The term “Retail Analytics” might make you want to run for the hills. Imbued with connotations of complex mathematics, retail analytics is a complex-sounding term that is actually relatively easy to understand,
The simple definition of retail analytics is “providing insights related to sales, inventory, customers, and other important aspects crucial for merchants’ decision-making process.”
The concept of retail analytics encompasses many small-scale elements to culminate into a broad, overarching snapshot of a business’s vitality, sales trends, and overall success, in addition to highlighting problematic areas requiring improvement.
Simple Translation: Retail Analytics = all the little parts of a business that come together to form the big picture showing how the business is currently doing.
Retail analytics are important to businesses of all sizes and types, and the data gleaned from the analytics helps them make the best choices possible to streamline their business practices, run their operations more efficiently, and improve customer service experiences, as well as identifying areas in need of strengthening.
Simple Translation: The importance of Retail Analytics = gaining important data to help make better business choices and run the business more efficiently, while addressing problem areas
Businesses also use retail analytics to create a more concise picture of their target audience so that they can harness the power of their data collection to precisely identify who their ideal customer is, by identifying categories such as age, buying patterns, the frequency of visits, preferences, and far more.
Simple Translation: Additional uses off Retail Analytics = Using gained data to go about producing a crystal-clear picture of a business's target demographic and identify who and what their ideal customer is regarding different concepts like age, habits, and preferences.
As a discipline, retail analytics tends not to rely much on cursory, frivolous, one-dimensional types of data; rather it employs techniques such as data discovery and data mining to produce an assemblage of data that is free of inaccurate or irrelevant data and is instead replete with high-calibre data of value that can be used to produce actionable business insights with the potential to be used in the short and long-term.
Examples of the insights gained from retail analytics include customer behaviour insights. Having an in-depth knowledge of customer behaviour is critical to any type of marketing or campaign strategy a company embarks upon.
Accurate data on the natural behaviours of customers can guide businesses to create or modify solutions to transform the customer experience.
Additionally, it can bring about valuable insight into other areas of interest, such as a store’s design, a website's layout, and the ease of navigation for customers as well as how effective the communication and ongoing dialogue is fostered between the customer and the business.
Simple Translation: The various abilities of Retail Analytics = Are achieved by ignoring and completely bypassing useless information of no relevance in improving a business’s bottom line. Instead, businesses rely on retail analytics use of data discovery and data mining to produce high-quality data that can be used to inform decisions and business practices.
Retail Analytics can be used for an immense number of elements within businesses.
Due to the use of intuitive predictive tools, businesses can utilise their own “historical data,” in addition to the performing analyses of business trends to gain a clear understanding of exact quantities to order, thus reducing waste, and more importantly, reducing costs.
Additionally, businesses can improve the management of their inventory to focus on products proven to sell well with customers on a regular basis to result in the reduction of unnecessarily needed space for inventory and any associated overhead.
Simple Translation: Data can be used to optimise inventory management and make the ordering of products far more efficient by focusing on products that tend to sell well, buying appropriate quantities of other products, and ultimately saving substantially from wasted products resulting from over ordering.
Retail analytics are being used by companies all over the world in 2018. Providing several benefits, retail analytics have become an integral part of business operations and is heavily relied upon to make the most prudent and well-informed decisions for a business, its employees, and its financial bottom line.
At-a-glance Overview Retail Analytics Benefits
• Provides customer behaviour insights
• Improves marketing return on investment
• Assists in optimising in-store experiences, operations, and transactions
• Gives valuable information to inform decision-making on inventory and stock, tracking, and ordering
• Facilitates and enhances customer loyalty and patronage
• Improves marketing campaigns via an enhanced understanding of customer preferences
• Helps to create individual-specific specialised strategies taking into account customer shopping habits, purchase history, the frequency of visits, and preferences
• Traffic data gleaned from retail analytics can allow retail establishments to become more efficient by optimising employee scheduling to cut down on operational costs significantly, as there will not be an excess of employees present in slower times of business
• Gives businesses a distinct edge over their competitors through their vast assemblage of valuable consumer information that can be used to guide business practices, decisions, and marketing schemes
According to IBM’s limited-edition E-book “Business Analytics In Retail For Dummies,” the businesses who take the time to analyse how consumers browse company websites, question staff members and purchase items, are put into a superior market position where business practices can be geared and stylized through intimate understandings of their customer base.
While retail analytics is not particularly difficult to understand, it does, on the other hand, require a degree of difficulty in giving it a basic definition that encompasses all of its virtues.
After all, retail analytics is a multi-faceted business tool that contends with innumerable aspects in any enterprise. These aspects include the management of scheduling, inventory, and operational processes, and modifying business practices based on data showing clearly indisputable patterns.
Ultimately helping to foster ongoing relationships between a business and its many customers via enhanced, specialised experiences, businesses can take advantage of retail analytics for sustained profits and continued success for years to come.