Retail Analytics: How to Reap the Benefits of Consumer Data

In 2015, Starbucks launched Mobile Order & Pay. Today, it accounts for nearly a quarter of their total U.S. retail orders. 

The program aimed to make the coffee buying experience easy, allowing customers to order ahead or pay in-store using their mobile devices. But the app provides more than just convenience for coffee lovers.

Mobile Order & Pay also acts as a data collection tool, giving Starbucks a real-time, detailed look into their customers’ behavior. The retail analytics Starbucks gathers from their Mobile Order & Pay program are invaluable to their overall success.

Simply put, retail analytics help businesses make smarter decisions. Brands utilize data gathered from retail analytics software to pin down their ideal customers based on factors like buying patterns, location, and personal preferences. 

With retail analytics, you can better understand shopping behaviors and how to use that information to create personalized experiences that attract new customers and cater to their needs. 

How analytics are used in retail

Retail analytics is the catchall term given to sets of insightful data that help retailers manage operations and make informed business decisions. Retailers use one or more software programs to obtain data on various business aspects, such as inventory levels and consumer demand.

Gathering analytics can help guide a number of important business decisions, from inventory levels to new store openings.

• Customer insights: Gain invaluable insights into your customers’ decision-making and shopping behavior. This data helps drive your product assortment, pricing, and multichannel retail marketing strategies. 
• Supply chain management: Use sales forecasting to help effectively manage supply chains and inventory levels. Understanding future consumer demands helps you plan for fulfillment and avoid out-of-stock products.
• Optimized business operation: In-store and online data can help you adapt to fit your customers’ needs. This data can also help ensure inventory is properly stocked and that your stores are properly staffed at peak times. 
• Create customer loyalty: Consumers want personalized shopping experiences that meet their needs. Retail analytics allow businesses to give consumers with suggestions based on their interests and past shopping habits. If customers are happy with their experience, they are likely to return.
• Forecast future trends: Companies that have real-time access to different business analytics can better predict future events and behaviors based on historical data. This can help guide decisions pertaining to product development, inventory, and logistics.

Starbucks uses retail analytics pertaining to location, demographics, behavior, and trends to predict the future performance of new store locations. This helps mitigate the risk of opening a new location in an unprofitable area.

Source: Starbucks app

Starbucks goes even deeper into its data to ensure they are constantly engaging with their customers. The brand utilizes advanced analytics to create personalized marketing campaigns and incentives within the mobile app to engage with customers outside of their brick-and-mortar stores. 

The app provides various incentives to encourage the user to continuously return to their physical store locations. Starbucks centers its campaigns around earning ‘stars’ so that users feel rewarded when they spend money at the coffeehouse. 

What does the Mobile Order & Pay analytics tell Starbucks about their customers?

• How they buy: These insights show if customers prefer to buy in-store, in the drive-thru, or by ordering ahead for pick-up. 
• What they buy: Purchase history can influence inventory management and new product releases with insights on popular drinks, food, and merchandise.
• Where they buy: Popular stores and busy regional areas often influence where Starbucks corporate decides to open new locations.
• When they buy: Store managers can use app data to uncover peak buying hours to staff locations and schedule employees accordingly.

And of course, the Starbucks mobile app boasts a user-friendly interface that makes ordering and paying for your favorite drinks easy. This strategy helps Starbucks score higher customer satisfaction levels and encourages brand loyalty.

Types of retail analytics

There are four types of retail analytics businesses use for decision-making. All of these data types work together to paint a larger picture of your business operations and customer journey behavior. 

Descriptive analytics

Descriptive analytics provides businesses with an overview of how various actions are performing. Purchase history, order fulfillment, inventory levels, campaign results, and other similar data points all contribute to descriptive analytics. 

Retailers use descriptive analytics to see how many users visit their website or store, how long they spend on the site, the pages they visit, and which actions led to a purchase.

Diagnostic analytics

Diagnostic analytics looks at the same historical data but works to provide context and uncover trends. Retailers use diagnostic analytics to identify relationships between these different variables. 

Most businesses experienced an influx of delivery and online purchases during the pandemic. Diagnostic retail analytics would help uncover the connection between the spike in contactless shopping and the nationwide stay-at-home orders.

