7 ways that BIG DATA & analytics can help retailers become more successful
Small businesses have the advantage of agility, making them perfectly suited to act on data-derived insights with speed and efficiency.
Bernard Marr, Data consultant
While they sound complicated, analytics are simply programs that comb through large amounts of data (often called “big data”) and search for patterns and trends. They provide insights you need to successfully compete with online retailers who have been using them for years to gain a competitive advantage.
1. Predict trends
Algorithms (a set of guidelines that describe how to perform a task) look for trends in data to predict future behaviour. For example, an algorithm can crawl social media posts and web browsing habits to predict top selling products in a category.
2. Forecast demand
Analytics look at demographic data and economic indicators to understand the spending habits of your customers. Those insights can help you recommend new products when a customer comes into your store.
In Russia, the demand for books increases as the weather gets colder. Ozon.ru increases the number of book recommendations in its customers’ social media feeds as the temperature falls.
3. Optimize pricing
In the past, most retailers would reduce prices at the end of a buying season when demand was low. Retailers using analytics discovered that a more gradual price reduction — from the moment demand started to fall — led to increased revenues.
U.S. retailer Stage discovered that dynamic pricing (changing prices in response to demand) increased revenue more than a traditional end-of-season sale approach 90% of the time.
4. Identify customer needs
It’s not easy to select products that customers want to purchase and their buying channels. Recommendations from predictive analytics and data collected through transactional records and loyalty programs can make those decisions much easier.
5. Attract customers
Analytics can help you gain a better understanding of your customers. You can use that information to develop targeted messaging that attracts customers to your store.
Google and Facebook’s targeted advertising platforms are examples of data-driven marketing that provides customized messages to specific target groups.
Social analytics are an example of predictive analytics — driven by big data — that analyze social media interactions and identify target groups that might be interested in your product or service.
6. Operate more efficiently
Comparative analytics help you develop benchmarks and compare productivity metrics against other retailers in your sector. That information can help you discover inefficiencies and operate your store more effectively.
7. Streamline your business operations
Business analytics — driven by big data — provide consolidated reporting and dashboards that help you make more informed business decisions using real-time data and plan for the future.
72% of small businesses invest in big data solutions to improve their business operations
Big data analytics — once reserved for large corporations — replace guesswork and intuition with data-derived insights that help you understand your customers, their preferences and buying journey. Those insights help you provide customized offers to your customers and operate your retail store more efficiently.
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