Big data analytics are becoming increasingly indispensable across most industries, including retail. When retailers clearly understand their industry by leveraging key information, they can improve their marketing efforts, demand forecasting, inventory planning, and much more. However, those looking to big data for improvements must overcome certain challenges and develop specific strategies before implementing big data analytics into their businesses. With a range of potential opportunities, what are some business applications for big data in the retail sector?
What are retailers doing?
Retailers can leverage big data in several ways to gain an edge. Here are five key areas worth mentioning:
- Personalization — Many retailer websites feature recommendation engines that use customer preferences to tailor selection results. However, it can be challenging for these engines to make recommendations effectively without being obtrusive. For instance, generating irrelevant offers can irritate potential customers. Through big data analysis and machine-learning capabilities, retailers can use customer information to train their engines. For example, they can improve relevance by implementing a control loop that compares generated recommendations to response rates. In other words, retailers are using big data to improve personalization with increasing accuracy.
- Pricing — Product prices can fluctuate throughout the year, especially during high-demand periods. How do retailers adapt? Through big data analytics, retailers can employ dynamic pricing, a pricing method that automatically adjusts their prices in response to their competitors’. Retailers can use analytics software that monitors prices and creates rules so the software adjusts prices accordingly. However, if retailers don’t check or limit their dynamic pricing controls, they can become problematic; if a competitor drastically discounts a product and the retailer hasn’t set restrictions on his or her dynamic pricing system, it can devastate margins. Therefore, big data has the capacity to make real-time price matching easy and effective — but only if retailers set clear boundaries.
- Inventory — It’s important for any retailer to have a clear view of his or her inventory, especially if he or she oversees more than one location or channel. In the past, industry insiders relied on physical observations and manual inventorying. Now, a variety of digital touch points makes inventory management simpler, even for those with multiple channels. Big data analytics can help retailers track their inventories and understand trends, which, in turn, can help them better — and more accurately — prepare for changes in demand.
- Competition — As with inventory, previous competitor assessment methods were relatively low-tech. Retailers can do more than set competitor pricing alternatives based on real-time data: They can also use competitors’ success to their own advantages by monitoring which competing deals are the most profitable and localizing prices based on what competitors’ customers are buying.
- Sensor analytics — Through increasing Internet of Things (IoT) connectivity, retailers have more opportunities than ever to connect with and attract customers. This year, more retail owners will likely “profit from an increase in sensors and data coming from various customer-owned devices.” This, in turn, is generating greater interest among global retailers hoping to take advantage as 70% are “ready to adopt the Internet of Things to improve customer experiences.” By leveraging customer information on everything from loyalty cards to social media platforms and store apps, retailers can use these countless data points to gain an edge.
It’s clear that, when it comes to harnessing big data, there’s plenty of room for retail success. To truly unlock big data’s potential, retailers will likely invest in more IoT, machine learning, and automation technology. However, careful strategic planning and preparation are essential for any business leader planning to tap into big data. Retail executives should understand what their goals are for leveraging big data, what information they will need, and how they will turn insights into actions moving forward. Without proper planning, it is easy to become overwhelmed — and more information can hinder rather than help business operations. Data has always driven decisions: Big data technology is exciting because it drives smarter, faster, more accurate decisions based on real-time information from many, many data points.