leverage your web presence to better understand your customers. This means developing a top notch website that attracts visitors. It doesn’t have to cost the world with many Pantheon competitors offering good value.

Assuming your website is well-optimized, the kinds of data analytics you should be targeting with it include: 

• Landing page conversion rates: how many users are making a purchase or signing up for your email list?
• Average session duration: how long do visitors stay on your site?
• Entrance and exit rates: which pages do visitors land on, and which do they leave your site from? 
• Acquisition channels: where is your traffic coming from?
• Device data: which devices are customers using to access your site? 

You can also drill down into some detail on your product pages. For instance, if some pages have an unusually high bounce rate (that is, the percentage of visitors landing on the page then clicking away from your site without moving on to another page first), then that could indicate a problem either with the page or the product.  

4 essential retail data analytics tools

The following are some of the best ways of gathering the necessary data.

• Customer relationship management software (CRM)
• Point-of-sale systems (POS)
• Inventory management software (IMS)
• KPI trackers 

Let’s explore them in a little detail.

CRMs

You can use this software to gather customer data. It’s a good way to monitor how customers interact with your business. You can use it to better focus marketing and sales efforts on the right customers.

POS systems

These systems gather transaction data. You’ll be able to track sales and revenue. Also, you’ll find out what customers are buying. When paired with a CRM, you’ll get even better insights about what certain demographics are buying.

IMS

Inventory data is vital in ensuring you can meet customer demand. Inventory management systems tell you how much stock you have on hand. You can have visibility across the business; warehouses, distribution centers, stores. Even if it’s not on the shelf, an employee can easily find out whether or not they can order it for a customer.

KPI Trackers

Depending on your priorities as a business, you can set out a number of key performance indicators. KPI tracking software can be plugged into your other systems so you can see how well you’re doing. 

For example, you may be trying to keep customer service call times to a minimum. You can integrate KPI software with a cloud calling platform to monitor call times. You’ll be better able to take actions to ensure your customer service reps are as efficient as possible.

4 types of retail data analytics

Armed with this data, you can now begin your analysis. But hold on a moment. What kind of analysis should you conduct? Let’s look at four different kinds of retail data analytics.

Descriptive retail data analytics

Descriptive analytics seeks to look back and describe some event or time period. For example, the launch of a new product range will generate lots of data. You can examine sales and customer sentiment and try to see what worked and what didn't.  This may mean you will adjust your approach the next time you have a big launch.

Diagnostic retail data analytics

Diagnostic analytics takes a deeper look at past events. Let’s stick with the product launch example for now. Using this kind of analytics will help you understand why things happened the way they did. Perhaps analysis showed that marketing didn’t quite hit the right demographic. This analysis better identifies opportunities to improve on past performance.

Predictive retail data analytics

This method of analysis is about looking to the future. You use it to make an offer to a customer who you predict may be interested in another product. For instance, you know if a customer buys a camping stove, they’ll also need gas cartridges. You can make sure customers see these are available online or are told about it in store.

Prescriptive retail data analytics

Prescriptive retail data analytics again looks forward in time. You use it to try to predict what customers want and when they want it. For example, you may expect a spike in web traffic around the holidays. You can then decide to boost remote support staff numbers to meet customer demand. 

8 ways to use retail data analytics to boost your business

1.   Better understand your customers

The value of knowing your customers cannot be overstated. It should drive your decisions around promotions, product range, and marketing. By analyzing sales data and customer satisfaction metrics, you’ll be able to form a better picture of who buys from you. This will also help you identify untapped groups of customers.

2.   Create targeted promotions

Promotions are a good way to boost sales and attract new customers. Create promotions that appeal to your target customers. You can do so thanks to a better understanding of their behavior. Also, knowing what customers have already bought from you is an opportunity. You can promote similar products or those that go well with previous purchases.

​​​​​​​3.   Make personalized customer experiences

Through retail data analytics, you have the ability to personalize the customer experience. You can show online shoppers more relevant recommendations based on previous purchases. This may mean making changes to your website. You can make such changes easily with Github Actions environment variables.

In store, you can offer carefully selected promotions via a loyalty programme. This not only provides the customer with extra value but gives you a new source of data.

4.   Better customer segmentation

The way to achieve better personalisation and promo targeting is through segmentation. This is where you split customers into different groups. This can be done by age, gender, profession, or whatever makes the most sense for your business. Retail data analytics provides you with all the information you need to effectively segment your customers.

5.   Increase your attach rate

The attach rate represents a measure of how many secondary products are sold as a direct result of selling a given primary one. One common example would be customers buying insurance alongside an expensive electrical product for peace of mind. Retail data analytics can help you pinpoint opportunities for cross-selling and upselling. 

6.   Improve revenue forecasts

Forward planning is the best way to grow your business. You need to know how much money is coming in to know how much you can invest. You can review previous years’ sales data and compare it with current sales. This should give you a good picture of what you can expect for the coming period. 

7.   React to emerging trends

Retail data analytics will help you spot trends and patterns. The earlier you can do so, the quicker you can react. A fashion retailer, for example, can expand a certain range if it’s a hot item. It’s important that you’re quick because it gives you a leg up on the competition. 

8.   Improve inventory management

Demand forecasting is a crucial aspect of retail data analytics. This allows you to order enough products to supply all your customers, but without running the risk of over-ordering. There’s nothing worse than a warehouse full of stock that you can’t shift.

Use retail data analytics to grow your business

Gone are the days of gut instinct and guess work in retail. In the information age, you have to embrace data to compete. Through retail data analytics you can better understand your customers’ needs. You can see trends emerging and act on them before your competitors. You can even see into the future, giving you valuable time to prepare.

Collect data. Analyze it. Gain valuable insights. Grow your business in a sustainable way.