Predictive analytics

Predictive analytics offers a glimpse into future trends based on the data uncovered by diagnostic analytics. These past consumer behaviors can help retailers anticipate future actions. 

If your store is usually understaffed on Saturdays and often runs out of a specific product, you would use predictive retail analytics to manage your inventory and ensure your store is properly staffed on the weekends.

Prescriptive analytics

Prescriptive analytics enables businesses to adjust strategies based on anticipated changes in consumer behavior, demand, and inventory. This data is constantly updated so retailers can make real-time changes to their strategy. 

Historically busy shopping days, such as Black Friday, often call for increased inventory numbers and changes in pricing. These decisions are all driven by prescriptive analytics.

The data science behind retail analytics

Acquiring deep insights into your customers and business is undoubtedly helpful. But where does the data come from in the first place?

Put simply, the data comes from you. 

Although big data is often associated with multi-billion dollar companies like Starbucks, it’s not out of reach for the average retailer. Many of the tools and resources your business already uses hold important data to analyze.

In-store retail analytics

Understanding how consumers act in-store can help guide decision-making at your brick-and-mortar locations. In-store retail analytics can influence everything from staffing to store layout. Fortunately, there are easy ways to track consumer behavior with equipment commonly found in retail stores. 

Security devices, such as cameras and tags, provide useful information about your customers. CCTV footage can show you which parts of your store have the most foot traffic and if the location is properly staffed during peak hours.  

Point-of-sale (POS) systems are the holy grail of retail analytics. Your POS system stores a lot of helpful information about your business, including inventory numbers and average sale amount.

Website retail analytics

You want to know what sections stand out to shoppers in-store, and the same applies to your brand’s website. 

Web analytics provide an in-depth look at how your customers behave online. You can uncover what customers added to their cart, the items they abandoned in their cart, and how often they return to your site.

Consumer behavior analytics

If you only utilize your in-store and online data, then you’ve just dipped your toes into the water of retail analytics. Data from third-party platforms, like online marketplaces or social media, can provide an even deeper look into your current and potential customers. 

Customer retail analytics help companies determine which platforms are the most popular among different segments. These analytics can also uncover consumer preferences to help guide product development. 

Starbucks can gather loads of data from their website, in-store POS systems, and mobile app. But the analytics they gather from third-party sources provide even more insight. For instance, the company may find that customers prefer to subscribe to regular shipments of Starbucks ground coffee on Amazon versus other e-commerce platforms.

Retail data analytics trends of today and tomorrow

Despite how informative retailers find their in-store and online data, we’ve only scratched the surface of retail analytics. Advances in technology and data sharing will continue to evolve how retailers interact with customers and run their businesses. 

Here’s what you can expect for the future of retail analytics:

• Studying foot traffic analytics to learn what products customers try on or test but ultimately abandon in-store.
• Using facial recognition and other artificial intelligence to better recognize repeat customers in-store and online. 
• Micro-targeting customers with marketing campaigns that cater to their specific needs and preferences. 
• Improving in-store and online experiences and building better customer relationships by sharing consumer data across sales channels.

Retail analytics is evolving – and it’s evolving faster than ever. In today’s world, neglecting your customer data can be detrimental to your retail business. 

When the first Starbucks mobile app was launched in 2009, it offered a store locator, nutritional information, and a drink builder tool. Today, the app allows users to order ahead, customize drinks, send gift cards, earn loyalty program rewards, and scan to pay for their purchases.

Starbucks CEO Kevin Johnson explains, "There are two transformative elements for modern-day retail. The first is you have to create a customer experience in your brick-and-mortar store to make it a destination." 

“You have to extend that experience to a digital customer relationship. If you fail to do both of those, you will struggle.”

Kevin Johnson
CEO of Starbucks

Retail analytics software

Retail analytics software gives you a comprehensive look into all aspects of your business. The insights gathered from software helps you analyze what’s working, what’s not, and what you should do to improve performance. 

Retailers can also integrate retail analytics software with other applications, such as POS software, retail management systems, and retail operations software, to access real-time data. 

To be included in the retail analytics software list, a product must: 

• Focus solely on the retail industry 
• Integrate with, or study data from, other retail software systems 
• Offer in-depth insights based on data 
• Produce various reports using data from numerous sources

*This list is based on G2 data and individual G2 Scores collected on May 18, 2021. Some reviews may have been edited for clarity.

