In an age where technology is evolving more rapidly than ever, we are seeing the retail industry adopt new forms of personalization and segmentation to optimize their businesses. Retailers can use analytics and big data to successfully segment both their customers, and their competitors’ customers, to better market and service them. Retailers have a better understanding of who their customers are, what types of customers are best for business and what types of customers are leaving them for their competitors. These changes don’t just apply to online shopping experiences. In 2017, the National Retail Federation (NRF) reported that for each company closing a store, 2.7 companies were opening stores. This places increased importance on in-store analytics. Heading into 2019, companies will need to dive into these retail trends to stand out from the competition over the coming years.
The Role of Big Data in Retail
Before we can dive into retail trends, we need to establish the role of big data in the retail industry. Big data consists of large, complex data sets often defined by the three Vs: volume, velocity, and variety. The use of big data in shopping is certainly nothing new. Businesses have always attempted to paint a picture of what makes customers purchase. As technology advances, big data has grown, well, even bigger—allowing brands to track a more complete buyer’s journey, understand brand sentiment, and optimize the company’s effort to decrease the cost per conversion. The ability to draw insights from big data will be at the heart of retail industry trends for the coming year.
Trend #1: Retail Predictive Analytics
The Internet of Things allows nearly every technological device to be connected and share information. This means that retailers have never had more data to help them target customers and deliver hyper-personalized shopping experiences. Retailers can track past purchases, competitor shopping patterns, most visited stores and a variety of other data points to predict when a shopper needs more shampoo or a new toothbrush. The company can then deliver targeting marketing efforts and shopping experiences to create efficiencies not only for the shopper, but for the company. Walmart uses predictive analytics in collaboration with Weather Co. to create hyper-local experiences by leveraging weather forecasts and store sales on a zip code level. This collaboration manifests itself in a number of ways. When it’s warm with high winds and no rain, people are more likely to eat steak. When temperatures top 80 and wind is low, Walmart sells more salads. Walmart can create displays and deliver ads specific to these products when the weather hits to increase sales.
Trend #2: Dynamic Pricing
Dynamic pricing is one of the most cutting-edge uses of machine learning and artificial intelligence in the retail industry. The best price for a product fluctuates based on seasonality, supply, demand, and competitor prices. Machine learning can enable a company to account for these factors and generate the right price and the right time while still allowing retailers to stay on track for sales goals. Dynamic pricing is not a quarterly or even monthly practice. Instead, prices can change throughout a single day based on retail trends. Dynamic pricing has always been in practice on some level, but AI and machine learning has made the processes more automated and efficient thanks to the abundance of data.
Trend #3: Omnichannel Retail
Google defines omnichannel retail as “ensuring [retailer] marketing strategies are geared toward enabling customers to convert on any channel.” This new approach considers the modern shopping journey which has a multitude of touch points from traditional brick-and-mortar stores to Facebook Ads. People do not shop exclusively on one platform. Because of this, companies need to adopt a retail analytics strategy that can track success across platforms and throughout the customer journey. Using omnichannel analytics, retailers can adjust the supply chain based on trends to ensure they have enough stock and optimal staffing to account for demand. Retailers can also increase profitability by optimizing merchandizing based on learnings across channels.
Trend #4: Retail Store Analytics
In-store analytics provide insights into consumer behavior, utilizing everything from carts with location beacons and in-store Wi-Fi networks to video cameras. Cutting-edge brands can track when they entered and left the store, how they moved around inside and key areas they visited. Using these retail store analytics along with basic demographic data, stores can begin to optimize their in-store experience to drive business, adjusting how employees interact with guests to developing product displays.
Envestnet | Yodlee and Retail Analytics
In the race for competitive advantage in the retail industry, the need for robust, high-quality data sets is quickly growing in importance. The retailers that rely on data with inadequate details, lack of geo-location or time delays will lose to brands that leverage more expansive retail shopping information. With the Envestnet | Yodlee Retail Analytics for Market Research, your business can access easy-to-use dashboards that display customer affinity profiling, share of wallet metrics and market shares. It is a web-based consumer spending analytics tool using billions of de-identified transactions to answer competitive analysis questions for retailers. It is easy to use, does not require technical knowledge and helps find meaningful insights in this large dataset. It gives you access to near real-time shopping measurements to determine the impact of advertising campaigns and allows companies to increase and maintain market share in key regions by discovering geographical areas with high or low market share. Unlike survey and traditional data sets, the Envestnet | Yodlee Retail Analytics for Market Research solution is powered by a de-identified and dynamic data panel that can be segmented in countless ways to reveal consumer spending data patterns for a variety of market categories and services. Drawing from 16 million de-identified active consumers, the panel is consistent with U.S. Census data in terms of geographical location and income distribution.