Retail analytics: data-driven commerce
May 12, 2021 ● 11 min read
For any retailer who wants to stay and remain competitive in the harsh world of business, data analytics might just be ‘the order of things.’ The figures in your retail business should always be on the radar, from foot traffic and sales to inventory levels and expenditures, so that you can gauge your retail business' success and progress.
Table of Contents
- What is retail analytics?
- Types of Retail Analytics
- Why is retail analytics important?
- An Overview of Big Data in Retail Analytics
- How Big Data Is Transforming the Retail Industry
- How Do Data Analytics Help Manage Multiple Locations of a Retail supply chain?
- Retail analytics will give you a competitive advantage
However, statistics alone do not tell the whole narrative in retail analytics. To derive useful self-service information like shopper data, data analytics must be delivered in a user-friendly manner, and in certain situations, various data points and measurements must be connected to obtain action steps that can help you succeed. And how in the world would that become possible, if not except through retail analytics.
What is retail analytics?
Business methods and approaches used to identify, capture, understand customer needs, and interpret retail data are referred to as retail analytics.
Retail analytics are important in almost every retail industry aspect, from sales, customer service, marketing to inventory management and operations. Simply put, retail analytics allows you to harness and leverage information.
Types of Retail Analytics
Descriptive analytics is the most popular form of data analytics. It helps entrepreneurs organize their information to create a holistic story. It generates useful insights into past and current results by pulling in raw data from various sources (POS system, inventory networks, OMS, ERPs, e.t.c) to help improve customer experience.
Analysts used to do this manually in Excel, extracting data from various channels, editing it, charting it, and so on. A large portion of this information collection and reporting work can now be streamlined using retail analytics tools like BI software, and integration products.
Data is used in descriptive analytics to explain "what" is happening in your business. However, it does not have satisfactory answers to the question "why?"
Diagnostic analytics, the most basic type of “advanced” analytics in the retail analytics industries. It assists entrepreneurs in using data to address the “why” answers to specific business issues. Using the same raw data as descriptive analytics drills deeper into the data to discover connections between data points using retail analytics tools, statistical process, algorithms, and, in some cases, AI.
Diagnostic analytics can also be used to detect site performance and alert people to possible problems as they occur. Historically, the more experienced analysts did it manually. They'd sift through data, apply statistical models, search for insights, and look for historical data.
If descriptive analytics reveals the "what" of what's going on in the business and diagnostic analytics reveals the "why," predictive analytics reveals the "what's next" in retail analytics. This is the second most sophisticated form of analytics retail.
Efficient predictive analytics forecasts the future by combining results from descriptive and diagnostic analytics.
Predictive analytics finds clusters of exceptions automatically and predicts potential patterns using way more sophisticated algorithms and analytical insights.
Prescriptive analytics is the most sophisticated form of retail analytics and the final step. The previous forms of analytics will tell the business owners "what" is going on, "why" it is happening, and "what will occur next." Prescriptive analytics will advise entrepreneurs on "what to do next" to achieve the best market share.
To make great suggestions, prescriptive analytics tools must understand what is expected to happen in the future and which operations will yield the best potential future outcome. This is a complicated proposition since a business will require an almost infinite number of actions to produce a change in the numbers.
Why is retail analytics important?
You can achieve a more healthy and competitive business in various fields when you understand how to leverage data insights and retail analytics properly. It would be best if you considered the following:
Opportunity to launch an efficient marketing and sales strategy
Retail analytics help you learn more about a business its people, performance, market share, and clients, leading to new ideas about how to promote and sell your services. For example, once you know that a single in-store presentation effectively convinces customers to purchase a specific product, you can find opportunities to mimic that supply chain movement and increase revenue even more.
Alternatively, once you understand what makes your customers tick (i.e., relying on behavioral data), you can determine what kinds of messaging to use in your promotions and advertising campaigns.
