Enhancing Retail Business Operations and Performance Through Data Analytics
There are few industries as well-positioned as retail to use data-driven systems to improve their bottom lines. Retail analysis is flush with sources of data, including information that can be derived from sales, inventory, traffic and marketing. Turning all this information into something useful, however, requires an understanding of where retail data systems fit into the bigger picture.
How is it Affecting the Retail Industry?
As of 2019, omnichannel marketing and sales have become key features of how many retailers and customers interact. Even the simplest forms of this approach have changed what items are put into inventory, which customers are met with what appeals, how prices are chosen, and how stores themselves are designed.
For example, let’s look at loss prevention systems that are used by many brick-and-mortar retailers. Using retail analysis methods, we can quickly spot which departments suffer the greatest losses. Items that are commonly stolen can be moved to spots where sales associates can see them. Patterns that might not be obvious to the average person can be discovered by comparing data across multiple stores.
Why Should Retailers Invest in Data Analysis?
Retail data systems can dig deep into information gleaned from social media to empower merchandise buyers on the other side of the planet to make decisions about items to put in stores and on websites. The timing of trend data pulled from customer analysis will increase the chances that a trending product will arrive in stores right before it’s ready to take off with the general public.
Personalization also offers many opportunities. Insights can be derived from mobile apps, website purchases, in-store sensors and point-of-sale units. Marketing appeals can then be tailored to the specific tastes and desires of the customer.
All of this is data intensive. Customer analytics calls for a back end of systems that can store data securely and make it readily available to decision makers in a timely manner.
Analyzing Customer Behavior
Good data scientists approach customer analysis with a highly experimental attitude. Let’s say you want to determine the optimal layout for your company's website. A/B testing methods can be utilized to discover how to maximize return on investment. You simply serve multiple versions of the website, and then you can use data analytics software to compare which versions kept folks on the site longest, drove sales and encouraged return engagement.
Using Predictive Analytics
Figuring out where to put money before the next sales season hits will be one of the biggest goals of many retail analysis efforts this holiday season and into 2020. In-store Wi-Fi offered for free can include opt-ins that allow data gathering and mining to be performed. Metadata can even be employed to establish what the relationships are among different customers, allowing you to see how friend circles and families influence shoppers.
In addition to getting ahead of trends, decisions can be made about how many items to put on shelves, what times of day customer support is most needed, and where to place sales associates in-store.
Assortment analytics can be used to make recommendations regarding products that are frequently purchased together. Website and app versions of stores can point customers toward product recommendations they’ll actually want.
Deriving these sorts of insights isn't a light undertaking. Data needs to be accumulated in sufficient quantities to ensure that predictions actually track closely with results. A data-driven attitude has to be fostered throughout a business, and an eye always has to be kept on quality control. In time, a company can form a robust base to work from and to deliver value to both customers and internal stakeholders.
Nicole Horn is digital marketing manager at Inzata Analytics, the first full-service data analytics platform powered by artificial intelligence for revolutionary integrating, exploring, and analysis of any kind of data from any source, on a massive scale.
Nicole Horn is Digital Marketing Manager at Inzata Analytics, the first full-service data analytics platform powered by artificial intelligence for revolutionary integrating, exploring, and analysis of any kind of data from any source, on a massive scale.
Nicole graduated from the University of South Florida in 2016 with a bachelors in business management. Her experience in the marketing field includes multiple years of social media management, paid search and promotion strategy, process improvement, website design, and lead generation for a wide variety of companies and industries. In May of 2018, Nicole joined Inzata as the team’s Digital Marketing Manager. In this role, Nicole oversees and guides all digital marketing operations, specifically with lead generation, company branding, and expanding the initial interest of potential customers.