1. SPS Commerce Analytics

SPS Commerce Analytics’ goal is to make retail analytics less complex. The software provides users with actionable insights by collecting and cleaning sales and inventory data across multiple retail customers. SPS Commerce Analytics transforms messy data into insights with tables, charts, graphs, and interactive visualizations to help you meet demand and boost profit.

What users like:

“Embrace the ability to have speedy, relevant data in order to make quick, insightful decisions to grow business. The dashboard insights provide tools to dig deep, deeper, and deepest into root cause and opportunities.” 

- SPS Commerce Analytics Review, Mark Z.

What users dislike:

“The timing of getting the reports is atrocious. As a company, we are required to have our data in by 11 AM on Monday mornings. Week after week the data is not uploaded into SPS Commerce Analytics until Wednesday. It is insanely delayed.”

- SPS Commerce Analytics Review, Elise W.

2. Numerator Insights

Numerator brings omnichannel marketing, merchandising, and sales data together. This software helps provide clarity behind what people buy and what influences their purchase. If you’re seeking a platform that can uncover real-time consumer behavior insights, look no further than Numerator. 

What users like:

“What I love about this product is that it allows you to see what a shopper is doing across all the different retail avenues. Knowing where they are digitally and where they are migrating to is the key to understanding leakage. So far IRI and Nielsen can’t give the CPG community the kind of view Numerator does.”

- Numerator Review, Steve Z. 

What users dislike:

“Their e-commerce and click & collect coverage is slim, but they are working to expand. I would like to see their panel size increase by another 25% to help increase coverage of smaller retailers and categories.”

- Numerator Review, Jason R.

3. Omnilytics 

Brands and retailers are able to make informed decisions with Omnilytics. This software helps retailers understand the market by pairing together consumer trends and e-commerce insights. Omnilytics enables retailers to transform into sustainable, demand-led brands. 

What users like:

“With Omnilytics, we can see all the detailed information of our competitors from pricing and assortment to top-selling materials and patterns. We can also see the seasonal trend of the specific categories and retailers that help us further understand business results and improve our decision-making as we learn with more information. What makes it even better is that this is all done in Omnilytics, which makes our work more efficient and productive.”

- Omnilytics Review, Kevin L. 

What users dislike:

“Omnilytics tends to focus on well-established global fashion brands but we would love to see more data from smaller local labels that are in our similar product category. It would also be great to see more in-depth info on local competitor's sustainability commitments and how this impacts their unique price points as an additional category of research.”

- Omnilytics Review, User in Apparel & Fashion

4. Dataweave

Dataweave is an AI-powered platform that helps e-commerce brands make smart, data-driven business decisions. The software aggregates and analyzes web analytics to provide relevant actionable data. Retailers use Dataweave to help drive profitable growth and enhance product discoverability.

What users like:

“Dataweave has been a valuable partner in helping us improve our competitive intelligence. The team is well-trained, flexible, very responsive, and quick to implement new requests. Great service overall!”

- Dataweave Review, Hendrik M.

What users dislike:

“We would have preferred data weave to help us with additional data quality checks from the start of the project. We reached a great level of quality in the end, however.”

- Dataweave Review, Louise B.

5. RQ

Acting as both a cloud-based POS and retail management system, RQ is a great solution for multi-location retailers. The software features mobile POS, inventory management, HR, and marketing, as well as comprehensive reporting and analytics. RQ also boasts numerous integrations to help streamline the core functions of your business. 

What users like:

“The widgets and full integration work really well. I have enjoyed using it because it allows me to migrate customer data from one system to another. I also enjoy the database of customer information.”

- RQ Review, Sean C. 

What users dislike:

“There needs to be a better training system so my representatives can better understand how to look for certain files. We also use a data system that I wish connected to RQ4.” 

- RQ Review, Michael B.

Cognizance, coffee, and consumer behavior

Starbucks can track your coffee order – and much, much more. 

But you don’t have to launch a full-scale mobile ordering program to gain valuable customer insights. In fact, much of the high-value retail analytics you need to drive business success is waiting at your fingertips. 

Once you gather your retail analytics, learn how to effectively leverage your customer marketing data for ongoing business success.