You can improve customer satisfaction
Retail analytics can reveal useful data insights, such as the reasons that keep customers coming back for more and those that drive them away. Knowing these aspects would help you design a great customer experience (whether in-store or online) to ensure that customers are satisfied and happy.
You can optimize your operations
Retail analytics can help you improve your operations, conversion rate, and processes in some instances. Foot traffic monitoring solutions, which can shed light on a store's busy hours and traffic data patterns, are a prime example of this. The business owner will then use the data to refine their staff schedules to ensure that they have accurate staff rosters available on the sales floor.
You will stop wasting resources
Using retail analytics in your business will help in better business investment and accurate allocation of resources. You'll be able to commit more time, attention, and resources to strategies, products, and projects that drive optimum performance rather than those that waste time and resources. Tracking your metrics regularly often allows you to identify problems. so you can make decisions that will play a part in improving business operations.
An Overview of Big Data in Retail Analytics
To remain competitive, entrepreneurs must be better at decision making and giving appropriate discounts, persuade consumers to embrace emerging trends, and remember their customers' name and birthdays—all while keeping the business operations smoothly behind the scenes. How can they manage to keep up? Big data in retail is critical for attracting and retaining customers, streamlining processes, optimizing supply chains, improving business decisions, and, finally, saving money.
Before the advent of cloud apps, businesses could only track what people buy and where they purchased it. Companies can collect a wealth of data about their shoppers using increasingly advanced tech apps, such as their age, geographical area, gender, favorite restaurants, other stores they shop at, what books or news they read—the list goes on and on. To make better decisions and maintain the data, retailers have switched to cloud-based big data products.
How Big Data Is Transforming the Retail Industry
How do retailers get their data?
Loyalty plans are one of the most popular ways that big data is obtained in the retail industry. It is now also obtained via credit card transactions, IP addresses, POS system, user log-ins, and other means. When more data is gathered, retail businesses can use industry analytics to measure the ebb and flow of consumer shopping and sales in the past to forecast future expenditure and make personalized recommendations.
Based on your previous searches and orders, Amazon uses customer data to recommend products for you. Their recommendations engine, which analyzes over 150 million accounts, delivered 29 percent of their revenue. This has resulted in significant gains for the eCommerce powerhouse.
Customer Experience Personalization
Big data can provide opportunities for business owners to provide a better customer experience. Costco collects transaction data to keep its clients safe. When a California fruit packing firm warned Costco of the risk of listeria exposure of fruits such as peaches and plums, the retailer could email individual customers who had ordered the contaminated products rather than a blanket warning to their entire list.
Forecasting Demand in Retail
In addition to big data, several algorithms examine social media and web surfing patterns to forecast the next big thing in retail. The weather is one of the most interesting data points for predicting demand. Walgreens and Pantene collaborated with the Weather Channel to customize product reviews to customers based on weather conditions.
Walgreens and Pantene expected increased humidity—a time when people would be looking for anti-frizz products—and used commercials and in-store deals to boost sales. Within two months, Walgreens saw a 10% rise in Pantene product purchases, as well as a 4% increase in revenue across the hair care category. Economic forecasting and retail predictions are used to allocate resources as efficiently as possible during the year.
Customer Journey Analytics
The customer journey is not a solitary one. From research to purchasing, it's a zig-zag around channels. Big data is the only way to understand the consumer journey and develop a smoother customer experience. Retail analytics tools can assist business owners in making decisions on: Where are consumers looking for product information? Where are they going? What are the most effective methods for reaching them and persuading them to buy?
How Do Data Analytics Help Manage Multiple Locations of a Retail supply chain?
Retail Analytics can help retail chains and businesses identify the variations in demand for their products across different geographic locations. Retailers can use market buying insights to help improve customer service in particular areas and stock products more efficiently.
Thriving in the Digital Age as a Brick and Mortar Retailer
The continued growth and success of online shopping are posing new obstacles for traditional retailers. However, when using the proper analytical instruments, there is no need for these stores to feel intimidated. Physical shops remain an important outlet for business owners because they have something online vendors don't have: a personal touch.
Even Amazon, the world's largest online e-commerce company, recognizes the importance of traditional storefronts. With a retail analytics solution, online and traditional stores can work together to increase sales and engage consumers in innovative and exciting ways. As physical stores face intensified competition from online sales, consider the following three retail analytics solution for bridging the physical-digital divide:
Leverage on your high-value clients
Customers who shop both online and in-store are the most faithful and important to a company. These customers appreciate the ability to sample items in-store and the convenience of purchasing them from the luxury of their own home.
These same shoppers are even more likely to visit special occasions at physical store owners and hear about new products or services offering and provides excellent ways to meet customer needs and gather more shopper data.
Retail businesses must capitalize on this high-value consumer segment, to do so, they need more details than what can be gleaned from their consumers' purchase histories. Combining data from online and in-store consumers is a positive start, but it also has gaps. Adopting retail analytics platform services is one of the most reliable and powerful ways to eliminate these blind spots and boost sales.
You would learn more about customer needs, location, behaviors, preferences, attitudes, and values, as well as where they live, by connecting your consumer data with a validated segmentation system. This will not only help you increase sales, but it would also help you improve your company customer experience and identify opportunity openings for new marketing campaigns. This data analytics will help your company improve the marketing process and gather more sales data by using retail analytics platform services.
Leverage on the BOPIS Trend
Shoppers that buy online and pick up in-store (BOPIS) are another significant category that physical stores cannot afford to overlook. These customers not only read product reviews and compare prices, but they also have a strong attachment to the stores from which they buy.
They choose to immediately pick up their orders in a local shop rather than wait for the products to arrive by mail and cope with distribution problems. Nothing, however, irritates shoppers more than finding that the commodity they want is out of stock at their favorite retailer or that they may have to wait in long lines to pick up their online order.
A reputable third-party segmentation system may assist in identifying these consumers and ensuring that stores are well equipped and staffed. Store owners could study what consumers in their trade areas want to purchase based on their lifestyles, shopping patterns, and attitudes by segmenting their customers using "geodemographic" data. This method assists you in identifying where BOPIS clients want to congregate, allowing you to illustrate physical stores (and any possible store layout changes) that better match this local customer base. This knowledge allows business owners to adapt their product range and staffing levels to this consumer segment.
Analyze Shopping Patterns
To optimize the marketing and distribution process of a new store location, a retailer must consider the preferences and buying habits of a shopper.
Previously, this degree of research may have been prohibitively expensive, but modern mobility analytics platforms provide a cost-effective way to discover the travel trends of your store's and your competitors' customers over the span of a day, week, or year.
Mobile analytics enable you to examine the size and shape of your trade area, particularly in comparison to other stores or competitors.
As an added perk, retailers can use mobile analytics to determine where a visitor was before and after visiting your store, enriching your strategic understanding of the shopping experience and creating new opportunities for engagement.
By combining these observations with geodemographic statistics, retailers may classify consumer groups that are likely to defect to competitors. These will kick-start a plan to keep them with things they appreciate.
The advent of online shopping still wouldn't mean the end of brick-and-mortar stores, but it will necessitate a shift in mindset among brick-and-mortar retailers. Physical retailers will still prosper with the right combination of details, insights, techniques, and behavior.
Customers that are highly interested in brick-and-mortar shops are among the most valuable. It is the responsibility of those retailers to respond by identifying and engaging them with effective marketing and product collection.
Retail analytics will give you a competitive advantage
There is no doubt that data mining is extremely useful in this day and age for better decision-making. It's no surprise that the phrase "data is the new currency" has been circulating for a few years now. However, it is important to keep in mind that retail data by itself does not have much value.
To derive value from retail data, you must have the right analytics tools and understand how to use them. Hopefully, this guide has successfully provided you with the information you need to use analytics in your retail business.